Repository: leandromoreira/digital_video_introduction
Branch: master
Commit: 4547487189a5
Files: 28
Total size: 3.2 MB
Directory structure:
gitextract__0xyor60/
├── .gitignore
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README-cn.md
├── README-es.md
├── README-it.md
├── README-ja.md
├── README-ko.md
├── README-pt.md
├── README-ru.md
├── README.md
├── clean_docker.sh
├── dct_better_explained.ipynb
├── dct_experiences.ipynb
├── encoding_pratical_examples.md
├── filters_are_easy.ipynb
├── frame_difference_vs_motion_estimation_plus_residual.ipynb
├── image_as_3d_array.ipynb
├── image_transform_frequency_domain.ipynb
├── s/
│ ├── cut_smaller_video.sh
│ ├── download_video.sh
│ ├── ffmpeg
│ ├── mediainfo
│ ├── start_jupyter.sh
│ └── vmaf
├── setup.sh
└── uniform_quantization_experience.ipynb
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
v/*
!v/small_bunny_1080p_60fps.mp4
!v/small_bunny_1080p_30fps.mp4
.ipynb_checkpoints/
.DS_Store
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at leandro.ribeiro.moreira@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
================================================
FILE: CONTRIBUTING.md
================================================
## Contributing
First off, thank you for considering contributing to this repo. It's people
like you that make this material such a great tutorial.
### 1. Found a bug, have a question or want to add a new feature
If you've noticed a bug, a feature or have a question go ahead and [open an issue](https://github.com/leandromoreira/digital_video_introduction/issues/new)!
### 2. Fork & create a branch
> * **Ensure the bug was not already reported** by [searching all issues](https://github.com/leandromoreira/digital_video_introduction/issues?q=).
If this is something you think you can fix, then
[fork digital_video_introduction](https://help.github.com/articles/fork-a-repo)
and create a branch with a descriptive name.
A good branch name would be (where issue #325 is the ticket you're working on):
```sh
git checkout -b 325-add-japanese-translations
```
### 3. Implement your fix or feature
At this point, you're ready to make your changes!
### 4. Make a Pull Request
At this point, you should switch back to your master branch and make sure it's
up to date with Active Admin's master branch:
```sh
git remote add upstream git@github.com:leandromoreira/digital_video_introduction.git
git checkout master
git pull upstream master
```
Then update your feature branch from your local copy of master, and push it!
```sh
git checkout 325-add-japanese-translations
git rebase master
git push --set-upstream origin 325-add-japanese-translations
```
Finally, go to GitHub and
[make a Pull Request](https://help.github.com/articles/creating-a-pull-request)
:D
### 5. Merging a PR (maintainers only)
A PR can only be merged into master by a maintainer if:
* It was reviewed.
* It is up to date with current master.
Any maintainer is allowed to merge a PR if all of these conditions are
met.
PS: this contributing text was inspired by active admin contributing.
================================================
FILE: LICENSE
================================================
BSD 3-Clause License
Copyright (c) 2017, Leandro Moreira
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: README-cn.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
[](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)
# 介绍
这是一份循序渐进的视频技术的介绍。尽管它面向的是软件开发人员/工程师，但我们希望**对任何人而言**，这份文档都能简单易学。这个点子产生于一个[视频技术新手小型研讨会](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p)期间。
本文档旨在尽可能使用**浅显的词语，丰富的图像和实际例子**介绍数字视频概念，使这些知识能适用于各种场合。你可以随时反馈意见或建议，以改进这篇文档。
“**自己动手**”需要安装 docker，并将这个 repo clone 到你的计算机。
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **注意**：当你看到 `./s/ffmpeg` 或 `./s/mediainfo` 命令时，说明我们运行的是 docker 容器中的版本，容器已经包含了程序所需的依赖。
所有的 **“自己动手”应从本 repo 的根目录运行**。**jupyter 的示例**应使用 `./s/start_jupyter.sh` 启动服务器，然后复制 URL 到你的浏览器中使用。
# 更新日志
* 增加 DRM 系统
* 发布版本 1.0.0
* 添加简体中文翻译
* 添加FFmpeg oscilloscope滤镜示例
# 目录
- [介绍](#介绍)
- [目录](#目录)
- [基本术语](#基本术语)
* [编码彩色图像的其它方法](#编码彩色图像的其它方法)
* [自己动手：玩转图像和颜色](#自己动手玩转图像和颜色)
* [DVD 的 DAR 是 4:3](#dvd-的-dar-是-43)
* [自己动手：检查视频属性](#自己动手检查视频属性)
- [消除冗余](#消除冗余)
* [颜色，亮度和我们的眼睛](#颜色亮度和我们的眼睛)
+ [颜色模型](#颜色模型)
+ [YCbCr 和 RGB 之间的转换](#ycbcr-和-rgb-之间的转换)
+ [色度子采样](#色度子采样)
+ [自己动手：检查 YCbCr 直方图](#自己动手检查-ycbcr-直方图)
* [帧类型](#帧类型)
+ [I 帧（帧内，关键帧）](#i-帧帧内关键帧)
+ [P 帧（预测）](#p-帧预测)
- [自己动手：具有单个 I 帧的视频](#自己动手具有单个-i-帧的视频)
+ [B 帧（双向预测）](#b-帧双向预测)
- [自己动手：使用 B 帧比较视频](#自己动手使用-b-帧比较视频)
+ [小结](#小结)
* [时间冗余（帧间预测）](#时间冗余帧间预测)
- [自己动手：查看运动向量](#自己动手查看运动向量)
* [空间冗余（帧内预测）](#空间冗余帧内预测)
- [自己动手：查看帧间预测](#自己动手查看帧间预测)
- [视频编解码器是如何工作的？](#视频编解码器是如何工作的)
* [是什么？为什么？怎么做？](#是什么为什么怎么做)
* [历史](#历史)
+ [AV1 的诞生](#av1-的诞生)
* [通用编解码器](#通用编解码器)
* [第一步 - 图片分区](#第一步---图片分区)
+ [自己动手：查看分区](#自己动手查看分区)
* [第二步 - 预测](#第二步---预测)
* [第三步 - 转换](#第三步---转换)
+ [自己动手：丢弃不同的系数](#自己动手丢弃不同的系数)
* [第四步 - 量化](#第四步---量化)
+ [自己动手：量化](#自己动手量化)
* [第五步 - 熵编码](#第五步---熵编码)
+ [VLC 编码](#vlc-编码)
+ [算术编码](#算术编码)
+ [自己动手：CABAC vs CAVLC](#自己动手cabac-vs-cavlc)
* [第六步 - 比特流格式](#第六步---比特流格式)
+ [H.264 比特流](#h264-比特流)
+ [自己动手：检查 H.264 比特流](#自己动手检查-h264-比特流)
* [回顾](#回顾)
* [H.265 如何实现比 H.264 更好的压缩率?](#h265-如何实现比-h264-更好的压缩率)
- [在线流媒体](#在线流媒体)
* [通用架构](#通用架构)
* [逐行下载和自适应流](#逐行下载和自适应流)
* [内容保护](#内容保护)
- [如何使用 jupyter](#如何使用-jupyter)
- [会议](#会议)
- [参考](#参考)
# 基本术语
一个**图像**可以视作一个**二维矩阵**。如果将**色彩**考虑进来，我们可以做出推广：将这个图像视作一个**三维矩阵**——多出来的维度用于储存色彩信息。
如果我们选择三原色（红、绿、蓝）代表这些色彩，这就定义了三个平面：第一个是红色平面，第二个是绿色平面，最后一个是蓝色平面。
我们把这个矩阵里的每一个点称为**像素**（图像元素）。像素的色彩由三原色的**强度**（通常用数值表示）表示。例如，一个**红色像素**是指强度为 0 的绿色，强度为 0 的蓝色和强度最大的红色。**粉色像素**可以通过三种颜色的组合表示。如果规定强度的取值范围是 0 到 255，**红色 255、绿色 192、蓝色 203** 则表示粉色。
> ### 编码彩色图像的其它方法
>
> 还有许多其它模型也可以用来表示色彩，进而组成图像。例如，给每种颜色都标上序号（如下图），这样每个像素仅需一个字节就可以表示出来，而不是 RGB 模型通常所需的 3 个。在这样一个模型里我们可以用一个二维矩阵来代替三维矩阵去表示我们的色彩，这将节省存储空间，但色彩的数量将会受限。
>
> 
例如以下几张图片。第一张包含所有颜色平面。剩下的分别是红、绿、蓝色平面（显示为灰调）（译注：颜色强度高的地方显示为亮色，强度低为暗色）。
我们可以看到，对于最终的成像，红色平面对强度的贡献更多（三个平面最亮的是红色平面），蓝色平面（最后一张图片）的贡献大多只在马里奥的眼睛和他衣服的一部分。所有颜色平面对马里奥的胡子（最暗的部分）均贡献较少。
存储颜色的强度，需要占用一定大小的数据空间，这个大小被称为颜色深度。假如每个颜色（平面）的强度占用 8 bit（取值范围为 0 到 255），那么颜色深度就是 24（8*3）bit，我们还可以推导出我们可以使用 2 的 24 次方种不同的颜色。
> 很棒的学习材料：[现实世界的照片是如何拍摄成 0 和 1 的](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm)。
图片的另一个属性是**分辨率**，即一个平面内像素的数量。通常表示成宽*高，例如下面这张 **4x4** 的图片。
> ### 自己动手：玩转图像和颜色
>
> 你可以使用 [jupyter](#如何使用-jupyter)（python, numpy, matplotlib 等等）[玩转图像](/image_as_3d_array.ipynb)。
>
> 你也可以学习[图像滤镜（边缘检测，锐化，模糊。。。）的原理](/filters_are_easy.ipynb)。
图像或视频还有一个属性是宽高比，它简单地描述了图像或像素的宽度和高度之间的比例关系。
当人们说这个电影或照片是 16:9 时，通常是指显示宽高比（DAR），然而我们也可以有不同形状的单个像素，我们称为像素宽高比（PAR）。
> ## DVD 的 DAR 是 4:3
>
> 虽然 DVD 的实际分辨率是 704x480，但它依然保持 4:3 的宽高比，因为它有一个 10:11（704x10／480x11）的 PAR。
现在我们可以将**视频**定义为在**单位时间**内**连续的 n 帧**，这可以视作一个新的维度，n 即为帧率，若单位时间为秒，则等同于 FPS (每秒帧数 Frames Per Second)。
播放一段视频每秒所需的数据量就是它的**比特率**（即常说的码率）。
> 比特率 = 宽 * 高 * 颜色深度 * 帧每秒
例如，一段每秒 30 帧，每像素 24 bits，分辨率是 480x240 的视频，如果我们不做任何压缩，它将需要 **82,944,000 比特每秒**或 82.944 Mbps (30x480x240x24)。
当**比特率**几乎恒定时称为恒定比特率（**CBR**）；但它也可以变化，称为可变比特率（**VBR**）。
> 这个图形显示了一个受限的 VBR，当帧为黑色时不会花费太多的数据量。
>
> 
在早期，工程师们想出了一项技术能将视频的感官帧率加倍而**没有消耗额外带宽**。这项技术被称为**隔行扫描**；总的来说，它在一个时间点发送一个画面——画面用于填充屏幕的一半，而下一个时间点发送的画面用于填充屏幕的另一半。
如今的屏幕渲染大多使用**逐行扫描技术**。这是一种显示、存储、传输运动图像的方法，每帧中的所有行都会被依次绘制。
现在我们知道了数字化**图像**的原理；它的**颜色**的编排方式；给定**帧率**和**分辨率**时，展示一个视频需要花费多少**比特率**；它是恒定的（CBR）还是可变的（VBR）；还有很多其它内容，如隔行扫描和 PAR。
> ## 自己动手：检查视频属性
> 你可以[使用 ffmpeg 或 mediainfo 检查大多数属性的解释](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)。
# 消除冗余
我们认识到，不对视频进行压缩是不行的；**一个单独的一小时长的视频**，分辨率为 720p 和 30fps 时将**需要 278GB\***。仅仅使用无损数据压缩算法——如 DEFLATE（被PKZIP, Gzip, 和 PNG 使用）——也无法充分减少视频所需的带宽，我们需要找到其它压缩视频的方法。
> *我们使用乘积得出这个数字 1280 x 720 x 24 x 30 x 3600 （宽，高，每像素比特数，fps 和秒数）
为此，我们可以**利用视觉特性**：和区分颜色相比，我们区分亮度要更加敏锐。**时间上的重复**：一段视频包含很多只有一点小小改变的图像。**图像内的重复**：每一帧也包含很多颜色相同或相似的区域。
## 颜色，亮度和我们的眼睛
我们的眼睛[对亮度比对颜色更敏感](http://vanseodesign.com/web-design/color-luminance/)，你可以看看下面的图片自己测试。
如果你看不出左图的**方块 A 和方块 B** 的颜色是**相同的**，那么好，是我们的大脑玩了一个小把戏，这让我们更多的去注意光与暗，而不是颜色。右边这里有一个使用同样颜色的连接器，那么我们（的大脑）就能轻易分辨出事实，它们是同样的颜色。
> **简单解释我们的眼睛工作的原理**
>
> [眼睛是一个复杂的器官](http://www.biologymad.com/nervoussystem/eyenotes.htm)，有许多部分组成，但我们最感兴趣的是视锥细胞和视杆细胞。眼睛有[大约1.2亿个视杆细胞和6百万个视锥细胞](https://en.wikipedia.org/wiki/Photoreceptor_cell)。
>
> **简单来说**，让我们把颜色和亮度放在眼睛的功能部位上。[视杆细胞](https://en.wikipedia.org/wiki/Rod_cell)**主要负责亮度**，而[视锥细胞](https://en.wikipedia.org/wiki/Cone_cell)**负责颜色**，有三种类型的视锥，每个都有不同的颜料，叫做：[S-视锥（蓝色），M-视锥（绿色）和L-视锥（红色）](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg)。
>
> 既然我们的视杆细胞（亮度）比视锥细胞多很多，一个合理的推断是相比颜色，我们有更好的能力去区分黑暗和光亮。
>
> 
一旦我们知道我们对**亮度**（图像中的亮度）更敏感，我们就可以利用它。
### 颜色模型
我们最开始学习的[彩色图像的原理](#基本术语)使用的是 **RGB 模型**，但也有其他模型。有一种模型将亮度（光亮）和色度（颜色）分离开，它被称为 **YCbCr***。
> * 有很多种模型做同样的分离。
这个颜色模型使用 **Y** 来表示亮度，还有两种颜色通道：Cb（蓝色色度） 和 Cr（红色色度）。YCbCr 可以由 RGB 转换得来，也可以转换回 RGB。使用这个模型我们可以创建拥有完整色彩的图像，如下图。
### YCbCr 和 RGB 之间的转换
有人可能会问，在 **不使用绿色(色度)** 的情况下，我们如何表现出所有的色彩？
为了回答这个问题，我们将介绍从 RGB 到 YCbCr 的转换。我们将使用 [ITU-R 小组](https://en.wikipedia.org/wiki/ITU-R)*建议的[标准 BT.601](https://en.wikipedia.org/wiki/Rec._601) 中的系数。
第一步是计算亮度，我们将使用 ITU 建议的常量，并替换 RGB 值。
```
Y = 0.299R + 0.587G + 0.114B
```
一旦我们有了亮度后，我们就可以拆分颜色（蓝色色度和红色色度）：
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
并且我们也可以使用 YCbCr 转换回来，甚至得到绿色。
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> *组织和标准在数字视频领域中很常见，它们通常定义什么是标准，例如，[什么是 4K？我们应该使用什么帧率？分辨率？颜色模型？](https://en.wikipedia.org/wiki/Rec._2020)
通常，**显示屏**（监视器，电视机，屏幕等等）**仅使用 RGB 模型**，并以不同的方式来组织，看看下面这些放大效果：
### 色度子采样
一旦我们能从图像中分离出亮度和色度，我们就可以利用人类视觉系统对亮度比色度更敏感的特点，选择性地剔除信息。**色度子采样**是一种编码图像时，使**色度分辨率低于亮度**的技术。
我们应该减少多少色度分辨率呢？已经有一些模式定义了如何处理分辨率和合并（`最终的颜色 = Y + Cb + Cr`）。
这些模式称为子采样系统，并被表示为 3 部分的比率 - `a:x:y`，其定义了色度平面的分辨率，与亮度平面上的、分辨率为 `a x 2` 的小块之间的关系。
* `a` 是水平采样参考 (通常是 4)，
* `x` 是第一行的色度样本数（相对于 a 的水平分辨率），
* `y` 是第二行的色度样本数。
> 存在的一个例外是 4:1:0，其在每个亮度平面分辨率为 4 x 4 的块内提供一个色度样本。
现代编解码器中使用的常用方案是： 4:4:4 (没有子采样), 4:2:2, 4:1:1, 4:2:0, 4:1:0 and 3:1:1。
> YCbCr 4:2:0 合并
>
> 这是使用 YCbCr 4:2:0 合并的一个图像的一块，注意我们每像素只花费 12bit。
>
> 
下图是同一张图片使用几种主要的色度子采样技术进行编码，第一行图像是最终的 YCbCr，而最后一行图像展示了色度的分辨率。这么小的损失确实是一个伟大的胜利。
前面我们计算过我们需要 [278GB 去存储一个一小时长，分辨率在720p和30fps的视频文件](#消除冗余)。如果我们使用 `YCbCr 4:2:0` 我们能减少`一半的大小（139GB）`*，但仍然不够理想。
> * 我们通过将宽、高、颜色深度和 fps 相乘得出这个值。前面我们需要 24 bit，现在我们只需要 12 bit。
> ### 自己动手：检查 YCbCr 直方图
> 你可以[使用 ffmpeg 检查 YCbCr 直方图](/encoding_pratical_examples.md#generates-yuv-histogram)。这个场景有更多的蓝色贡献，由[直方图](https://en.wikipedia.org/wiki/Histogram)显示。
>
> 
### 颜色, 亮度, 视频亮度, 伽马 视频回顾
观看这段精彩的视频，它解释什么是亮度并了解视频亮度、伽马和颜色。
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
> ### 自己动手: 检查 YCbCr 强度
> 你可以使用[FFmpeg's oscilloscope滤镜](https://ffmpeg.org/ffmpeg-filters.html#oscilloscope)可视化给定视频行的Y强度.
> ```bash
> ffplay -f lavfi -i 'testsrc2=size=1280x720:rate=30000/1001,format=yuv420p' -vf oscilloscope=x=0.5:y=200/720:s=1:c=1
> ```
> 
## 帧类型
现在我们进一步消除`时间冗余`，但在这之前让我们来确定一些基本术语。假设我们一段 30fps 的影片，这是最开始的 4 帧。
  
我们可以在帧内看到**很多重复内容**，如**蓝色背景**，从 0 帧到第 3 帧它都没有变化。为了解决这个问题，我们可以将它们**抽象地分类**为三种类型的帧。
### I 帧（帧内，关键帧）
I 帧（可参考，关键帧，帧内编码）是一个**自足的帧**。它不依靠任何东西来渲染，I 帧与静态图片相似。第一帧通常是 I 帧，但我们将看到 I 帧被定期插入其它类型的帧之间。
### P 帧（预测）
P 帧利用了一个事实：当前的画面几乎总能**使用之前的一帧进行渲染**。例如，在第二帧，唯一的改变是球向前移动了。仅仅使用（第二帧）对前一帧的引用和差值，我们就能重建前一帧。
 <- 
> #### 自己动手：具有单个 I 帧的视频
> 既然 P 帧使用较少的数据，为什么我们不能用[单个 I 帧和其余的 P 帧](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)来编码整个视频？
>
> 编码完这个视频之后，开始观看它，并**快进到视频的末尾部分**，你会注意到**它需要花一些时间**才真正跳转到这部分。这是因为 **P 帧需要一个引用帧**（比如 I 帧）才能渲染。
>
> 你可以做的另一个快速试验，是使用单个 I 帧编码视频，然后[再次编码且每 2 秒插入一个 I 帧](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second)，并**比较成品的大小**。
### B 帧（双向预测）
如何引用前面和后面的帧去做更好的压缩？！简单地说 B 帧就是这么做的。
 <-  -> 
> #### 自己动手：使用 B 帧比较视频
> 你可以生成两个版本，一个使用 B 帧，另一个[全部不使用 B 帧](/encoding_pratical_examples.md#no-b-frames-at-all)，然后查看文件的大小以及画质。
### 小结
这些帧类型用于提供更好的压缩率，我们将在下一章看到这是如何发生的。现在，我们可以想到 I 帧是昂贵的，P 帧是便宜的，最便宜的是 B 帧。
## 时间冗余（帧间预测）
让我们探究去除**时间上的重复**，去除这一类冗余的技术就是**帧间预测**。
我们将尝试**花费较少的数据量**去编码在时间上连续的 0 号帧和 1 号帧。
我们可以做个减法，我们简单地**用 0 号帧减去 1 号帧**，得到残差，这样我们就只需要**对残差进行编码**。
但我们有一个**更好的方法**来节省数据量。首先，我们将`0 号帧` 视为一个个分块的集合，然后我们将尝试将 `帧 1` 和 `帧 0` 上的块相匹配。我们可以将这看作是**运动预测**。
> ### 维基百科—块运动补偿
> “运动补偿是一种描述相邻帧（相邻在这里表示在编码关系上相邻，在播放顺序上两帧未必相邻）差别的方法，具体来说是描述前面一帧（相邻在这里表示在编码关系上的前面，在播放顺序上未必在当前帧前面）的每个小块怎样移动到当前帧中的某个位置去。”
我们预计那个球会从 `x=0, y=25` 移动到 `x=6, y=26`，**x** 和 **y** 的值就是**运动向量**。**进一步**节省数据量的方法是，只编码这两者运动向量的差。所以，最终运动向量就是 `x=6 (6-0), y=1 (26-25)`。
> 实际情况下，这个球会被切成 n 个分区，但处理过程是相同的。
帧上的物体**以三维方式移动**，当球移动到背景时会变小。当我们尝试寻找匹配的块，**找不到完美匹配的块**是正常的。这是一张运动预测与实际值相叠加的图片。
但我们能看到当我们使用**运动预测**时，**编码的数据量少于**使用简单的残差帧技术。
你可以[使用 jupyter 玩转这些概念](/frame_difference_vs_motion_estimation_plus_residual.ipynb)。
> ### 自己动手：查看运动向量
>
> 我们可以[使用 ffmpeg 生成包含帧间预测（运动向量）的视频](/encoding_pratical_examples.md#generate-debug-video)。
>
> 
>
> 或者我们也可使用 [Intel® Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)（需要付费，但也有只能查看前 10 帧的免费试用版）。
>
> 
## 空间冗余（帧内预测）
如果我们分析一个视频里的**每一帧**，我们会看到有**许多区域是相互关联的**。
让我们举一个例子。这个场景大部分由蓝色和白色组成。
这是一个 `I 帧`，我们**不能使用前面的帧来预测**，但我们仍然可以压缩它。我们将编码我们选择的那块红色区域。如果我们**看看它的周围**，我们可以**估计它周围颜色的变化**。
我们预测:帧中的颜色在垂直方向上保持一致，这意味着**未知像素的颜色与临近的像素相同**。
我们的**预测会出错**，所以我们需要先利用这项技术（**帧内预测**），然后**减去实际值**，算出残差，得出的矩阵比原始数据更容易压缩。
> ### 自己动手：查看帧内预测
> 你可以[使用 ffmpeg 生成包含宏块及预测的视频](/encoding_pratical_examples.md#generate-debug-video)。请查看 ffmpeg 文档以了解[每个块颜色的含义](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7)。
>
> 
>
> 或者我们也可使用 [Intel® Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)（需要付费，但也有只能查看前 10 帧的免费试用版）。
>
> 
# 视频编解码器是如何工作的？
## 是什么？为什么？怎么做？
**是什么？** 就是用于压缩或解压数字视频的软件或硬件。**为什么？** 人们需要在有限带宽或存储空间下提高视频的质量。还记得当我们计算每秒 30 帧，每像素 24 bit，分辨率是 480x240 的视频[需要多少带宽](#基本术语)吗？没有压缩时是 **82.944 Mbps**。电视或互联网提供 HD/FullHD/4K 只能靠视频编解码器。**怎么做？** 我们将简单介绍一下主要的技术。
> 视频编解码 vs 容器
>
> 初学者一个常见的错误是混淆数字视频编解码器和[数字视频容器](https://en.wikipedia.org/wiki/Digital_container_format)。我们可以将**容器**视为包含视频（也很可能包含音频）元数据的包装格式，**压缩过的视频**可以看成是它承载的内容。
>
> 通常，视频文件的格式定义其视频容器。例如，文件 `video.mp4` 可能是 [MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14) 容器，一个叫 `video.mkv` 的文件可能是 [matroska](https://en.wikipedia.org/wiki/Matroska)。我们可以使用 [ffmpeg 或 mediainfo](/encoding_pratical_examples.md#inspect-stream) 来完全确定编解码器和容器格式。
## 历史
在我们跳进通用编解码器内部工作之前，让我们回头了解一些旧的视频编解码器。
视频编解码器 [H.261](https://en.wikipedia.org/wiki/H.261) 诞生在 1990（技术上是 1988），被设计为以 **64 kbit/s 的数据速率**工作。它已经使用如色度子采样、宏块，等等理念。在 1995 年，**H.263** 视频编解码器标准被发布，并继续延续到 2001 年。
在 2003 年 **H.264/AVC** 的第一版被完成。在同一年，一家叫做 **TrueMotion** 的公司发布了他们的**免版税**有损视频压缩的视频编解码器，称为 **VP3**。在 2008 年，**Google 收购了**这家公司，在同一年发布 **VP8**。在 2012 年 12 月，Google 发布了 **VP9**，**市面上大约有 3/4 的浏览器**（包括手机）支持。
[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1) 是由 **Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel, Cisco** 等公司组成的[开放媒体联盟（AOMedia）](http://aomedia.org/)设计的一种新的免版税和开源的视频编解码器。**第一版** 0.1.0 参考编解码器**发布于 2016 年 4 月 7 号**。
> ### AV1 的诞生
>
> 2015 年早期，Google 正在开发VP10，Xiph (Mozilla) 正在开发Daala，Cisco 开源了其称为 Thor 的免版税视频编解码器。
>
> 接着 MPEG LA 宣布了 HEVC (H.265) 每年版税的的上限，比 H.264 高 8 倍，但很快他们又再次改变了条款：
> * **不设年度收费上限**
> * **收取内容费**（收入的 0.5%）
> * **每单位费用高于 h264 的 10 倍**
>
> [开放媒体联盟](http://aomedia.org/about/)由硬件厂商（Intel, AMD, ARM , Nvidia, Cisco），内容分发商（Google, Netflix, Amazon），浏览器维护者（Google, Mozilla），等公司创建。
>
> 这些公司有一个共同目标，一个免版税的视频编解码器，所以 AV1 诞生时使用了一个更[简单的专利许可证](http://aomedia.org/license/patent/)。**Timothy B. Terriberry** 做了一个精彩的介绍，[关于 AV1 的概念，许可证模式和它当前的状态](https://www.youtube.com/watch?v=lzPaldsmJbk)，就是本节的来源。
>
> 前往 [https://arewecompressedyet.com/analyzer/](https://arewecompressedyet.com/analyzer/)， 你会惊讶于**使用你的浏览器就可以分析 AV1 编解码器**。
> 
>
> 附：如果你想了解更多编解码器的历史，你需要了解[视频压缩专利](https://www.vcodex.com/video-compression-patents/)背后的基本知识。
## 通用编解码器
我们接下来要介绍**通用视频编解码器背后的主要机制**，大多数概念都很实用，并被现代编解码器如 VP9, AV1 和 HEVC 使用。需要注意：我们将简化许多内容。有时我们会使用真实的例子（主要是 H.264）来演示技术。
## 第一步 - 图片分区
第一步是**将帧**分成几个**分区**，**子分区**甚至更多。
**但是为什么呢**有许多原因，比如，当我们分割图片时，我们可以更精确的处理预测，在微小移动的部分使用较小的分区，而在静态背景上使用较大的分区。
通常，编解码器**将这些分区组织**成切片（或瓦片），宏（或编码树单元）和许多子分区。这些分区的最大大小有所不同，HEVC 设置成 64x64，而 AVC 使用 16x16，但子分区可以达到 4x4 的大小。
还记得我们学过的**帧的分类**吗？你也可以**把这些概念应用到块**，因此我们可以有 I 切片，B 切片，I 宏块等等。
> ### 自己动手：查看分区
>
> 我们也可以使用 [Intel® Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)（需要付费，但也有只能查看前 10 帧的免费试用版）。这是 [VP9 分区](/encoding_pratical_examples.md#transcoding)的分析。
>
> 
## 第二步 - 预测
一旦我们有了分区，我们就可以在它们之上做出预测。对于[帧间预测](#时间冗余帧间预测)，我们需要**发送运动向量和残差**；至于[帧内预测](#空间冗余帧内预测)，我们需要**发送预测方向和残差**。
## 第三步 - 转换
在我们得到残差块（`预测分区-真实分区`）之后，我们可以用一种方式**变换**它，这样我们就知道**哪些像素我们应该丢弃**，还依然能保持**整体质量**。这个确切的行为有几种变换方式。
尽管有[其它的变换方式](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms)，但我们重点关注离散余弦变换（DCT）。[DCT](https://en.wikipedia.org/wiki/Discrete_cosine_transform) 的主要功能有：
* 将**像素**块**转换**为相同大小的**频率系数块**。
* **压缩**能量，更容易消除空间冗余。
* **可逆的**，也意味着你可以还原回像素。
> 2017 年 2 月 2 号，F. M. Bayer 和 R. J. Cintra 发表了他们的论文：[图像压缩的 DCT 类变换只需要 14 个加法](https://arxiv.org/abs/1702.00817)。
如果你不理解每个要点的好处，不用担心，我们会尝试进行一些实验，以便从中看到真正的价值。
我们来看下面的**像素块**（8x8）：
下面是其渲染的块图像（8x8）：
当我们对这个像素块**应用 DCT** 时， 得到如下**系数块**（8x8）：
接着如果我们渲染这个系数块，就会得到这张图片：
如你所见它看起来完全不像原图像，我们可能会注意到**第一个系数**与其它系数非常不同。第一个系数被称为直流分量，代表了输入数组中的**所有样本**，有点**类似于平均值**。
这个系数块有一个有趣的属性：高频部分和低频部分是分离的。
在一张图像中，**大多数能量**会集中在[低频部分](https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm)，所以如果我们将图像转换成频率系数，并**丢掉高频系数**，我们就能**减少描述图像所需的数据量**，而不会牺牲太多的图像质量。
> 频率是指信号变化的速度。
让我们通过实验学习这点，我们将使用 DCT 把原始图像转换为频率（系数块），然后丢掉最不重要的系数。
首先，我们将它转换为其**频域**。
然后我们丢弃部分（67%）系数，主要是它的右下角部分。
然后我们从丢弃的系数块重构图像（记住，这需要可逆），并与原始图像相比较。
如我们所见它酷似原始图像，但它引入了许多与原来的不同，我们**丢弃了67.1875%**，但我们仍然得到至少类似于原来的东西。我们可以更加智能的丢弃系数去得到更好的图像质量，但这是下一个主题。
> ### 使用全部像素形成每个系数
>
> 需要注意的是，每个系数并不直接映射到单个像素，而是所有像素的加权和。这个神奇的图形展示了如何使用每个指数唯一的权重来计算第一个和第二个系数。
>
> 
>
> 来源：[https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm)
>
> 你也可以尝试[通过查看在 DCT 基础上形成的简单图片来可视化 DCT](/dct_better_explained.ipynb)。例如，这是使用每个系数权重[形成的字符 A](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT)。
>
> 
> ### 自己动手：丢弃不同的系数
> 你可以玩转 [DCT 变换](/uniform_quantization_experience.ipynb)
## 第四步 - 量化
当我们丢弃一些系数时，在最后一步（变换），我们做了一些形式的量化。这一步，我们选择性地剔除信息（**有损部分**）或者简单来说，我们将**量化系数以实现压缩**。
我们如何量化一个系数块？一个简单的方法是均匀量化，我们取一个块并**将其除以单个的值**（10），并舍入值。
我们如何**逆转**（重新量化）这个系数块？我们可以通过**乘以我们先前除以的相同的值**（10）来做到。
这**不是最好的方法**，因为它没有考虑到每个系数的重要性，我们可以使用一个**量化矩阵**来代替单个值，这个矩阵可以利用 DCT 的属性，多量化右下部，而少（量化）左上部，[JPEG 使用了类似的方法](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html)，你可以通过[查看源码看看这个矩阵](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40)。
> ### 自己动手：量化
> 你可以玩转[量化](/dct_experiences.ipynb)
## 第五步 - 熵编码
在我们量化数据（图像块／切片／帧）之后，我们仍然可以以无损的方式来压缩它。有许多方法（算法）可用来压缩数据。我们将简单体验其中几个，你可以阅读这本很棒的书去深入理解：[Understanding Compression: Data Compression for Modern Developers](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/)。
### VLC 编码：
让我们假设我们有一个符号流：**a**, **e**, **r** 和 **t**，它们的概率（从0到1）由下表所示。
| | a | e | r | t |
|-----|-----|-----|------|-----|
| 概率 | 0.3 | 0.3 | 0.2 | 0.2 |
我们可以分配不同的二进制码，（最好是）小的码给最可能（出现的字符），大些的码给最少可能（出现的字符）。
| | a | e | r | t |
|--------|-----|-----|------|-----|
| 概率 | 0.3 | 0.3 | 0.2 | 0.2 |
| 二进制码 | 0 | 10 | 110 | 1110 |
让我们压缩 **eat** 流，假设我们为每个字符花费 8 bit，在没有做任何压缩时我们将花费 **24 bit**。但是在这种情况下，我们使用各自的代码来替换每个字符，我们就能节省空间。
第一步是编码字符 **e** 为 `10`，第二个字符是 **a**，追加（不是数学加法）后是 `[10][0]`，最后是第三个字符 **t**，最终组成已压缩的比特流 `[10][0][1110]` 或 `1001110`，这只需 **7 bit**（比原来的空间少 3.4 倍）。
请注意每个代码必须是唯一的前缀码，[Huffman 能帮你找到这些数字](https://en.wikipedia.org/wiki/Huffman_coding)。虽然它有一些问题，但是[视频编解码器仍然提供该方法](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding)，它也是很多应用程序的压缩算法。
编码器和解码器都**必须知道**这个（包含编码的）字符表，因此，你也需要传送这个表。
### 算术编码
让我们假设我们有一个符号流：**a**, **e**, **r**, **s** 和 **t**，它们的概率由下表所示。
| | a | e | r | s | t |
|-----|-----|-----|------|------|-----|
| 概率 | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
考虑到这个表，我们可以构建一个区间，区间包含了所有可能的字符，字符按出现概率排序。
让我们编码 **eat** 流，我们选择第一个字符 **e** 位于 **0.3 到 0.6** （但不包括 0.6）的子区间，我们选择这个子区间，按照之前同等的比例再次分割。
让我们继续编码我们的流 **eat**，现在使第二个 **a** 字符位于 **0.3 到 0.39** 的区间里，接着再次用同样的方法编码最后的字符 **t**，得到最后的子区间 **0.354 到 0.372**。
我们只需从最后的子区间 0.354 到 0.372 里选择一个数，让我们选择 0.36，不过我们可以选择这个子区间里的任何数。仅靠这个数，我们将可以恢复原始流 **eat**。就像我们在区间的区间里画了一根线来编码我们的流。
**反向过程**（又名解码）一样简单，用数字 **0.36** 和我们原始区间，我们可以进行同样的操作，不过现在是使用这个数字来还原被编码的流。
在第一个区间，我们发现数字落入了一个子区间，因此，这个子区间是我们的第一个字符，现在我们再次切分这个子区间，像之前一样做同样的过程。我们会注意到 **0.36** 落入了 **a** 的区间，然后我们重复这一过程直到得到最后一个字符 **t**（形成我们原始编码过的流 eat）。
编码器和解码器都**必须知道**字符概率表，因此，你也需要传送这个表。
非常巧妙，不是吗？人们能想出这样的解决方案实在是太聪明了，一些[视频编解码器使用](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding)这项技术（或至少提供这一选择）。
关于无损压缩量化比特流的办法，这篇文章无疑缺少了很多细节、原因、权衡等等。作为一个开发者你[应该学习更多](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/)。刚入门视频编码的人可以尝试使用不同的[熵编码算法，如ANS](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)。
> ### 自己动手：CABAC vs CAVLC
> 你可以[生成两个流，一个使用 CABAC，另一个使用 CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc)，并比较生成每一个的时间以及最终的大小。
## 第六步 - 比特流格式
完成所有这些步之后，我们需要将**压缩过的帧和内容打包进去**。需要明确告知解码器**编码定义**，如颜色深度，颜色空间，分辨率，预测信息（运动向量，帧内预测方向），档次\*，级别\*，帧率，帧类型，帧号等等更多信息。
> * 译注：原文为 profile 和 level，没有通用的译名
我们将简单地学习 H.264 比特流。第一步是[生成一个小的 H.264\* 比特流](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream)，可以使用本 repo 和 [ffmpeg](http://ffmpeg.org/) 来做。
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * ffmpeg 默认将所有参数添加为 **SEI NAL**，很快我们会定义什么是 NAL。
这个命令会使用下面的图片作为帧，生成一个具有**单个帧**，64x64 和颜色空间为 yuv420 的原始 h264 比特流。
> 
### H.264 比特流
AVC (H.264) 标准规定信息将在宏帧（网络概念上的）内传输，称为 [NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)（网络抽象层）。NAL 的主要目标是提供“网络友好”的视频呈现方式，该标准必须适用于电视（基于流），互联网（基于数据包）等。
[同步标记](https://en.wikipedia.org/wiki/Frame_synchronization)用来定义 NAL 单元的边界。每个同步标记的值固定为 `0x00 0x00 0x01` ，最开头的标记例外，它的值是 `0x00 0x00 0x00 0x01` 。如果我们在生成的 h264 比特流上运行 **hexdump**，我们可以在文件的开头识别至少三个 NAL。
我们之前说过，解码器需要知道不仅仅是图片数据，还有视频的详细信息，如：帧、颜色、使用的参数等。每个 NAL 的**第一位**定义了其分类和**类型**。
| NAL type id | 描述 |
|--- |---|
| 0 | Undefined |
| 1 | Coded slice of a non-IDR picture |
| 2 | Coded slice data partition A |
| 3 | Coded slice data partition B |
| 4 | Coded slice data partition C |
| 5 | **IDR** Coded slice of an IDR picture |
| 6 | **SEI** Supplemental enhancement information |
| 7 | **SPS** Sequence parameter set |
| 8 | **PPS** Picture parameter set |
| 9 | Access unit delimiter |
| 10 | End of sequence |
| 11 | End of stream |
| ... | ... |
通常，比特流的第一个 NAL 是 **SPS**，这个类型的 NAL 负责传达通用编码参数，如**档次，级别，分辨率**等。
如果我们跳过第一个同步标记，就可以通过解码**第一个字节**来了解第一个 **NAL 的类型**。
例如同步标记之后的第一个字节是 `01100111`，第一位（`0`）是 **forbidden_zero_bit** 字段，接下来的两位（`11`）告诉我们是 **nal_ref_idc** 字段，其表示该 NAL 是否是参考字段，其余 5 位（`00111`）告诉我们是 **nal_unit_type** 字段，在这个例子里是 NAL 单元 **SPS** (7)。
SPS NAL 的第 2 位 (`binary=01100100, hex=0x64, dec=100`) 是 **profile_idc** 字段，显示编码器使用的配置，在这个例子里，我们使用[高阶档次](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)，一种没有 B（双向预测） 切片支持的高阶档次。
当我们阅读 SPS NAL 的 H.264 比特流规范时，会为**参数名称**，**分类**和**描述**找到许多值，例如，看看字段 `pic_width_in_mbs_minus_1` 和 `pic_height_in_map_units_minus_1`。
| 参数名称 | 分类 | 描述 |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: 无符号整形 [Exp-Golomb-coded](https://pythonhosted.org/bitstring/exp-golomb.html)
如果我们对这些字段的值进行一些计算，将最终得出**分辨率**。我们可以使用值为 `119（ (119 + 1) * macroblock_size = 120 * 16 = 1920）`的 `pic_width_in_mbs_minus_1` 表示 `1920 x 1080`，再次为了减少空间，我们使用 `119` 来代替编码 `1920`。
如果我们再次使用二进制视图检查我们创建的视频 (ex: `xxd -b -c 11 v/minimal_yuv420.h264`)，可以跳到帧自身上一个 NAL。
我们可以看到最开始的 6 个字节：`01100101 10001000 10000100 00000000 00100001 11111111`。我们已经知道第一个字节告诉我们 NAL 的类型，在这个例子里， (`00101`) 是 **IDR 切片 (5)**，可以进一步检查它：
对照规范，我们能解码切片的类型（**slice_type**），帧号（**frame_num**）等重要字段。
为了获得一些字段（`ue(v), me(v), se(v) 或 te(v)`）的值，我们需要称为 [Exponential-Golomb](https://pythonhosted.org/bitstring/exp-golomb.html) 的特定解码器来解码它。当存在很多默认值时，这个方法编码变量值特别高效。
> 这个视频里 **slice_type** 和 **frame_num** 的值是 7（I 切片）和 0（第一帧）。
我们可以将**比特流视为一个协议**，如果你想学习更多关于比特流的内容，请参考 [ITU H.264 规范](http://www.itu.int/rec/T-REC-H.264-201610-I)。这个宏观图展示了图片数据（压缩过的 YUV）所在的位置。
我们可以探究其它比特流，如 [VP9 比特流](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf)，[H.265（HEVC）](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf)或是我们的新朋友 [AV1 比特流](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8)，[他们很相似吗？不](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/)，但只要学习了其中之一，学习其他的就简单多了。
> ### 自己动手：检查 H.264 比特流
>
> 我们可以[生成一个单帧视频](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video)，使用 [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) 检查它的 H.264 比特流。事实上，你甚至可以查看[解析 h264(AVC) 视频流的源代码](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp)。
>
> 
>
> 我们也可使用 [Intel® Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)，需要付费，但也有只能查看前 10 帧的免费试用版，这已经够达成学习目的了。
>
> 
## 回顾
我们可以看到我们学了许多**使用相同模型的现代编解码器**。事实上，让我们看看 Thor 视频编解码器框图，它包含所有我们学过的步骤。你现在应该能更好地理解数字视频领域内的创新和论文。
之前我们计算过我们[需要 139GB 来保存一个一小时，720p 分辨率和30fps的视频文件](#色度子采样)，如果我们使用在这里学过的技术，如**帧间和帧内预测，转换，量化，熵编码和其它**我们能实现——假设我们**每像素花费 0.031 bit**——同样观感质量的视频，**对比 139GB 的存储，只需 367.82MB**。
> 我们根据这里提供的示例视频选择**每像素使用 0.031 bit**。
## H.265 如何实现比 H.264 更好的压缩率
我们已经更多地了解了编解码器的工作原理，那么就容易理解新的编解码器如何使用更少的数据量传输更高分辨率的视频。
我们将比较 AVC 和 HEVC，要记住的是：我们几乎总是要在压缩率和更多的 CPU 周期（复杂度）之间作权衡。
HEVC 比 AVC 有更大和更多的**分区**（和**子分区**）选项，更多**帧内预测方向**，**改进的熵编码**等，所有这些改进使得 H.265 比 H.264 的压缩率提升 50%。
# 在线流媒体
## 通用架构
[TODO]
## 渐进式下载和自适应流
[TODO]
## 内容保护
我们可以用一个简单的令牌认证系统来保护视频。用户需要拥有一个有效的令牌才可以播放视频，CDN 会拒绝没有令牌的用户的请求。它与大多数网站的身份认证系统非常相似。
仅仅使用令牌认证系统，用户仍然可以下载并重新分发视频。DRM 系统可以用来避免这种情况。
实际情况下，人们通常同时使用这两种技术提供授权和认证。
### DRM
#### 主要系统
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### 是什么
DRM 指的是数字版权管理，是一种**为数字媒体提供版权保护**的方法，例如数字视频和音频。尽管用在了很多场合，但它并[没有被普遍接受](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### 为什么
内容的创作者（大多是工作室/制片厂）希望保护他们的知识产权，使他们的数字媒体免遭未经授权的分发。
#### 怎么做
我们将用一种简单的、抽象的方式描述 DRM
现有一份**内容 C1**（如 HLS 或 DASH 视频流），一个**播放器 P1**（如 shaka-clappr, exo-player 或 iOS），装在**设备 D1**（如智能手机、电视或台式机/笔记本）上，使用 **DRM 系统 DRM1**（如 FairPlay Streaming, PlayReady, Widevine）
内容 C1 由 DRM1 用一个**对称密钥 K1** 加密，生成**加密内容 C'1**
设备 D1 上的播放器 P1 有一个非对称密钥对，密钥对包含一个**私钥 PRK1**（这个密钥是受保护的1，只有 **D1** 知道密钥内容），和一个**公钥 PUK1**
> **1受保护的**: 这种保护可以**通过硬件**进行保护，例如, 将这个密钥存储在一个特殊的芯片（只读）中，芯片的工作方式就像一个用来解密的[黑箱]。 或**通过软件**进行保护（较低的安全系数）。DRM 系统提供了识别设备所使用的保护类型的方法。
当 **播放器 P1 希望播放***加密内容 C'1** 时，它需要与 **DRM1** 协商，将公钥 **PUK1** 发送给 DRM1, DRM1 会返回一个被公钥 **PUK1** **加密过的 K1**。按照推论，结果就是**只有 D1 能够解密**。
`K1P1D1 = enc(K1, PUK1)`
**P1** 使用它的本地 DRM 系统（这可以使用 [SoC](https://zh.wikipedia.org/wiki/系统芯片) ，一个专门的硬件和软件，这个系统可以使用它的私钥 PRK1 用来**解密**内容，它可以解密被加密过的**K1P1D1 的对称密钥 K1**。理想情况下，密钥不会被导出到内存以外的地方。
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# 如何使用 jupyter
确保你已安装 docker，只需运行 `./s/start_jupyter.sh`，然后按照控制台的说明进行操作。
# 会议
* [DEMUXED](https://demuxed.com/) - 您可以[查看最近的2个活动演示](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA)。
# 参考
这里有最丰富的资源，这篇文档包含的信息，均摘录、依据或受它们启发。你可以用这些精彩的链接，书籍，视频等深化你的知识。
在线课程和教程：
* [https://www.coursera.org/learn/digital/](https://www.coursera.org/learn/digital/)
* [https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf](https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf)
* [https://xiph.org/video/vid1.shtml](https://xiph.org/video/vid1.shtml)
* [https://xiph.org/video/vid2.shtml](https://xiph.org/video/vid2.shtml)
* [http://slhck.info/ffmpeg-encoding-course](http://slhck.info/ffmpeg-encoding-course)
* [http://www.cambridgeincolour.com/tutorials/camera-sensors.htm](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm)
* [http://www.slideshare.net/vcodex/a-short-history-of-video-coding](http://www.slideshare.net/vcodex/a-short-history-of-video-coding)
* [http://www.slideshare.net/vcodex/introduction-to-video-compression-1339433](http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338)
* [https://developer.android.com/guide/topics/media/media-formats.html](https://developer.android.com/guide/topics/media/media-formats.html)
* [http://www.slideshare.net/MadhawaKasun/audio-compression-23398426](http://www.slideshare.net/MadhawaKasun/audio-compression-23398426)
* [http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf](http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf)
书籍:
* [https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1)
* [https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925](https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925)
* [https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO](https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO)
* [https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer](https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer)
比特流规范:
* [http://www.itu.int/rec/T-REC-H.264-201610-I](http://www.itu.int/rec/T-REC-H.264-201610-I)
* [http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en](http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en)
* [https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf)
* [http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf](http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf)
* [http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243](http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243)
* [http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html](http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html)
软件:
* [https://ffmpeg.org/](https://ffmpeg.org/)
* [https://ffmpeg.org/ffmpeg-all.html](https://ffmpeg.org/ffmpeg-all.html)
* [https://ffmpeg.org/ffprobe.html](https://ffmpeg.org/ffprobe.html)
* [https://trac.ffmpeg.org/wiki/](https://trac.ffmpeg.org/wiki/)
* [https://software.intel.com/en-us/intel-video-pro-analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)
* [https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8)
非-ITU 编解码器:
* [https://aomedia.googlesource.com/](https://aomedia.googlesource.com/)
* [https://github.com/webmproject/libvpx/tree/master/vp9](https://github.com/webmproject/libvpx/tree/master/vp9)
* [https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml](https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml)
* [https://people.xiph.org/~jm/daala/revisiting/](https://people.xiph.org/~jm/daala/revisiting/)
* [https://www.youtube.com/watch?v=lzPaldsmJbk](https://www.youtube.com/watch?v=lzPaldsmJbk)
* [https://fosdem.org/2017/schedule/event/om_av1/](https://fosdem.org/2017/schedule/event/om_av1/)
编码概念:
* [http://x265.org/hevc-h265/](http://x265.org/hevc-h265/)
* [http://slhck.info/video/2017/03/01/rate-control.html](http://slhck.info/video/2017/03/01/rate-control.html)
* [http://slhck.info/video/2017/02/24/vbr-settings.html](http://slhck.info/video/2017/02/24/vbr-settings.html)
* [http://slhck.info/video/2017/02/24/crf-guide.html](http://slhck.info/video/2017/02/24/crf-guide.html)
* [https://arxiv.org/pdf/1702.00817v1.pdf](https://arxiv.org/pdf/1702.00817v1.pdf)
* [https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors)
* [http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html](http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html)
* [http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html](http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html)
* [https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/](https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/)
测试用视频序列:
* [http://bbb3d.renderfarming.net/download.html](http://bbb3d.renderfarming.net/download.html)
* [https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx](https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx)
杂项:
* [http://stackoverflow.com/a/24890903](http://stackoverflow.com/a/24890903)
* [http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264](http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264)
* [http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html](http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html)
* [http://vanseodesign.com/web-design/color-luminance/](http://vanseodesign.com/web-design/color-luminance/)
* [http://www.biologymad.com/nervoussystem/eyenotes.htm](http://www.biologymad.com/nervoussystem/eyenotes.htm)
* [http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf](http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf)
* [http://www.csc.villanova.edu/~rschumey/csc4800/dct.html](http://www.csc.villanova.edu/~rschumey/csc4800/dct.html)
* [http://www.explainthatstuff.com/digitalcameras.html](http://www.explainthatstuff.com/digitalcameras.html)
* [http://www.hkvstar.com](http://www.hkvstar.com)
* [http://www.hometheatersound.com/](http://www.hometheatersound.com/)
* [http://www.lighterra.com/papers/videoencodingh264/](http://www.lighterra.com/papers/videoencodingh264/)
* [http://www.red.com/learn/red-101/video-chroma-subsampling](http://www.red.com/learn/red-101/video-chroma-subsampling)
* [http://www.slideshare.net/ManoharKuse/hevc-intra-coding](http://www.slideshare.net/ManoharKuse/hevc-intra-coding)
* [http://www.slideshare.net/mwalendo/h264vs-hevc](http://www.slideshare.net/mwalendo/h264vs-hevc)
* [http://www.slideshare.net/rvarun7777/final-seminar-46117193](http://www.slideshare.net/rvarun7777/final-seminar-46117193)
* [http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf](http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf)
* [http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx](http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx)
* [http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1](http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1)
* [http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/](http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/)
* [https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/](https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/)
* [https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/](https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/)
* [https://codesequoia.wordpress.com/category/video/](https://codesequoia.wordpress.com/category/video/)
* [https://developer.apple.com/library/content/technotes/tn2224/_index.html](https://developer.apple.com/library/content/technotes/tn2224/_index.html)
* [https://en.wikibooks.org/wiki/MeGUI/x264_Settings](https://en.wikibooks.org/wiki/MeGUI/x264_Settings)
* [https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming](https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming)
* [https://en.wikipedia.org/wiki/AOMedia_Video_1](https://en.wikipedia.org/wiki/AOMedia_Video_1)
* [https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg](https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg)
* [https://en.wikipedia.org/wiki/Cone_cell](https://en.wikipedia.org/wiki/Cone_cell)
* [https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg](https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg)
* [https://en.wikipedia.org/wiki/Inter_frame](https://en.wikipedia.org/wiki/Inter_frame)
* [https://en.wikipedia.org/wiki/Intra-frame_coding](https://en.wikipedia.org/wiki/Intra-frame_coding)
* [https://en.wikipedia.org/wiki/Photoreceptor_cell](https://en.wikipedia.org/wiki/Photoreceptor_cell)
* [https://en.wikipedia.org/wiki/Pixel_aspect_ratio](https://en.wikipedia.org/wiki/Pixel_aspect_ratio)
* [https://en.wikipedia.org/wiki/Presentation_timestamp](https://en.wikipedia.org/wiki/Presentation_timestamp)
* [https://en.wikipedia.org/wiki/Rod_cell](https://en.wikipedia.org/wiki/Rod_cell)
* [https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg](https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg)
* [https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/](https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/)
* [https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping](https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping)
* [https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/](https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/)
* [https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03)
* [https://www.encoding.com/android/](https://www.encoding.com/android/)
* [https://www.encoding.com/http-live-streaming-hls/](https://www.encoding.com/http-live-streaming-hls/)
* [https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm](https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm)
* [https://www.lifewire.com/cmos-image-sensor-493271](https://www.lifewire.com/cmos-image-sensor-493271)
* [https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ](https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ)
* [https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar](https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar)
* [https://www.vcodex.com/h264avc-intra-precition/](https://www.vcodex.com/h264avc-intra-precition/)
* [https://www.youtube.com/watch?v=9vgtJJ2wwMA](https://www.youtube.com/watch?v=9vgtJJ2wwMA)
* [https://www.youtube.com/watch?v=LFXN9PiOGtY](https://www.youtube.com/watch?v=LFXN9PiOGtY)
* [https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6](https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6)
* [https://www.youtube.com/watch?v=LWxu4rkZBLw](https://www.youtube.com/watch?v=LWxu4rkZBLw)
================================================
FILE: README-es.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
[](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)
# Introducción
Esto es una breve introducción a la tecnología de vídeo, principalmente dirigido a desarrolladores o ingenieros de software. La intención es que sea **fácil de aprender para cualquier persona**. La idea nació durante un [breve taller para personas nuevas en vídeo](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p).
El objetivo es introducir algunos conceptos de vídeo digital con un **vocabulario simple, muchas figuras y ejemplos prácticos** en cuanto sea posible, y dejar disponible este conocimiento. Por favor, siéntete libre de enviar correcciones, sugerencias y mejoras.
Habrán **prácticas** que requiere tener instalado **docker** y este repositorio clonado.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **ADVERTENCIA**: cuando observes el comando `./s/ffmpeg` o `./s/mediainfo`, significa que estamos ejecutando la **versión contenerizada** del mismo, que a su vez incluye todos los requerimientos necesarios.
Todas las **prácticas deberán ser ejecutadas desde el directorio donde has clonado** este repositorio. Para ejecutar los **ejemplos de jupyter** deberás primero iniciar el servicio `./s/start_jupyter.sh`, copiar la URL y pegarla en tu navegador.
# Control de cambios
* Detalles sobre sistemas DRM
* Versión 1.0.0 liberada
* Traducción a Chino Simplificado
* Ejemplo de FFmpeg oscilloscope filter
* Traducción a Portugués (Brasil)
* Traducción a Español
# Tabla de contenidos
- [Introducción](#introducción)
- [Tabla de contenidos](#tabla-de-contenidos)
- [Terminología Básica](#terminología-básica)
* [Otras formas de codificar una imagen a color](#otras-formas-de-codificar-una-imagen-a-color)
* [Práctica: juguemos con una imagen y colores](#práctica-juguemos-con-una-imagen-y-colores)
* [DVD es DAR 4:3](#dvd-es-dar-43)
* [Práctica: Verifiquemos las propiedades del vídeo](#práctica-verifiquemos-las-propiedades-del-vídeo)
- [Eliminación de Redundancias](#eliminación-de-redundancias)
* [Colores, Luminancia y nuestros ojos](#colores-luinancia-y-nuestros-ojos)
+ [Modelo de color](#modelo-de-color)
+ [Conversiones entre YCbCr y RGB](#conversiones-entre-ycbcr-y-rgb)
+ [Chroma subsampling](#chroma-subsampling)
+ [Práctica: Observemos el histograma YCbCr](#práctica-observemos-el-histograma-ycbcr)
+ [Color, luma, luminancia, gamma](#color-luma-luminancia-gamma)
+ [Práctica: Observa la intensidad YCbCr](#práctica-observa-la-intensidad-ycbcr)
* [Tipos de fotogramas](#tipos-de-fotogramas)
+ [I Frame (intra, keyframe)](#i-frame-intra-keyframe)
+ [P Frame (predicted)](#p-frame-predicted)
- [Práctica: Un vídeo con un solo I-frame](#práctica-un-vídeo-con-un-solo-i-frame)
+ [B Frame (bi-predictive)](#b-frame-bi-predictive)
- [Práctica: Compara vídeos con B-frame](#práctica-compara-videos-con-b-frame)
+ [Resumen](#resumen)
* [Redundancia Temporal (inter prediction)](#redundancia-temporal-inter-prediction)
- [Práctica: Observa los vectores de movimiento](#práctica-observa-los-vectores-de-movimiento)
* [Redundancia Espacial (intra prediction)](#redundancia-espacial-intra-prediction)
- [Práctica: Observa las intra predictions](#práctica-observa-las-intra-predictions)
- [¿Cómo funciona un códec de vídeo?](#cómo-funciona-un-códec-de-vídeo)
* [¿Qué? ¿Por qué? ¿Cómo?](#qué-por-qué-cómo)
* [Historia](#historia)
+ [El nacimiento de AV1](#el-nacimiento-de-av1)
* [Un códec genérico](#un-códec-genérico)
* [Paso 1 - Particionado de imágenes](#paso-1---particionado-de-imágenes)
+ [Práctica: Verificar particiones](#práctica-verificar-particiones)
* [Paso 2 - Predicciones](#paso-2---predicciones)
* [Paso 3 - Transformación](#paso-3---transformación)
+ [Práctica: Descartar diferentes coeficientes](#práctica-descartar-diferentes-coeficientes)
* [Paso 4 - Cuantización](#paso-4---cuantización)
+ [Práctica: Cuantización](#práctica-cuantización)
* [Paso 5 - Codificación de la entropía](#paso-5---codificación-de-la-entropía)
+ [Codificación VLC](#codificación-vlc)
+ [Codificación aritmética](#codificación-aritmética)
+ [Práctica: CABAC vs CAVLC](#práctica-cabac-vs-cavlc)
* [Paso 6 - formato *bitstream*](#paso-6---formato-bitstream)
+ [H.264 bitstream](#h264-bitstream)
+ [Práctica: Inspeccionar el *bitstream* H.264](#práctica-inspeccionar-el-bitstream-h264)
* [Repaso](#repaso)
* [¿Cómo logra H.265 una mejor relación de compresión que H.264?](#cómo-logra-h265-una-mejor-relación-de-compresión-que-h264)
- [Online streaming](#online-streaming)
* [Arquitectura general](#arquitectura-general)
* [Descarga progresiva y streaming adaptativo](#descarga-progresiva-y-streaming-adaptativo)
* [Protección de contenido](#protección-de-contenido)
- [¿Cómo usar jupyter?](#cómo-usar-jupyter)
- [Conferencias](#conferencias)
- [Referencias](#referencias)
# Terminología Básica
Una **imagen** puede ser pensada como una **matriz 2D**. Si pensamos en **colores**, podemos extrapolar este concepto a que una imagen es una **matriz 3D** donde la **dimensión adicional** es usada para indicar la **información de color**.
Si elegimos representar esos colores usando los [colores primarios (rojo, verde y azul)](https://es.wikipedia.org/wiki/Color_primario), definimos tres planos: el primero para el **rojo**, el segundo para el **verde**, y el último para el **azul**.
Llamaremos a cada punto en esta matriz **un píxel** (del inglés, *picture element*). Un píxel representa la **intensidad** (usualmente un valor numérico) de un color dado. Por ejemplo, un **píxel rojo** significa 0 de verde, 0 de azul y el máximo de rojo. El **píxel de color rosa** puede formarse de la combinación de estos tres colores. Usando la representación numérica con un rango desde 0 a 255, el píxel rosa es definido como **Rojo=255, Verde=192 y Azul=203**.
> #### Otras formas de codificar una imagen a color
> Otros modelos pueden ser utilizados para representar los colores que forman una imagen. Por ejemplo, podemos usar una paleta indexada donde necesitaremos solamente un byte para representar cada píxel en vez de los 3 necesarios cuando usamos el modelo RGB. En un modelo como este, podemos utilizar una matriz 2D para representar nuestros colores, esto nos ayudaría a ahorrar memoria aunque tendríamos menos colores para representar.
>
> 
Observemos la figura que se encuentra a continuación. La primer cara muestra todos los colores utilizados. Mientras las demás muestran a intencidad de cada plano rojo, verde y azul (representado en escala de grises).
Podemos ver que el **color rojo** es el que **contribuye más** (las partes más brillantes en la segunda cara) mientras que en la cuarta cara observamos que el **color azul** su contribución se **limita a los ojos de Mario** y parte de su ropa. También podemos observar como **todos los planos no contribuyen demasiado** (partes oscuras) al **bigote de Mario**.
Cada intensidad de color requiere de una cantidad de bits para ser representada, esa cantidad es conocida como **bit depth**. Digamos que utilizamos **8 bits** (aceptando valores entre 0 y 255) por color (plano), entonces tendremos una **color depth** (profundidad de color) de **24 bits** (8 bits * 3 planos R/G/B), por lo que podemos inferir que tenemos disponibles 2^24 colores diferentes.
> **Es asombroso** aprender [como una imagen es capturada desde el Mundo a los bits](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm).
Otra propiedad de una images es la **resolución**, que es el número de píxeles en una dimensión. Frecuentemente es presentado como ancho × alto (*width × height*), por ejemplo, la siguiente imagen es **4×4**.
> #### Práctica: juguemos con una imagen y colores
> Puedes [jugar con una imagen y colores](/image_as_3d_array.ipynb) usando [jupyter](#how-to-use-jupyter) (python, numpy, matplotlib, etc).
>
> También puedes aprender [cómo funcionan los filtros de imágenes (edge detection, sharpen, blur...)](/filters_are_easy.ipynb).
Trabajando con imágenes o vídeos, te encontrarás con otra propiedad llamada **aspect ratio** (relación de aspecto) que simplemente describe la relación proporcional que existe entre el ancho y alto de una imagen o píxel.
Cuando las personas dicen que una película o imagen es **16x9** usualmente se están refiriendo a la relación de aspecto de la pantalla (**Display Aspect Ratio (DAR)**), sin embargo podemos tener diferentes formas para cada píxel, a ello le llamamos relación de aspecto del píxel (**Pixel Aspect Ratio (PAR)**).
> #### DVD es DAR 4:3
> La resolución del formato de DVD es 704x480, igualmente mantiente una relación de aspecto (DAR) de 4:3 porque tiene PAR de 10:11 (704x10/480x11)
Finalmente, podemos definir a un **vídeo** como una **sucesión de *n* fotogramas** en el **tiempo**, la cual la podemos definir como otra dimensión, *n* es el *frame rate* o fotogramas por segundo (**FPS**, del inglés *frames per second*).
El número necesario de bits por segundo para mostrar un vídeo es llamado **bit rate**.
> bit rate = ancho * alto * bit depth * FPS
Por ejemplo, un vídeo de 30 fotogramas por segundo, 24 bits por píxel, 480x240 de resolución necesitaría de **82,944,000 bits por segundo** o 82.944 Mbps (30x480x240x24) si no queremos aplicar ningún tipo de compresión.
Cuando el **bit rate** es casi constante es llamado bit rate constante (**CBR**, del inglés *constant bit rate*), pero también puede ser variable por lo que lo llamaremos bit rate variable (**VBR**, del inglés *variable bit rate*).
> La siguiente gráfica muestra como utilizando VBR no se necesita gastar demasiados bits para un fotograma es negro.
>
> 
En otros tiempos, unos ingenieros idearon una técnica para duplicar la cantidad de fotogramas por segundo percividos en una pantalla **sin consumir ancho de banda adicional**. Esta técnica es conocida como **vídeo entrelazado** (*interlaced video*); básicamente envía la mitad de la pantalla en un "fotograma" y la otra mitad en el siguiente "fotograma".
Hoy en día, las pantallas representan imágenes principalmente utilizando la **técnica de escaneo progresivo** (*progressive vídeo*). El vídeo progresivo es una forma de mostrar, almacenar o transmitir imágenes en movimiento en la que todas las líneas de cada fotograma se dibujan en secuencia.
Ahora ya tenemos una idea acerca de cómo una **imagen** es representada digitalmente, cómo sus **colores** son codificados, cuántos **bits por segundo** necesitamos para mostrar un vídeo, si es constante (CBR) o variable (VBR), con una **resolución** dada a determinado **frame rate** y otros términos como entrelazado, PAR, etc.
> #### Práctica: Verifiquemos las propiedades del vídeo
> Puedes [verificar la mayoría de las propiedades mencionadas con ffmpeg o mediainfo.](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)
# Eliminación de Redundancias
Aprendimos que no es factible utilizar vídeo sin ninguna compresión; **un solo vídeo de una hora** a una resolución de 720p y 30fps **requeriría 278GB***. Dado que **utilizar únicamente algoritmos de compresión de datos sin pérdida** como DEFLATE (utilizado en PKZIP, Gzip y PNG), **no disminuiría** suficientemente el ancho de banda necesario, debemos encontrar otras formas de comprimir el vídeo.
> * Encontramos este número multiplicando 1280 x 720 x 24 x 30 x 3600 (ancho, alto, bits por píxel, fps y tiempo en segundos).
Para hacerlo, podemos **aprovechar cómo funciona nuestra visión**. Somos mejores para distinguir el brillo que los colores, las **repeticiones en el tiempo**, un vídeo contiene muchas imágenes con pocos cambios, y las **repeticiones dentro de la imagen**, cada fotograma también contiene muchas áreas que utilizan colores iguales o similares.
## Colores, Luminancia y nuestros ojos
Nuestros ojos son [más sensibles al brillo que a los colores](http://vanseodesign.com/web-design/color-luminance/), puedes comprobarlo por ti mismo, mira esta imagen.
Si no puedes ver que los colores de los **cuadrados A y B son idénticos** en el lado izquierdo, no te preocupes, es nuestro cerebro jugándonos una pasada para que **prestemos más atención a la luz y la oscuridad que al color**. Hay un conector, del mismo color, en el lado derecho para que nosotros (nuestro cerebro) podamos identificar fácilmente que, de hecho, son del mismo color.
> **Explicación simplista de cómo funcionan nuestros ojos**
> El [ojo es un órgano complejo](http://www.biologymad.com/nervoussystem/eyenotes.htm), compuesto por muchas partes, pero principalmente nos interesan las células de conos y bastones. El ojo [contiene alrededor de 120 millones de células de bastones y 6 millones de células de conos](https://en.wikipedia.org/wiki/Photoreceptor_cell).
>
> Para **simplificar absurdamente**, intentemos relacionar los colores y el brillo con las funciones de las partes del ojo. Los **[bastones](https://es.wikipedia.org/wiki/Bast%C3%B3n_(c%C3%A9lula)) son principalmente responsables del brillo**, mientras que los **[conos](https://es.wikipedia.org/wiki/Cono_(c%C3%A9lula)) son responsables del color**. Hay tres tipos de conos, cada uno con un pigmento diferente, a saber: [Tipo S (azul), Tipo M (verde) y Tipo L (rojos)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> Dado que tenemos muchas más células de bastones (brillo) que células de conos (color), se puede inferir que somos más capaces de distinguir el contraste entre la luz y la oscuridad que los colores.
>
> 
>
> **Funciones de sensibilidad al contraste**
>
> Los investigadores de psicología experimental y muchas otras disciplinas han desarrollado varias teorías sobre la visión humana. Una de ellas se llama funciones de sensibilidad al contraste. Están relacionadas con el espacio y el tiempo de la luz y su valor se presenta en una luz inicial dada, cuánto cambio se requiere antes de que un observador informe que ha habido un cambio. Observa el plural de la palabra "función", esto se debe a que podemos medir las funciones de sensibilidad al contraste no solo con blanco y negro, sino también con colores. El resultado de estos experimentos muestra que en la mayoría de los casos nuestros ojos son más sensibles al brillo que al color.
Una vez que sabemos que somos más sensibles a la **luminancia** (*luma*, el brillo en una imagen), podemos aprovecharlo.
### Modelo de color
Primero aprendimos [cómo funcionan las imágenes en color](#terminología-básica) utilizando el **modelo RGB**, pero existen otros modelos también. De hecho, hay un modelo que separa la luminancia (brillo) de la crominancia (colores) y se conoce como **YCbCr***.
> * hay más modelos que hacen la misma separación.
Este modelo de color utiliza **Y** para representar el brillo y dos canales de color, **Cb** (croma azul) y **Cr** (croma rojo). El [YCbCr](https://es.wikipedia.org/wiki/YCbCr) se puede derivar a partir de RGB y también se puede convertir de nuevo a RGB. Utilizando este modelo, podemos crear imágenes a todo color, como podemos ver a continuación.
### Conversiones entre YCbCr y RGB
Algunos pueden preguntarse, ¿cómo podemos producir **todos los colores sin usar el verde**?
Para responder a esta pregunta, vamos a realizar una conversión de RGB a YCbCr. Utilizaremos los coeficientes del **[estándar BT.601](https://en.wikipedia.org/wiki/Rec._601)** que fue recomendado por el **[grupo ITU-R*](https://en.wikipedia.org/wiki/ITU-R)**. El primer paso es **calcular la luminancia**, utilizaremos las constantes sugeridas por la ITU y sustituiremos los valores RGB.
```
Y = 0.299R + 0.587G + 0.114B
```
Una vez obtenida la luminancia, podemos **separar los colores** (croma azul y croma rojo):
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
Y también podemos **covertirlo de vuelta** e incluse obtener el **verde utilizando YCbCr**.
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * Los grupos y estándares son comunes en el vídeo digital y suelen definir cuáles son los estándares, por ejemplo, [¿qué es 4K? ¿qué FPS debemos usar? ¿resolución? ¿modelo de color?](https://en.wikipedia.org/wiki/Rec._2020)
Generalmente, las **pantallas** (monitores, televisores, etc.) utilizan **solo el modelo RGB**, organizado de diferentes maneras, como se muestra a continuación:
### Chroma subsampling
Con la imagen representada en componentes de luminancia y crominancia, podemos aprovechar la mayor sensibilidad del sistema visual humano a la resolución de luminancia en lugar de la crominancia para eliminar selectivamente información. El **chroma subsampling** es la técnica de codificar imágenes utilizando **menor resolución para la crominancia que para la luminancia**.
¿Cuánto debemos reducir la resolución de la crominancia? Resulta que ya existen algunos esquemas que describen cómo manejar la resolución y la combinación (`color final = Y + Cb + Cr`).
Estos esquemas se conocen como sistemas de *subsampling* y se expresan como una relación de 3 partes: `a:x:y`, que define la resolución de la crominancia en relación con un bloque `a x 2` de píxeles de luminancia.
* `a` es la referencia de muestreo horizontal (generalmente 4).
* `x` es el número de muestras de crominancia en la primera fila de `a` píxeles (resolución horizontal en relación con `a`).
* `y` es el número de cambios de muestras de crominancia entre la primera y segunda filas de a píxeles.
> Una excepción a esto es el 4:1:0, que proporciona una sola muestra de crominancia dentro de cada bloque de `4 x 4` píxeles de resolución de luminancia.
Los esquemas comunes utilizados en códecs modernos son: **4:4:4** *(sin subsampling)*, **4:2:2, 4:1:1, 4:2:0, 4:1:0 y 3:1:1**.
> Puedes seguir algunas discusiones para [aprender más sobre el Chroma Subsampling](https://github.com/leandromoreira/digital_video_introduction/issues?q=YCbCr).
> **Fusión YCbCr 4:2:0**
>
> Aquí tienes una parte fusionada de una imagen utilizando YCbCr 4:2:0, observa que solo utilizamos 12 bits por píxel.
>
> 
Puedes ver la misma imagen codificada por los principales tipos de *chroma subsampling*, las imágenes en la primera fila son las YCbCr finales, mientras que la última fila de imágenes muestra la resolución de crominancia. Es realmente una gran ganancia con una pérdida tan pequeña.
Anteriormente habíamos calculado que necesitábamos [278 GB de almacenamiento para mantener un archivo de video con una hora de resolución de 720p y 30 fps](#eliminación-de-redundancias). Si usamos **YCbCr 4:2:0**, podemos reducir **este tamaño a la mitad (139 GB)***, pero aún está lejos del ideal.
> * encontramos este valor multiplicando el ancho, alto, bits por píxel y fps. Anteriormente necesitábamos 24 bits, ahora solo necesitamos 12.
> ### Práctica: Observemos el histograma YCbCr
> Puedes [observar el histograma YCbCr con ffmpeg](/encoding_pratical_examples.md#generates-yuv-histogram). Esta excena tiene una mayor contribución de azul la cual se muestra en el [histograma](https://es.wikipedia.org/wiki/Histograma).
>
> 
### Color, luma, luminancia, gamma
Mira este increíble video que explica qué es la luma y aprende sobre luminancia, gamma y color.
**Vídeo en Inglés**
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
> ### Práctica: Observa la intensidad YCbCr
> Puedes visualizar la intensidad Y (luma) para una línea específica de un vídeo utilizando el [filtro de osciloscopio de FFmpeg](https://ffmpeg.org/ffmpeg-filters.html#oscilloscope).
> ```bash
> ffplay -f lavfi -i 'testsrc2=size=1280x720:rate=30000/1001,format=yuv420p' -vf oscilloscope=x=0.5:y=200/720:s=1:c=1
> ```
> 
## Tipos de fotogramas
Ahora podemos continuar y tratar de eliminar la **redundancia en el tiempo**, pero antes de hacerlo, establezcamos algunas terminologías básicas. Supongamos que tenemos una película con 30 fps. Aquí están sus primeros 4 fotogramas.
  
Podemos ver **muchas repeticiones** dentro de los fotogramas, como **el fondo azul**, que no cambia del fotograma 0 al fotograma 3. Para abordar este problema, podemos **categorizarlos abstractamente** en tres tipos de fotogramas.
### I Frame (intra, keyframe)
Un *I-frame* (*reference*, *keyframe*, *intra*, en español fotograma o cuadro de referencia) es un **fotograma autónomo**. No depende de nada para ser renderizado, un *I-frame* se parece a una fotografía estática. Por lo general, el primer fotograma es un *I-frame*, pero veremos *I-frames* insertados regularmente entre otros tipos de fotogramas.
### P Frame (predicted)
Un *P-frame* (en español, fotograma o cuadro predictivo) aprovecha el hecho de que casi siempre l**a imagen actual se puede renderizar utilizando el fotograma anterior**. Por ejemplo, en el segundo fotograma, el único cambio fue que la pelota se movió hacia adelante. Podemos **reconstruir el fotograma 1, utilizando solo la diferencia y haciendo referencia al fotograma anterior**.
 <- 
> #### Práctica: Un vídeo con un solo I-frame
> Dado que un fotograma P utiliza menos datos, ¿por qué no podemos codificar un [video entero con un solo *I-frame* y todos los demás siendo *P-frames*?](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)
>
> Después de codificar este vídeo, comienza a verlo y **busca una parte avanzada** del vídeo; notarás que **toma algo de tiempo** llegar realmente a esa parte. Esto se debe a que un ***P-frame* necesita un fotograma de referencia** (como un *I-frame*, por ejemplo) para renderizarse.
>
> Otra prueba rápida que puedes hacer es codificar un vídeo utilizando un solo *I-frame* y luego [codificarlo insertando un *I-frame* cada 2 segundos](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second) y **comprobar el tamaño de cada versión**.
### B Frame (bi-predictive)
¿Qué pasa si hacemos referencia a los fotogramas pasados y futuros para proporcionar una compresión aún mejor? Eso es básicamente lo que hace un *B-frame*.
 <-  -> 
> #### Práctica: Compara vídeos con B-frame
> Puedes generar dos versiones, una con *B-frames* y otra sin ningún [*B-frame* en absoluto](/encoding_pratical_examples.md#no-b-frames-at-all) y verificar el tamaño del archivo y la calidad.
### Resumen
Estos tipos de fotogramas se utilizan para **proporcionar una mejor compresión**. Veremos cómo sucede esto en la próxima sección, pero por ahora podemos pensar en un ***I-frame* como costoso, un *P-frame* como más económico y el más económico es el *B-frame*.**
## Redundancia temporal (inter prediction)
Vamos a explorar las opciones que tenemos para reducir las **repeticiones en el tiempo**, este tipo de redundancia se puede resolver con técnicas de **interpredicción**.
Intentaremos **utilizar menos bits** para codificar la secuencia de los fotogramas 0 y 1.
Una cosa que podemos hacer es una resta, simplemente **restamos el fotograma 1 del fotograma 0** y obtenemos lo que necesitamos para **codificar el residual**.
Pero, ¿qué pasa si te digo que hay un **método mejor** que utiliza incluso menos bits? Primero, tratemos el `fotograma 0` como una colección de particiones bien definidas y luego intentaremos emparejar los bloques del `fotograma 0` en el `fotograma 1`. Podemos pensar en ello como una **estimación de movimiento**.
> ### Wikipedia - compensación de movimiento por bloques
> "La **compensación de movimiento por bloques** divide el fotograma actual en bloques no superpuestos, y el vector de compensación de movimiento **indica de dónde provienen esos bloques** (una idea errónea común es que el fotograma anterior se divide en bloques no superpuestos y los vectores de compensación de movimiento indican hacia dónde se mueven esos bloques). Los bloques de origen suelen superponerse en el fotograma fuente. Algunos algoritmos de compresión de vídeo ensamblan el fotograma actual a partir de piezas de varios fotogramas previamente transmitidos."
Podríamos estimar que la pelota se movió de `x=0, y=25` a `x=6, y=26`, los valores de **x** e **y** son los **vectores de movimiento**. Un **paso adicional** que podemos dar para ahorrar bits es **codificar solo la diferencia del vector de movimiento** entre la última posición del bloque y la predicción, por lo que el vector de movimiento final sería `x=6 (6-0), y=1 (26-25)`.
> En una situación del mundo real, esta **pelota se dividiría en n particiones**, pero el proceso es el mismo.
Los objetos en el fotograma se **mueven de manera tridimensional**, la pelota puede volverse más pequeña cuando se mueve al fondo. Es normal que no encontremos una coincidencia perfecta con el bloque que intentamos encontrar. Aquí hay una vista superpuesta de nuestra estimación frente a la imagen real.
Pero podemos ver que cuando aplicamos la **estimación de movimiento**, los **datos a codificar son más pequeños** que cuando simplemente usamos técnicas de fotograma delta.
> ### Cómo se vería la compensación de movimiento real
> Esta técnica se aplica a todos los bloques, muy a menudo una pelota se dividiría en más de un bloque.
> 
> Fuente: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
Puedes [experimentar con estos conceptos utilizando jupyter](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Práctica: Observa los vectores de movimiento
> Podemos [generar un vídeo con la interpredicción (vectores de movimiento) con ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> O podemos usar el [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que es de pago, pero hay una versión de prueba gratuita que te limita a trabajar solo con los primeros 10 fotogramas).
>
> 
## Redundancia Espacial (intra prediction)
Si analizamos **cada fotograma** en un vídeo, veremos que también hay **muchas áreas que están correlacionadas**.
Vamos a través de un ejemplo. Esta escena está compuesta principalmente por colores azules y blancos.
Este es un `I-frame` y **no podemos usar fotogramas anteriores** para hacer una predicción, pero aún podemos comprimirlo. Codificaremos la selección de bloques en rojo. Si **observamos a sus vecinos**, podemos **estimar** que hay una **tendencia de colores a su alrededor**.
Vamos a **predecir** que el fotograma continuará **extendiendo los colores verticalmente**, lo que significa que los colores de los **píxeles desconocidos mantendrán los valores de sus vecinos**.
Nuestra **predicción puede ser incorrecta**, por esa razón necesitamos aplicar esta técnica (**intra prediction**) y luego **restar los valores reales**, lo que nos da el bloque residual, lo que resulta en una matriz mucho más compresible en comparación con la original.
Existen muchos tipos diferentes de este tipo de predicción. La que se muestra aquí es una forma de predicción plana recta, donde los píxeles de la fila superior del bloque se copian fila por fila dentro del bloque. La predicción plana también puede tener una componente angular, donde se utilizan píxeles tanto de la izquierda como de la parte superior para ayudar a predecir el bloque actual. Y también existe la predicción DC, que implica tomar el promedio de las muestras justo arriba y a la izquierda del bloque.
> #### Práctica: Observa las intra predictions
> Puedes [generar con ffmpeg un vídeo con macrobloques y sus predicciones.](/encoding_pratical_examples.md#generate-debug-video) Por favor verifica la documentación de ffmpeg para comprender el [significado de cada bloque de color](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes).
>
> 
>
> O puedes usar [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que es de pago, pero hay una versión de prueba gratuita que te limita a trabajar solo con los primeros 10 fotogramas).
>
> 
# ¿Cómo funciona un códec de vídeo?
## ¿Qué? ¿Por qué? ¿Cómo?
**¿Qué?** Es un software o hardware que comprime o descomprime vídeo digital. **¿Por qué?** El mercado y la sociedad demandan vídeos de mayor calidad con ancho de banda o almacenamiento limitados. ¿Recuerdas cuando [calculamos el ancho de banda necesario](#terminología-básica) para 30 fps, 24 bits por píxel, resolución de un vídeo de 480x240? Fueron **82.944 Mbps** sin aplicar compresión. Es la única forma de entregar HD/FullHD/4K en televisores e Internet. **¿Cómo?** Echaremos un vistazo breve a las técnicas principales aquí.
> **CODEC vs Contenedor**
>
> Un error común que cometen los principiantes es confundir el CODEC de vídeo digital y el [contenedor de vídeo digital](https://en.wikipedia.org/wiki/Container_format). Podemos pensar en los **contenedores** como un formato que contiene metadatos del vídeo (y posiblemente también audio), y el **vídeo comprimido** se puede ver como su contenido.
>
> Por lo general, la extensión de un archivo de vídeo define su contenedor de vídeo. Por ejemplo, el archivo `video.mp4` probablemente es un contenedor **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MP4_file_format)** y un archivo llamado `video.mkv` probablemente es un **[matroska](https://en.wikipedia.org/wiki/Matroska)**. Para estar completamente seguro sobre el códec y el formato del contenedor, podemos usar [ffmpeg o mediainfo](/encoding_pratical_examples.md#inspect-stream).
## Historia
Antes de adentrarnos en el funcionamiento interno de un códec genérico, echemos un vistazo atrás para comprender un poco mejor algunos códecs de vídeo antiguos.
El códec de vídeo [H.261](https://en.wikipedia.org/wiki/H.261) nació en 1990 (técnicamente en 1988) y fue diseñado para trabajar con **tasas de datos de 64 kbit/s**. Ya utilizaba ideas como el *chroma subsampling*, el macrobloque, etc. En el año 1995, se publicó el estándar de códec de vídeo **H.263** y continuó siendo extendido hasta 2001.
En 2003 se completó la primera versión de **H.264/AVC**. En el mismo año, **On2 Technologies** (anteriormente conocida como Duck Corporation) lanzó su códec de vídeo como una compresión de vídeo con pérdida **libre de regalías** llamada **VP3**. En 2008, **Google compró** esta empresa y lanzó **VP8** en el mismo año. En diciembre de 2012, Google lanzó **VP9**, el cual es **compatible con aproximadamente el ¾ del mercado de navegadores** (incluyendo móviles).
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)** es un nuevo códec de vídeo **libre de regalías** y de código abierto que está siendo diseñado por la [Alliance for Open Media (AOMedia)](http://aomedia.org/), la cual está compuesta por **empresas como Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel y Cisco**, entre otras. La **primera versión**, 0.1.0 del códec de referencia, **se publicó el 7 de abril de 2016**.
> #### El nacimiento de AV1
>
> A principios de 2015, Google estaba trabajando en [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1), Xiph (Mozilla) estaba trabajando en [Daala](https://xiph.org/daala/) y Cisco lanzó su códec de video libre de regalías llamado [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03).
>
> Entonces, MPEG LA anunció por primera vez límites anuales para HEVC (H.265) y tarifas 8 veces más altas que H.264, pero pronto cambiaron las reglas nuevamente:
> * **sin límite anual**,
> * **tarifa por contenido** (0.5% de los ingresos) y
> * **tarifas por unidad aproximadamente 10 veces más altas que H.264**.
>
> La [Alliance for Open Media](http://aomedia.org/about/) fue creada por empresas de fabricantes de hardware (Intel, AMD, ARM, Nvidia, Cisco), distribución de contenido (Google, Netflix, Amazon), mantenimiento de navegadores (Google, Mozilla) y otros.
>
> Las empresas tenían un objetivo común, un códec de vídeo libre de regalías, y así nació AV1 con una [licencia de patente mucho más simple](http://aomedia.org/license/patent/). **Timothy B. Terriberry** hizo una excelente presentación, que es la fuente de esta sección, sobre la [concepción de AV1, el modelo de licencia y su estado actual](https://www.youtube.com/watch?v=lzPaldsmJbk).
>
> Te sorprenderá saber que puedes **analizar el códec AV1 a través de tu navegador**, visita https://arewecompressedyet.com/analyzer/
>
> 
>
> PD: Si deseas aprender más sobre la historia de los códecs, debes comprender los conceptos básicos detrás de las [patentes de compresión de vídeo](https://www.vcodex.com/video-compression-patents/).
## Un códec genérico
Vamos a presentar los **mecanismos principales detrás de un códec de vídeo genérico**, pero la mayoría de estos conceptos son útiles y se utilizan en códecs modernos como VP9, AV1 y HEVC. Asegúrate de entender que vamos a simplificar las cosas MUCHO. A veces usaremos un ejemplo real (principalmente H.264) para demostrar una técnica.
## Paso 1 - Particionado de imágenes
El primer paso es **dividir el fotograma** en varias **particiones, sub-particiones** y más allá.
Pero, **¿por qué?**. Hay muchas razones, por ejemplo, cuando dividimos la imagen, podemos trabajar las predicciones de manera más precisa, utilizando pequeñas particiones para las partes móviles y particiones más grandes para un fondo estático.
Por lo general, los códecs **organizan estas particiones** en *slices* (o *tiles*), macrobloques (o unidades de árbol de codificación) y muchas sub-particiones. El tamaño máximo de estas particiones varía, HEVC establece 64x64 mientras que AVC usa 16x16, pero las sub-particiones pueden alcanzar tamaños de 4x4.
¿Recuerdas que aprendimos cómo se **clasifican los fotogramas**? Bueno, también puedes **aplicar esas ideas a los bloques**, por lo tanto, podemos tener *I-Slice*, *B-Slice*, *I-Macroblock*, etc.
> ### Práctica: Verificar particiones
> Podemos usar [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que es de pago, pero hay una versión de prueba gratuita que limita el trabajo a los primeros 10 fotogramas). Aquí se analizan las [particiones de VP90](/encoding_pratical_examples.md#transcoding).
>
> 
## Paso 2 - Predicciones
Una vez que tenemos las particiones, podemos hacer predicciones sobre ellas. Para la [inter prediction](#redundancia-temporal-inter-prediction), necesitamos **enviar los vectores de movimiento y el residual**, y para la [intra prediction](#redundancia-espacial-intra-prediction), **enviaremos la dirección de predicción y el residual** también.
## Paso 3 - Transformación
Después de obtener el bloque residual (`partición predicha - partición real`), podemos **transformarlo** de tal manera que nos permita saber cuáles **píxeles podemos descartar** mientras mantenemos la **calidad general**. Hay algunas transformaciones para este comportamiento específico.
Aunque existen [otras transformaciones](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms), examinaremos más de cerca la transformada coseno discreta (DCT). Las principales características de la [**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform) son las siguientes:
* **convierte** bloques de **píxeles** en bloques del mismo tamaño de los **coeficientes de frecuencia**.
* **compacta** la energía, lo que facilita eliminar la redundancia espacial.
* es **reversible**, es decir, se puede revertir a píxeles.
> El 2 de febrero de 2017, Cintra, R. J. y Bayer, F. M publicaron su artículo [DCT-like Transform for Image Compression Requires 14 Additions Only](https://arxiv.org/abs/1702.00817).
No te preocupes si no comprendiste los beneficios de cada punto, intentaremos realizar algunos experimentos para ver el valor real de esto.
Tomemos el siguiente **bloque de píxeles** (8x8):
Lo cual se representa en la siguiente imagen de bloque (8x8):
Cuando **aplicamos la DCT** a este bloque de píxeles, obtenemos el **bloque de coeficientes** (8x8):
Y si representamos este bloque de coeficientes, obtendremos esta imagen:
Como puedes ver, no se parece en nada a la imagen original, podríamos notar que el **primer coeficiente** es muy diferente de todos los demás. Este primer coeficiente se conoce como el coeficiente DC, que representa **todas las muestras** en el array de entrada, algo **similar a un promedio**.
Este bloque de coeficientes tiene una propiedad interesante, que es que separa los componentes de alta frecuencia de los de baja frecuencia.
En una imagen, la **mayor parte de la energía** se concentrará en las [**frecuencias más bajas**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm), por lo que si transformamos una imagen en sus componentes de frecuencia y **eliminamos los coeficientes de frecuencia más altos**, podemos r**educir la cantidad de datos necesarios** para describir la imagen sin sacrificar demasiada calidad de imagen.
> La frecuencia significa cuán rápido cambia una señal.
Intentemos aplicar el conocimiento que adquirimos en la prueba, convirtiendo la imagen original a su dominio de frecuencia (bloque de coeficientes) usando DCT y luego descartando parte (67%) de los coeficientes menos importantes, principalmente la parte inferior derecha.
Primero, lo convertimos a su **dominio de frecuencia**.
A continuación, descartamos parte (67%) de los coeficientes, principalmente la parte inferior derecha.
Finalmente, reconstruimos la imagen a partir de este bloque de coeficientes descartados (recuerda, debe ser reversible) y la comparamos con la original.
Como podemos ver, se asemeja a la imagen original pero introduce muchas diferencias con respecto a la original. **Descartamos el 67.1875%** y aún así pudimos obtener algo similar a la original. Podríamos descartar de manera más inteligente los coeficientes para tener una mejor calidad de imagen, pero eso es el próximo tema.
> **Cada coeficiente se forma utilizando todos los píxeles**
>
> Es importante destacar que cada coeficiente no se asigna directamente a un solo píxel, sino que es una suma ponderada de todos los píxeles. Este gráfico asombroso muestra cómo se calcula el primer y segundo coeficiente, utilizando pesos únicos para cada índice.
>
> 
>
> Fuente: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> También puedes intentar [visualizar la DCT mirando una imagen simple](/dct_better_explained.ipynb) formada sobre la base de la DCT. Por ejemplo, aquí tienes cómo [se forma el carácter A](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT) utilizando el peso de cada coeficiente.
>
> 
> ### Práctica: Descartar diferentes coeficientes
> Puedes jugar con la [transformación DCT](/uniform_quantization_experience.ipynb).
## Paso 4 - Cuantización
Cuando descartamos algunos de los coeficientes en el último paso (transformación), de alguna manera hicimos una forma de cuantización. En este paso es donde elegimos perder información (la **parte perdida**) o, en términos simples, **cuantificamos coeficientes para lograr la compresión**.
¿Cómo podemos cuantificar un bloque de coeficientes? Un método simple sería una cuantificación uniforme, donde tomamos un bloque, lo **dividimos por un valor único** (10) y redondeamos este valor.
¿Cómo podemos **revertir** (re-cuantificar) este bloque de coeficientes? Podemos hacerlo **multiplicando el mismo valor** (10) por el que lo dividimos inicialmente.
Este **enfoque no es el mejor** porque no tiene en cuenta la importancia de cada coeficiente. Podríamos usar una **matriz de cuantizadores** en lugar de un solo valor. Esta matriz puede explotar la propiedad de la DCT, cuantificando más los coeficientes de la parte inferior derecha y menos los de la parte superior izquierda. El [JPEG utiliza un enfoque similar](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html); puedes consultar el [código fuente para ver esta matriz](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40).
> ### Práctica: Cuantización
> Puedes experimentar con la [cuantización aquí](/dct_experiences.ipynb).
## Paso 5 - Codificación de la entropía
Después de cuantificar los datos (bloques/slices/fotogramas de imágenes), aún podemos comprimirlos de manera sin pérdida. Hay muchas formas (algoritmos) de comprimir datos. Vamos a experimentar brevemente con algunos de ellos. Para una comprensión más profunda, puedes leer el increíble libro [Understanding Compression: Data Compression for Modern Developers](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/).
### Codificación VLC
Supongamos que tenemos una secuencia de símbolos: **a**, **e**, **r** y **t**; y su probabilidad (de 0 a 1) se representa en esta tabla.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probabilidad| 0.3 | 0.3 | 0.2 | 0.2 |
Podemos asignar códigos binarios únicos (preferiblemente pequeños) a los más probables y códigos más grandes a los menos probables.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probabilidad | 0.3 | 0.3 | 0.2 | 0.2 |
| código binario | 0 | 10 | 110 | 1110 |
Comprimamos la secuencia **eat**, asumiendo que gastaríamos 8 bits para cada símbolo, gastaríamos **24 bits** sin ninguna compresión. Pero en caso de que reemplacemos cada símbolo por su código, podemos ahorrar espacio.
El primer paso es codificar el símbolo **e**, que es `10`, y el segundo símbolo es **a**, que se agrega (no en un sentido matemático) como `[10][0]`, y finalmente el tercer símbolo **t**, lo que hace que nuestra secuencia de bits comprimida final sea `[10][0][1110]` o `1001110`, que solo requiere **7 bits** (3.4 veces menos espacio que el original).
Observa que cada código debe ser un código único y prefijado [Huffman puede ayudarte a encontrar estos números](https://en.wikipedia.org/wiki/Huffman_coding). Aunque tiene algunos problemas, todavía hay [códecs de vídeo que ofrecen este método](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding) y es el algoritmo para muchas aplicaciones que requieren compresión.
Tanto el codificador como el decodificador **deben conocer** la tabla de símbolos con sus códigos, por lo tanto, también debes enviar la tabla.
### Codificación aritmética
Supongamos que tenemos una secuencia de símbolos: **a**, **e**, **r**, **s** y **t**; y su probabilidad se representa en esta tabla.
| | a | e | r | s | t |
|--------------|-----|-----|------|------|-----|
| probabilidad | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
Con esta tabla en mente, podemos construir rangos que contengan todos los posibles símbolos ordenados por los más frecuentes.
Ahora codifiquemos la secuencia **eat**, tomamos el primer símbolo **e**, que se encuentra dentro del subrango **0.3 a 0.6** (pero no se incluye), y tomamos este subrango y lo dividimos nuevamente utilizando las mismas proporciones utilizadas anteriormente, pero dentro de este nuevo rango.
Continuemos codificando nuestra secuencia **eat**, ahora tomamos el segundo símbolo **a**, que está dentro del nuevo subrango **0.3 a 0.39**, y luego tomamos nuestro último símbolo **t** y hacemos el mismo proceso nuevamente y obtenemos el último subrango **0.354 a 0.372**.
Solo necesitamos elegir un número dentro del último subrango **0.354 a 0.372**, elijamos **0.36**, pero podríamos elegir cualquier número dentro de este subrango. Con **solo** este número podremos recuperar nuestra secuencia original **eat**. Si lo piensas, es como si estuviéramos dibujando una línea dentro de rangos de rangos para codificar nuestra secuencia.
El **proceso inverso** (también conocido como decodificación) es igualmente sencillo, con nuestro número **0.36** y nuestro rango original, podemos realizar el mismo proceso pero ahora usando este número para revelar la secuencia codificada original detrás de este número.
Con el primer rango, notamos que nuestro número encaja en la porción, por lo tanto, es nuestro primer símbolo, ahora dividimos este subrango nuevamente, haciendo el mismo proceso que antes, y notamos que **0.36** encaja en el símbolo **a**, y después de repetir el proceso llegamos al último símbolo **t** (formando nuestra secuencia original codificada *eat*).
Tanto el codificador como el decodificador **tienen que conocer** la tabla de probabilidades de los símbolos, por lo tanto, también debes enviar la tabla.
¿Bastante ingenioso, verdad? Las personas son realmente inteligentes para idear una solución así. Algunos [códecs de vídeo utilizan esta técnica](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding) (o al menos la ofrecen como opción).
La idea es comprimir sin pérdidas el flujo de bits cuantificados. Sin duda, este artículo carece de muchos detalles, razones, compensaciones, etc. Pero [puedes aprender más](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/). Los códecs más nuevos están tratando de utilizar diferentes[ algoritmos de codificación de entropía como ANS.](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)
> ### Práctica: CABAC vs CAVLC
> Puedes [generar 2 streams, uno con CABAC y otro con CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc) y **compara cuánto tiempo** toma generar cada uno de ellos, como así también el **tamaño final**.
## Paso 6 - formato *bitstream*
Después de completar todos estos pasos, necesitamos **empaquetar los fotogramas comprimidos y el contexto de estos pasos**. Debemos informar explícitamente al decodificador sobre **las decisiones tomadas por el codificador**, como el bit depth, el espacio de color, la resolución, la información de predicciones (vectores de movimiento, dirección de intra prediction), perfil, nivel, fps, tipo de fotograma, número de fotograma y mucho más.
Vamos a estudiar superficialmente el *bitstream* de H.264. Nuestro primer paso es [generar un *bitstream* H.264* mínimo](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream), lo podemos hacer utilizando nuestro propio repositorio y [ffmpeg](http://ffmpeg.org/).
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * fmpeg agrega, por defecto, todos los parámetros de codificación como un **SEI NAL**, pronto definiremos qué es un NAL.
Este comando generará un *bitstream* H.264 en bruto con un **único fotograma**, de 64x64 píxeles, con espacio de color yuv420, utilizando la siguiente imagen como fotograma.
> 
### H.264 bitstream
El estándar AVC (H.264) define que la información se enviará en **macro frames** (en el sentido de red), llamadas **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (Network Abstraction Layer). El objetivo principal de la NAL es proporcionar una representación de vídeo "amigable para la red". Este estándar debe funcionar en televisores (basados en transmisión) y en Internet (basado en paquetes), entre otros.
Hay un **[marcador de sincronización](https://en.wikipedia.org/wiki/Frame_synchronization)** para definir los límites de las unidades NAL. Cada marcador de sincronización tiene un valor de `0x00 0x00 0x01`, excepto el primero, que es `0x00 0x00 0x00 0x01`. Si ejecutamos el comando **hexdump** en el flujo de bits H.264 generado, podemos identificar al menos tres NAL al principio del archivo.
Como mencionamos antes, el decodificador necesita conocer no solo los datos de la imagen, sino también los detalles del vídeo, el fotograma, los colores, los parámetros utilizados y otros. El primer byte de cada NAL define su categoría y **tipo**.
| NAL type id | Description |
|--- |---|
| 0 | Undefined |
| 1 | Coded slice of a non-IDR picture |
| 2 | Coded slice data partition A |
| 3 | Coded slice data partition B |
| 4 | Coded slice data partition C |
| 5 | **IDR** Coded slice of an IDR picture |
| 6 | **SEI** Supplemental enhancement information |
| 7 | **SPS** Sequence parameter set |
| 8 | **PPS** Picture parameter set |
| 9 | Access unit delimiter |
| 10 | End of sequence |
| 11 | End of stream |
| ... | ... |
Normalmente, el primer NAL de un flujo de bits es un **SPS** (Sequence Parameter Set). Este tipo de NAL es responsable de proporcionar información sobre las variables de codificación generales como **perfil**, **nivel**, **resolución** y otros.
Si omitimos el primer marcador de sincronización, podemos decodificar el **primer byte** para saber qué **tipo de NAL** es el primero.
Por ejemplo, el primer byte después del marcador de sincronización es `01100111`, donde el primer bit (`0`) es el campo **forbidden_zero_bit**, los siguientes 2 bits (`11`) nos indican el campo **nal_ref_idc**, que indica si este NAL es un campo de referencia o no, y los siguientes 5 bits (`00111`) nos informan sobre el campo **nal_unit_type**, en este caso, es un NAL de tipo **SPS** (7).
El segundo byte (`binary=01100100, hex=0x64, dec=100`) de un NAL SPS es el campo **profile_idc**, que muestra el perfil que el codificador ha utilizado. En este caso, hemos utilizado el **[high profile](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)**. Además, el tercer byte contiene varias banderas que determinan el perfil exacto (como restringido o progresivo). Pero en nuestro caso, el tercer byte es 0x00 y, por lo tanto, el codificador ha utilizado solo el *high profile*.
Cuando leemos la especificación de flujo de bits H.264 para un NAL SPS, encontraremos muchos valores para el **parameter name**, **category** y **description**. Por ejemplo, veamos los campos `pic_width_in_mbs_minus_1` y `pic_height_in_map_units_minus_1`.
| Parameter name | Category | Description |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: unsigned integer [Exp-Golomb-coded](https://ghostarchive.org/archive/JBwdI)
Si realizamos algunos cálculos con el valor de estos campos, obtendremos la **resolución**. Podemos representar `1920 x 1080` usando un valor de `pic_width_in_mbs_minus_1` de `119 ((119 + 1) * macroblock_size = 120 * 16 = 1920)`, nuevamente ahorrando espacio, en lugar de codificar `1920`, lo hicimos con `119`.
Si continuamos examinando nuestro vídeo creado con una vista binaria (por ejemplo, `xxd -b -c 11 v/minimal_yuv420.h264`), podemos saltar al último NAL que es el propio cuadro.
Podemos ver los valores de sus primeros 6 bytes: `01100101 10001000 10000100 00000000 00100001 11111111`. Como ya sabemos, el primer byte nos dice qué tipo de NAL es, en este caso, (`00101`) es un **IDR Slice (5)** y podemos inspeccionarlo más a fondo:
Utilizando la información de la especificación, podemos decodificar qué tipo de slice (**slice_type**), el número de fotograma (**frame_num**) y otros campos importantes.
Para obtener los valores de algunos campos (`ue(v), me(v), se(v) o te(v)`), debemos decodificarlos utilizando un decodificador especial llamado [Exponential-Golomb](https://ghostarchive.org/archive/JBwdI). Este método es **muy eficiente para codificar valores variables**, sobre todo cuando hay muchos valores predeterminados.
> Los valores de **slice_type** y **frame_num** de este vídeo son 7 (I slice) y 0 (el primer fotograma).
Podemos ver el ***bitstream* como un protocolo**, y si deseas aprender más sobre este *bitstream*, consulta la especificación [ITU H.264]( http://www.itu.int/rec/T-REC-H.264-201610-I). Aquí tienes un diagrama macro que muestra dónde reside los datos de la imagen (YUV comprimido).
Podemos explorar otros *bitstreams* como el [*bitstreams* VP9](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf) o incluso nuestro **nuevo mejor amigo** el [*bitstream* AV1](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8), [¿se ven todos similares, no?](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/) Pero una vez que aprendes uno, puedes entender fácilmente los demás.
> ### Práctica: Inspeccionar el *bitstream* H.264
> Podemos [generar un video de un solo fotograma](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) y usar [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) para inspeccionar su *bitstream* H.264. De hecho, incluso puedes ver el [código fuente que analiza el *bitstream* h264 (AVC)](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp).
>
> 
>
> También podemos utilizar el [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) que es de pago, pero hay una versión de prueba gratuita que limita el trabajo a solo los primeros 10 fotogramas, lo cual está bien para fines de aprendizaje.
>
> 
## Repaso
Es evidente que muchos de los **códecs modernos utilizan el mismo modelo que aprendimos**. De hecho, echemos un vistazo al diagrama de bloques del códec de vídeo Thor, que contiene todos los pasos que estudiamos. La idea es que ahora deberías ser capaz de al menos comprender mejor las innovaciones y los documentos de esta área.
Anteriormente calculamos que necesitaríamos [139GB de almacenamiento para mantener un archivo de vídeo de una hora a una resolución de 720p y 30 fps](#chroma-subsampling) si utilizamos las técnicas que aprendimos aquí, como **inter e intra predictions, transformación, cuantización, codificación de entropía y otras**. Podemos lograrlo, asumiendo que estamos utilizando **0.031 bit por píxel**, el mismo vídeo de calidad perceptible **requiriendo solo 367.82MB en lugar de 139GB** de almacenamiento.
> Elegimos usar **0.031 bit por píxel** basándonos en el ejemplo de vídeo proporcionado aquí.
## ¿Cómo logra H.265 una mejor relación de compresión que H.264?
Ahora que sabemos más sobre cómo funcionan los códecs, es fácil entender cómo los nuevos códecs pueden ofrecer mayores resoluciones con menos bits.
Compararemos AVC y HEVC, ten en cuenta que casi siempre hay un equilibrio entre más ciclos de CPU (complejidad) y la velocidad de compresión.
HEVC tiene más opciones de **particiones** (y **sub-particiones**) que AVC, más **direcciones/ángulos de intra predictions**, **codificación de entropía mejorada** y más, todas estas mejoras hicieron que H.265 fuera capaz de comprimir un 50% más que H.264.
# Online streaming
## Arquitectura general
[TODO]
## Descarga progresiva y streaming adaptativo
[TODO]
## Protección de contenido
Podemos utilizar **un sistema de tokens simple** para proteger el contenido. El usuario sin un token intenta solicitar un video y la CDN (Red de Entrega de Contenido, del inglés *Content Delivery Network*) le prohíbe el acceso, mientras que un usuario con un token válido puede reproducir el contenido, funciona de manera bastante similar a la mayoría de los sistemas de autenticación web.
El uso exclusivo de este sistema de tokens todavía permite que un usuario descargue un video y lo distribuya. Es aquí dónde, los sistemas de **DRM (gestión de derechos digitales, del inglés *digital rights management*)** se pueden utilizar para tratar de evitar esto.
En los sistemas de producción del mundo real, a menudo se utilizan ambas técnicas para proporcionar autorización y autenticación.
### DRM
#### Soluciones principales
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### ¿Qué es?
DRM significa [*Digital Rights Management* o gestión de derechos digitales](https://sander.saares.eu/categories/drm-is-not-a-black-box/), es una forma de proporcionar protección de derechos de autor para medios digitales, como vídeo y audio digitales. Aunque se utiliza en muchos lugares, no es universalmente aceptado.
#### ¿Por qué?
Los creadores de contenido (principalmente estudios) desean proteger su propiedad intelectual contra la copia para prevenir la redistribución no autorizada de medios digitales.
#### ¿Cómo?
Vamos a describir una forma abstracta y genérica de DRM de manera muy simplificada.
Dado un **contenido C1** (por ejemplo, un vídeo en streaming HLS o DASH), con un **reproductor P1** (por ejemplo, shaka-clappr, exo-player o iOS) en un **dispositivo D1** (por ejemplo, un teléfono inteligente, una televisión, una tableta o una computadora de escritorio/portátil) utilizando un **sistema DRM llamado DRM1** (widevine, playready o FairPlay).
El contenido C1 está encriptado con una **clave simétrica K1** del sistema DRM1, generando el **contenido encriptado C'1**.
El reproductor P1, de un dispositivo D1, tiene dos claves (asimétricas), una **clave privada PRK1** (esta clave está protegida1 y solo la conoce **D1**) y una **clave pública PUK1**.
> 1Protegida: esta protección puede ser **mediante hardware**, por ejemplo, esta clave puede almacenarse dentro de un chip especial (de solo lectura) que funciona como [una caja negra](https://en.wikipedia.org/wiki/Black_box) para proporcionar la descifrado, o **por software** (menos seguro), el sistema DRM proporciona medios para saber qué tipo de protección tiene un dispositivo dado.
Cuando el **reproductor P1 quiere reproducir** el **contenido C'1**, debe interactuar con el **sistema DRM DRM1**, proporcionando su clave pública **PUK1**. El sistema DRM DRM1 devuelve la **clave K1 cifrada** con la clave pública del cliente **PUK1**. En teoría, esta respuesta es algo que **solo D1 es capaz de descifrar**.
`K1P1D1 = enc(K1, PUK1)`
**P1** utiliza su sistema local de DRM (puede ser un [SOC](https://en.wikipedia.org/wiki/System_on_a_chip), hardware especializado o software), este sistema es **capaz de descifrar** el contenido utilizando su clave privada PRK1, puede descifrar **la clave simétrica K1 de la K1P1D1** y **reproducir C'1**. En el mejor caso, las claves no se exponen a través de la RAM.
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# ¿Cómo usar jupyter?
Asegúrate de tener **docker instalado**, ejecuta `./s/start_jupyter.sh` y sigue las instrucciones en la consola.
# Conferencias
* [DEMUXED](https://demuxed.com/) - puedes [mirar las ediciones pasadas aquí.](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# Referencias
El contenido más rico se encuentra aquí, es de donde se extrajo, basó o inspiró toda la información que vimos en este texto. Puedes profundizar tu conocimiento con estos increíbles enlaces, libros, vídeos, etc.
Cursos online y tutoriales:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* https://wiki.multimedia.cx
* https://mahanstreamer.net
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Libros:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/High-Efficiency-Video-Coding-HEVC/dp/3319068946
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Material de *onboarding*:
* https://github.com/Eyevinn/streaming-onboarding
* https://howvideo.works/
* https://www.aws.training/Details/eLearning?id=17775
* https://www.aws.training/Details/eLearning?id=17887
* https://www.aws.training/Details/Video?id=24750
Especificaciones de *Bitstream*:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
Software:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://mediaarea.net/en/MediaInfo
* https://www.jongbel.com/
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
Códecs *Non-ITU*:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://ghostarchive.org/archive/0W0d8 (was: https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml)
* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
* https://jmvalin.ca/papers/AV1_tools.pdf
Conceptos de codificación:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
* https://videoblerg.wordpress.com/2017/11/10/ffmpeg-and-how-to-use-it-wrong/
Ejemplos de vídeos para pruebas:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
Miscelánea:
* https://github.com/Eyevinn/streaming-onboarding
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* https://web.archive.org/web/20100728070421/http://www.csc.villanova.edu/~rschumey/csc4800/dct.html (was: http://www.csc.villanova.edu/~rschumey/csc4800/dct.html)
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
* https://sander.saares.eu/categories/drm-is-not-a-black-box/
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# Introduzione
Una introduzione alla tecnologia del video digitale, destinata prevalentemente a ingegneri o sviluppatori software, ma sufficientemente **semplice per consentire a chiunque di imparare**. L'idea è nata durante un [workshop sulla tecnologia video per principianti](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p).
L'obiettivo è introdurre alcuni importanti concetti sul video digitale, usando un **vocabolario semplice, molte figure e esempi pratici** quando possibile, e rendere questa conoscenza disponibile ovunque. Non esitare a inviare correzioni o suggerimenti!
Ci saranno delle sezioni con esempi e esercizi che richiedono di avere Docker installato e una copia di questa repository scaricata in locale.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **ATTENZIONE**: quando vedi scritto un comando come `./s/ffmpeg` oppure `./s/mediainfo`, significa che si sta eseguendo quel programma all'interno di un container Docker, che contiene tutte le dipendenze richieste per funzionare.
**Tutti gli esempi pratici devono essere eseguiti dalla repository locale che hai scaricato**. Per gli esempi di jupyter, devi avviare il server eseguendo `./s/start_jupyter.sh`, e aprire in un browser l'URL mostrato.
# Indice
- [Introduzione](#introduzione)
- [Indice](#indice)
- [Terminologia base](#terminologia-di-base)
* [Altri modi per codificare un'immagine a colori](#altri-modi-per-codificare-unimmagine-a-colori)
* [Esercizio: sperimentare con le immagini e i colori](#esercizio-sperimentare-con-le-immagini-e-i-colori)
* [I DVD hanno un DAR 4:3](#i-dvd-hanno-un-dar-43)
* [Esercizio: controllare le proprietà di un file video](#esercizio-controllare-le-propriet%C3%A0-di-un-file-video)
- [Rimozione delle informazioni ridondanti](#rimozione-delle-informazioni-ridondanti)
* [I colori, la luminanza e i nostri occhi](#i-colori-la-luminanza-e-i-nostri-occhi)
+ [Modelli di colore](#modelli-di-colore)
+ [Conversione tra YCbCr e RGB](#conversione-tra-ycbcr-e-rgb)
+ [Sottocampionamento della crominanza](#sottocampionamento-della-crominanza)
+ [Esercizio: vedere l'istogramma YCbCr](#esercizio-vedere-listogramma-ycbcr)
* [Tipi di fotogramma](#tipi-di-fotogramma)
+ [I Frame (intra, keyframe)](#i-frame-intra-keyframe)
+ [P Frame (predicted)](#p-frame-predicted)
- [Esercizio: un video con un solo I-frame](#esercizio-un-video-con-un-solo-i-frame)
+ [B Frame (bi-predictive)](#b-frame-bi-predictive)
- [Esercizio: confronta video con e senza B-frame](#esercizio-confronta-video-con-e-senza-b-frame)
+ [Sommario](#sommario)
* [Ridondanza temporale (predizione inter-frame)](#ridondanza-temporale-predizione-inter-frame)
- [Esercizio: vedere i vettori di movimento](#esercizio-vedere-i-vettori-del-moto)
* [Ridondanza spaziale (predizione intra-frame)](#ridondanza-spaziale-predizione-intra-frame)
- [Esercizio: vedere la predizione intra-frame](#esercizio-vedere-la-predizione-intra-frame)
- [Come funziona un codec video?](#come-funziona-un-codec-video)
* [Cosa? Perché? Come?](#cosa-perch%C3%A9-come)
* [Storia](#storia)
+ [La nascita di AV1](#la-nascita-di-av1)
* [Un codec generico](#un-codec-generico)
* [1° passo - partizionamento dell'immagine](#1-passo---partizionamento-dellimmagine)
+ [Esercizio: vedere le partizioni](#esercizio-vedere-le-partizioni)
* [2° passo - predizioni](#2-passo---predizioni)
* [3° passo - trasformazione](#3-passo---trasformazione)
+ [Esercizio: scartare diversi coefficienti](#esercizio-scartare-diversi-coefficienti)
* [4° passo - quantizzazione](#4-passo---quantizzazione)
+ [Esercizio: quantizzazione](#esercizio-quantizzazione)
* [5° passo - codifica dell'entropia](#5-passo---codifica-dellentropia)
+ [Codifica VLC](#codifica-vlc)
+ [Codifica aritmetica](#codifica-aritmetica)
+ [Esercizio: CABAC vs CAVLC](#esercizio-cabac-vs-cavlc)
* [6° passo - formato bitstream](#6-passo---formato-bitstream)
+ [Bitstream di H.264](#bitstream-di-h264)
+ [Esercizio: ispezionare il bitstream H.264](#esercizio-ispezionare-il-bitstream-h264)
* [Ripasso](#ripasso)
* [Come fa H.265 a comprimere più di H.264?](#come-fa-h265-a-comprimere-pi%C3%B9-di-h264)
- [Streaming online](#streaming-online)
* [Architettura generale](#architettura-generale)
* [Download progressivo e download adattivo](#download-progressivo-vs-streaming-adattivo)
* [Protezione dei contenuti](#protezione-dei-contenuti)
- [Come usare jupyter](#come-usare-jupyter)
- [Conferenze](#conferenze)
- [Riferimenti](#riferimenti)
# Terminologia di base
Un'**immagine** può essere pensata come una **matrice bidimensionale**. Se pensiamo al fatto che ci sono anche i **colori**, possiamo evolvere questa idea pensando l'immagine come una **matrice tridimensionale**, dove la **terza dimensione** (o le dimensioni aggiuntive) viene utilizzata per memorizzare le **informazioni sul colore**.
Se scegliamo di rappresentare i colori utilizzando i [tre colori primari (rosso, verde e blu)](https://it.wikipedia.org/wiki/Colore_primario), possiamo definire tre piani: uno per il **rosso**, uno per il **verde** e uno per il **blu**.
Chiameremo ogni punto di questa matrice un **pixel** (dall'inglese *picture element*). Un pixel rappresenta l'**intensità** (che in genere è un valore numerico) di un determinato colore. Ad esempio, un **pixel rosso** può essere rappresentato con zero verde, zero blu e il massimo rosso possibile. Un **pixel di colore rosa** può essere formato con una combinazione di tre colori: usando una rappresentazione numerica in un intervallo che va da 0 a 255, il rosa può essere definito come **Rosso=255, Verde=192 e Blu=203**.
> #### Altri modi per codificare un'immagine a colori
> Si possono utilizzare molti altri modelli per rappresentare i colori che compongono un'immagine. Potremmo ad esempio utilizzare una tavolozza di colori (con un indice associato a ciascun colore), rappresentando poi i pixel colorati con l'indice del colore nella tavolozza. In questo modo ogni pixel richiederebbe soltanto un byte di memoria, anziché tre byte come nel caso del modello RGB. Il vantaggio di un modello a _palette_ è quello di occupare meno memoria, riducendo però il numero di colori a disposizione.
>
> 
Per fare un esempio, guarda le immagini qui sotto. La prima figura è completamente colorata. Le altre mostrano rispettivamente i piani (o canali) rosso, verde e blu, in scala di grigi.
Possiamo notare che il **colore rosso** è quello che **contribuisce di più** a creare il colore finale (si notino le parti più chiare nella seconda figura, sotto "Red"), mentre il **colore blu** contribuisce prevalentemente a rendere il colore degli **occhi di Mario** e parte dei suoi vestiti (ultima figura a destra). Si può anche notare come nessun canale RGB dia un forte contributo a realizzare i colori scuri, come i baffi di Mario.
Ogni colore richiede un certo numero di bit per essere rappresentato. Questo valore si chiama **profondità di bit**. Se ad esempio decidiamo di utilizzare **8 bit** per canale RGB (potendo rappresentare quindi valori che vanno da 0 a 255), avremo una **profondità di colore** di **8 * 3 = 24 bit**, per un totale di 224 colori diversi.
> **È molto interessante** imparare [come un'immagine viene catturata dal mondo reale e convertita in bit](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm).
Un'altra proprietà delle immagini è la **risoluzione**, cioè il numero di pixel per ogni dimensione. Viene spesso indicata utilizzando la notazione "larghezza x altezza", ad esempio **4x4** per indicare un'immagine larga 4 pixel e alta 4 pixel.
> #### Esercizio: sperimentare con le immagini e i colori
> Puoi esercitarti [con le immagini e i colori](/image_as_3d_array.ipynb) utilizzando [jupyter](#how-to-use-jupyter) (python, numpy, matplotlib, ecc.).
>
> Puoi anche imparare [come funzionano i filtri (rilevazione dei contorni, nitidezza, sfocatura, ecc.)](/filters_are_easy.ipynb).
Lavorando con immagini e video incontreremo anche una proprietà che si chiama **rapporto d'aspetto**, che descrive la relazione di proporzionalità tra la larghezza e l'altezza di un'immagine o di un pixel.
Quando senti dire che un film o un'immagine è in 16:9, di solito ci si riferisce al **rapporto d'aspetto dell'immagine** (anche chiamato *DAR*, dall'inglese *Display Aspect Ratio*). Tuttavia, anche i pixel possono avere forme diverse, per cui si parla di rapporto d'aspetto anche per i pixel (più precisamente *PAR*, dall'inglese **Pixel Aspect Ratio**).
> #### I DVD hanno un DAR 4:3
> Anche se la risoluzione video di un DVD è 704x480, il rapporto d'aspetto è 4:3, perché i pixel hanno un PAR di 10:11 (704x10/480x11).
Infine, possiamo definire un **video** come una **successione di *n* fotogrammi per unità di tempo**. Il valore *n* è la frequenza dei fotogrammi (*frame rate* in inglese), spesso abbreviata con FPS (fotogrammi al secondo).
Il numero dei bit necessari per rappresentare un secondo di video è chiamato **bitrate**, o meno comunemente *frequenza di bit*.
> bitrate = larghezza * altezza * profondità di bit * fotogrammi al secondo
Ad esempio, un video con 30 fotogrammi al secondo e 24 bit per pixel, con una risoluzione di 480x240 ha bisogno (se non utilizziamo nessun tipo di compressione) di **82˙944˙000 bit al secondo**, cioè 82,944 Mbps (il calcolo è 30x480x240x24 byte).
Quando il **bitrate** rimane quasi uguale nel tempo, parliamo di **bitrate costante (CBR)**, mentre quando varia di **bitrate variabile (VBR)**.
> Il grafico mostra il bitrate di un video codificato in modalità VBR limitata (dai valori min e max). Si può notare come il bitrate è più basso quando il fotogramma è nero.
>
> 
Molto tempo fa è stata inventata una tecnica per **raddoppiare il framerate percepito** da chi sta guardando un video, **senza richiedere l'utilizzo di dati aggiuntivi**. Questa tecnica è conosciuta come **interlacciamento**. Semplificando, in un video interlacciato metà dell'immagine è contenuta in un fotogramma, mentre l'altra metà nel fotogramma successivo.
Al giorno d'oggi la maggior parte degli schermi utilizza la **scansione progressiva**, una tecnica per cui la trasmissione, memorizzazione e visualizzazione delle immagini in movimento avviene disegnando progressivamente e in sequenza tutte le linee che compongono il fotogramma.
A questo punto abbiamo un'idea di come un'**immagine** viene rappresentata digitalmente, come i suoi **colori** sono disposti, quanti **bit al secondo** sono richiesti per mostrare un video, con bitrate costante (CBR) o variabile (VBR), e cosa sono i concetti di **risoluzione**, **framerate** (FPS), interlacciamento, PAR, ecc.
> #### Esercizio: controllare le proprietà di un file video
> Puoi vedere la maggior parte delle proprietà che abbiamo appena visto [usando ffmpeg oppure mediainfo](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream) su un file video.
# Rimozione delle informazioni ridondanti
Abbiamo imparato che memorizzare un video senza nessuna compressione è impraticabile. **Un'ora di video** a una risoluzione 720p30 (720 linee verticali con scansione progressiva, con 30 fotogrammi al secondo) **richiederebbe 278 GB**\*. Dato che utilizzando i normali algoritmi di compressione lossless (come DEFLATE, utilizzato in PKZIP, Gzip e PNG) non otterremmo una diminuzione significativa della dimensione del file, dobbiamo trovare altri modi per comprimere il video.
> \*Si può ottenere questo risultato moltiplicando 1280 x 720 x 24 x 30 x 3600 (larghezza, altezza, bit per pixel, fps e durata in secondi).
Per farlo possiamo sfruttare il modo in cui la nostra vista funziona. Possiamo infatti sfruttare il fatto che l'occhio riesce a distinguere meglio diverse luminosità che diversi colori. È importante anche considerare le **ripetizioni nel tempo** (un video contiene molti fotogrammi molto simili, con poche differenze) e le **ripetizioni all'interno delle immagini** (un fotogramma può contenere aree che hanno lo stesso colore o un colore molto simile).
## I colori, la luminanza e i nostri occhi
I nostri occhi sono [più sensibili alla luminosità che ai colori](http://vanseodesign.com/web-design/color-luminance/), puoi vederlo tu stesso con l'immagine seguente.
Se nell'immagine a sinistra non riesci a vedere che **i colori dei quadrati A e B sono identici**, è normale. È il nostro cervello che ci induce a **prestare più attenzione alla luce e al buio che al colore**. Nell'immagine a destra i due quadrati sono collegati con una striscia dello stesso colore dei quadrati, così riusciamo molto più facilmente a vedere che A e B sono effettivamente dello stesso colore.
> **Spiegazione semplificata del funzionamento dell'occhio**
>
> L'occhio [è un organo complesso](http://www.biologymad.com/nervoussystem/eyenotes.htm): è composto da molte parti ma siamo prevalentemente interessati alle cellule fotorecettrici, i coni e i bastoncelli. L'occhio contiene [circa 120 milioni di bastoncelli e 6 milioni di coni](https://it.wikipedia.org/wiki/Fotorecettore).
>
> In modo **molto semplificato**, proviamo ad associare colori e luminosità alle parti degli occhi. I **[bastoncelli](https://it.wikipedia.org/wiki/Bastoncello) sono quasi solo responsabili della luminosità**, mentre **[i coni](https://it.wikipedia.org/wiki/Cellula_cono) sono responsabili del colore**. I coni sono di tre tipi, ognuno con un pigmento diverso e sensibile a un colore diverso. In particolare, esistono [coni S (blu), coni M (verde) e coni L (rosso)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> Dato che l'occhio ha molti più bastoncelli (luminosità) che coni (colore), si può dedurre che l'occhio è più bravo a distinguere il chiaro dallo scuro che i colori.
>
> 
>
> **Curve di sensibilità al contrasto**
>
> I ricercatori di psicologia sperimentale (e altri campi) hanno sviluppato molte teorie sull'occhio umano. Una di queste si chiama "curve di sensibilità al contrasto", e studia le funzioni che descrivono il legame spaziotemporale della luce, il loro valore data una certa quantità di luce iniziale, e quanto cambiamento è richiesto prima che un osservatore si accorga che c'è stato un cambiamento. Il plurale della parola "curve" è dovuto al fatto che possiamo usare queste funzioni non solo per studiare il bianco e il nero, ma anche i colori. Il risultato di questi esperimenti mostra che nella maggior parte dei casi l'occhio è più sensibile alla luce che al colore.
Ora che sappiamo che siamo più sensibili alla lumimanza (la luminosità di un'immagine), possiamo provare a sfruttare questo fatto.
### Modelli di colore
Abbiamo inizialmente imparato [come colorare le immagini](#terminologia-di-base) usando il **modello RGB**, ma esistono altri modelli. Ad esempio esiste un modello che separa la luminanza (la luminosità) dalla crominanza (i colori), ed è conosciuto come **YCbCr**\*.
> \*esistono anche altri modelli che fanno la stessa distinzione.
Questo modello di colore utilizza la **Y** per rappresentare la luminosità, e due canali **Cb** (*chroma blue*) e **Cr** (*chroma red*) per i colori. Il modello [YCbCr](https://it.wikipedia.org/wiki/YCbCr) può essere derivato da RGB, e viceversa. Con i tre canali possiamo creare immagini pienamente colorate, come mostrato nell'immagine:
### Conversione tra YCbCr e RGB
Qualcuno si potrebbe chiedere: "**come è possibile produrre tutti i colori senza usare il verde?**"
Per rispondere a questa domanda, proveremo a fare una conversione da RGB a YCbCr. Utilizzeremo i coefficienti definiti dallo **[standard BT.601](https://it.wikipedia.org/wiki/BT.601)**, una delle raccomandazioni del gruppo [ITU-R](https://it.wikipedia.org/wiki/ITU-R)\*.
Il primo passo è **calcolare la luminanza**, utilizzando le costanti suggerite dall'ITU e i valori RGB.
```
Y = 0.299R + 0.587G + 0.114B
```
Ora che abbiamo la luminanza, possiamo **dividere i colori** (colore blu e rosso):
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
Possiamo anche **tornare indietro** facendo la conversione inversa, e anche **ottenere il valore del verde utilizzando YCbCr**.
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> \* i gruppi e gli standard sono comuni nel mondo del video digitale, di solito definiscono quali sono gli standard da usare, ad esempio [cosa si intende con 4K? quale framerate dovrei usare? quale risoluzione e modello di colore?](https://it.wikipedia.org/wiki/BT.2020)
In genere i display (monitor, tv, schermi, ecc.) utilizzano **solo il modello RGB**, anche se con geometrie dei pixel diverse, come si vede nelle immagini, che rappresentano schermi di tipo diverso, ingranditi.
### Sottocampionamento della crominanza
Con la rappresentazione dell'immagine in termini di luminanza e crominanza, possiamo trarre vantaggio dalle caratteristiche dell'occhio (che privilegiano la luminanza) per rimuovere selettivamente delle informazioni. La tecnica di **sottocampionamento della crominanza** fa proprio questo, e cioè utilizza **meno risoluzione per la crominanza rispetto alla luminanza**.
Quanto dovremmo ridurre la risoluzione della crominanza? Si scopre che esistono già degli schemi che descrivono come gestire la risoluzione e l'unione `colore finale = Y + Cb + Cr`.
Questi schemi sono noti come **sistemi di sottocampionamento** e sono espressi utilizzando un rapporto composto da tre valori. In particolare `a:x:y` definisce la risoluzione di crominanza di un blocco di pixel di luminanza di dimensione `a x 2` pixel.
* `a` numero di pixel orizzontali di riferimento per il campionamento (di solito 4)
* `x` numero di campioni di crominanza nella prima riga di `a` pixel (risoluzione orizzontale in relazione a `a`)
* `y` numero di cambiamenti di campioni di crominanza tra la prima e la seconda riga della griglia `a x 2`
> C'è un'eccezione a questa definizione, in particolare nello schema 4:1:0, che prevede un singolo campione di crominanza per ogni blocco `4 x 4` di luminanza.
Tra gli schemi più comuni nei codec moderni ci sono **4:4:4** *(nessun sottocampionamento)*, **4:2:2, 4:1:1, 4:2:0, 4:1:0 and 3:1:1**.
> **Unione di YCbCr 4:2:0**
>
> Ecco un esempio di come funziona l'unione di un'immagine usando YCbCr con schema di sottocampionamento della crominanza 4:2:0. Nota che vengono utilizzati soltanto 12 bit per pixel.
>
> 
Le immagini che seguono mostrano la stessa foto codificata con diversi tipi di sottocampionamento della crominanza. Le immagini della prima riga sono quelle finali, mentre quelle della seconda riga mostrano la risoluzione della crominanza. Il risultato è ottimo dato che la perdita di qualità nell'immagine a colori è molto bassa.
Prima abbiamo calcolato che sono necessari [278 GB di spazio per archiviare un file video dalla durata di un'ora, con risoluzione 720p e framerate di 30 fps](#rimozione-delle-informazioni-ridondanti). Se utilizziamo il modello colore **YCbCr 4:2:0**, possiamo **dimezzare la dimensione (139 GB)**\*, anche se siamo ben lontani dall'ideale.
> \*si ottiene questo valore moltiplicando tra loro larghezza, altezza, bit per pixel e fps. Con il modello RGB avevamo bisogno di 24 bit per pixel, ora ne abbiamo bisogno soltanto 12.
> ### Esercizio: vedere l'istogramma YCbCr
> Puoi controllare [l'istogramma di un video YCbCr con ffmpeg](/encoding_pratical_examples.md#generates-yuv-histogram). Ad esempio, in questa scena il colore blu porta un alto contributo, come evidenziato dall'[istogramma](https://it.wikipedia.org/wiki/Istogramma).
>
> 
## Tipi di fotogramma
Prima di occuparci della **ridondanza delle informazioni nel tempo**, chiariamo la terminologia che useremo. Supponiamo di avere un filmato con 30 fps, i cui primi 4 fotogrammi sono:
  
È evidente che ci sono **molte ripetizioni** all'interno e tra i fotogrammi. Ad esempio lo sfondo blu non cambia tra il primo e l'ultimo fotogramma. Per sfruttare questa caratteristica dei fotogrammi, possiamo **categorizzare in modo astratto** i fotogrammi in [tre tipi diversi](https://it.wikipedia.org/wiki/Tipi_di_fotogramma_nella_compressione_video).
### I-frame (intra, keyframe)
Un I-frame (chiamato anche *reference frame*, *keyframe*, *intra-frame*) è un fotogramma **indipendente**. Non si affida a nessun'altra informazione esterna per essere renderizzato. È come una foto statica. Di solito il primo fotogramma di un video è un I-frame, ma vedremo che ce ne possono essere più di uno in un video, mischiati tra gli altri fotogrammi di tipo diverso.
### P-frame (predicted)
Un P-frame (*fotogramma predetto*) sfrutta il fatto che quasi sempre il fotogramma può essere **renderizzato utilizzando il fotogramma precedente**. Ad esempio, nel secondo fotogramma l'unico cambiamento rispetto al precedente è il movimento della palla. Possiamo quindi **ricostruire questo fotogramma utilizzando le differenze rispetto al precedente**.
 <- 
> #### Esercizio: un video con un solo I-frame
> Dato che un P-frame utilizza meno dati rispetto a un I-frame, perché non possiamo codificare un intero video [usando solo un I-frame e poi solo P-frame?](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)
>
> Per esercizio codifica il video esempio, riproducilo e **salta a una posizione avanzata** del video. Noterai che sarà **richiesto del tempo** perché il player completi l'operazione di *seek*. Questo avviene perché un **P-frame ha bisogno di un fotogramma di riferimento** (ad esempio un I-Frame, ma non per forza) per essere renderizzato.
>
> Un altro veloce esperimento che puoi fare è [codificare un video sia utilizzando un singolo I-frame che impostato un I-frame ogni 2 secondi](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second), e confrontare la **dimensione dei due file risultanti**.
### B-frame (bi-predictive)
E se potessimo fare riferimento sia a fotogrammi passati che futuri, per ottenere una compressione migliore? È proprio come funzionano i B-frame, o frame bi-predittivi.
 <-  -> 
> #### Esercizio: confronta video con e senza B-frame
> Genera due video, uno con i B-frame e uno [senza B-frame](/encoding_pratical_examples.md#no-b-frames-at-all), e confronta la differenza di dimensione del file e la qualità dei risultati.
### Sommario
Diversi tipi di frame possono essere utilizzati per **fornire una migliore compressione**. Scopriremo meglio come nella prossima sezione, ma per ora è importante capire che **gli I-frame sono costosi, i P-frame un po' meno, e infine i B-frame sono i più leggeri**.
## Ridondanza temporale (predizione inter-frame)
Esploriamo le possibilità che abbiamo per cercare di ridurre la **ripetizione delle informazioni nel tempo**. Questo tipo di ridondanza può essere affrontata usando la **predizione tra fotogrammi** (*inter-frame prediction* o *inter prediction*).
L'obiettivo è **utilizzare meno bit** per codificare la sequenza di fotogrammi 0 e 1.
Una cosa che potremmo fare è **sottrarre il fotogramma 1 dal fotogramma 0**, in modo da ottenere soltanto il **residuo da codificare**.
E se ti dicessi che esiste un **metodo migliore**, che consente di utilizzare ancora meno informazione? Consideriamo il `fotogramma 0` come un insieme di partizioni (riquadri) ben definite, e proviamo a creare un'associazione tra i blocchi del `fotogramma 0` e il `fotogramma 1`. Possiamo pensare a questo meccanismo come una **stima del moto**.
> ### Da Wikipedia: compensazione del moto dei blocchi
> "La **compensazione del moto dei blocchi** divide il fotogramma corrente in blocchi non sovrapposti, e il vettore di compensazione del moto **indica da dove si sono spostati quei blocchi** (una convinzione comune ma errata è che il fotogramma precedente viene diviso in blocchi non sovrapposti, e i vettori di compensazione del moto indicano dove i blocchi si sposteranno). I blocchi sorgente tipicamente si sovrappongono nel fotogramma sorgente. Alcuni algoritmi di compressione video costruiscono il fotogramma corrente utilizzando pezzi di diversi fotogrammi creati precedentemente."
Possiamo stimare che la palla si è spostata dalla posizione `x=0, y=25` alla posizione `x=6, y=26`. I valori di **x** e **y** determinano i **vettori del moto**. Un passo aggiuntivo che possiamo fare per risparmiare spazio è **codificare soltanto il vettore di spostamento ottenuto dalla differenza** tra l'ultima posizione del blocco e quella predetta. Nell'esempio sopra il vettore del moto sarebbe quindi `x=6 (6-0), y=1 (26-25)`.
> In una situazione di codifica reale, **la palla sarebbe divisa tra più partizioni**, ma il procedimento è lo stesso.
Gli oggetti nel fotogramma **si muovono in 3 dimensioni**, cioè ad esempio la palla può diventare più piccola se si muove verso lo sfondo. È quindi normale non trovare **la corrispondenza perfetta** tra blocchi. Ad esempio questo è il risultato della nostra stima del moto in confronto alla figura reale:
Il vantaggio di applicare la tecnica della **stima del moto** è che **i dati da codificare sono di meno** in confronto a una semplice differenza tra fotogrammi.
> ### Come funziona realmente la compensazione del moto
> La tecnica appena vista viene applicata a tutti i blocchi, ma probabilmente la palla dell'esempio verrebbe partizionata in più di un blocco.
> 
> Fonte: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
Puoi esercitarti con questi concetti [usando jupyter](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Esercizio: vedere i vettori del moto
> Possiamo generare un video [che mostra la predizione tra frame (e i vettori del moto) con ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> In alternativa possiamo usare il software [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (è a pagamento ma esiste una versione di prova che limita l'analisi ai primi 10 fotogrammi).
>
> 
## Ridondanza spaziale (predizione intra-frame)
Se analizziamo un **singolo fotogramma** di un video noteremo che ci sono **molte aree correlate**.
Facciamo un esempio. Questa scena è composta prevalentemente dai colori blu e bianco:
Questo fotogramma è un `I-frame`, per cui **non possiamo usare fotogrammi precedenti** per costruirlo. Possiamo però comunque comprimerlo. Proviamo ad esempio a codificare il blocco evidenziato in rosso. Se guardiamo **intorno al blocco** possiamo **stimare** che c'è una **tendenza di colore attorno al blocco**.
Possiamo ad esempio **predire** che i pixel colorati sopra il blocco **continuino allo stesso modo verticalmente**. In altre parole **i pixel del blocco che hanno colore sconosciuto assumeranno attraverso la predizione il colore dei pixel che si trovano sopra** (in questo caso).
La **predizione che abbiamo fatto può essere sbagliata**, per cui dopo aver applicato la predizione dobbiamo **sottrarre i valori reali** dei pixel, in modo da ottenere un blocco residuo. Il risultato è una matrice molto più facilmente comprimibile in confronto all'originale.
> #### Esercizio: vedere la predizione intra-frame
> Puoi [generare un video con i macro-blocchi e le sue predizioni con ffmpeg](/encoding_pratical_examples.md#generate-debug-video). Consulta la documentazione di ffmpeg per comprendere meglio [il significato dei colori dei blocchi](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes).
>
> 
>
> In alternativa puoi anche usare il software [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (è a pagamento ma esiste una versione di prova che limita l'analisi ai primi 10 fotogrammi).
>
> 
# Come funziona un codec video?
## Cosa? Perché? Come?
**Cos'è?** È un software o un dispositivo hardware che comprime o decomprime un video digitale. **Perché?** Il mercato e la società richiedono video di qualità alta con banda o spazio di archiviazione limitati. Ti ricordi che abbiamo [calcolato la banda necessaria](#terminologia-di-base) per trasmettere un video con 30 fotogrammi al secondo, 24 bit per pixel e una risoluzione di 480x240 pixel? Risultava **83 Mbps**, senza applicare alcuna compressione. I codec sono l'unico modo per fornire video HD/Full HD/UHD alle tv e tramite Internet. **Come?** Stiamo per dare un'occhiata alle principali tecniche usate.
> **CODEC vs Container**
>
> Un errore che viene spesso fatto dai principianti è confondere il CODEC video con il [contenitore video](https://it.wikipedia.org/wiki/Formato_contenitore). Possiamo pensare al **contenitore** come un formato "wrapper" che contiene i metadati sulle tracce video (e audio), e il vero e proprio **video compresso** come carico pagante (*payload*).
>
> Solitamente l'estensione di un file video determina il suo formato contenitore. Ad esempio, un file `video.mp4` indica con molta probabilità il formato **[MPEG-4 Part 14](https://it.wikipedia.org/wiki/MPEG-4_Part_14)**, mentre un file chiamato `video.mkv` si riferisce al formato **[Matroska](https://it.wikipedia.org/wiki/Matroska)**. Per controllare con certezza il codec e il formato contenitore di un file possiamo utilizzare [ffmpeg oppure mediainfo](/encoding_pratical_examples.md#inspect-stream).
## Storia
Prima di passare ad analizzare il funzionamento interno di un codec generico, facciamo un salto indietro nella storia per comprendere meglio alcuni vecchi codec.
Il codec video [H.261](https://it.wikipedia.org/wiki/H.261) nacque nel 1990 (tecnicamente nel 1988) e fu progettato per funzionare con **bitrate di 64 kbit/s**. Il codec utilizzava già idee come il sottocampionamento della crominanza, i macro-blocchi, ecc. Nell'anno 1955 lo standard del codec video **H.263** fu pubblicato, poi modificato ed esteso fino al 2001.
Nel 2003 la prima versione del codec **H.264/AVC** fu completata. Nello stesso anno, un'azienda chiamata **TrueMotion** rilasciò un altro codec per la compressione video, lossy e **royalty-free**, chiamato **VP3**. Nel 2008, **Google acquisto l'azienda**, rilasciando **VP8** nello stesso anno. Nel dicembre del 2012, Google rilasciò **VP9**, oggi **supportato da circa ¾ dei browser sul mercato** (mobile incluso).
**[AV1](https://it.wikipedia.org/wiki/AOMedia_Video_1)** è un nuovo codec video **royalty-free** e open source che è stato progettato dalla [Alliance for Open Media (AOMedia)](http://aomedia.org/), composta da grandi aziende come **Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel e Cisco**. La **prima versione** 0.1.0 del codec di riferimento è stata pubblicata il 7 aprile 2016.
> #### La nascita di AV1
>
> All'inizio del 2015, Google stava lavorando su [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1), Xiph (Mozilla) su [Daala](https://xiph.org/daala/) e Cisco ha reso open source il suo codec video royalty-free chiamato [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03).
>
> MPEG LA ha annunciato dei limiti annuali per HEVC (H.265) e canoni di utilizzo 8 volte superiori a H.264, per poi cambiare le regole di nuovo:
> * **nessun limite annuale**,
> * **commissione sui contenuti** (lo 0,5% delle entrate)
> * **commissioni per unità circa 10 volte più alte di H.264**.
>
> La [*Alliance for Open Media*](http://aomedia.org/about/) è stata creata da aziende produttrici di hardware (Intel, AMD, ARM , Nvidia, Cisco), aziende che forniscono contenuti (Google, Netflix, Amazon), browser (Google, Mozilla), e altre.
>
> Le aziende si sono poste un obiettivo comune: un video codec royalty-free. È quindi nato AV1, con un [sistema di licenze molto più semplice](http://aomedia.org/license/patent/). **Timothy B. Terriberry** ha fatto [un'ottima presentazione](https://www.youtube.com/watch?v=lzPaldsmJbk) (fonte di questa sezione) sul concepimento di AV1, il modello di licenza e lo stato attuale del codec.
>
> Ti sorprenderà sapere che puoi **analizzare il codec AV1 tramite il tuo browser**, andando alla pagina https://arewecompressedyet.com/analyzer/
>
> 
>
> P.S.: se vuoi imparare di più sulla storia dei codec è importante conoscere le basi che stanno dietro ai [brevetti legati alla compressione video](https://www.vcodex.com/video-compression-patents/).
## Un codec generico
Introdurremo ora i **meccanismi principali che stanno dietro a un generico codec video**, ma che si applicano anche ai principali codec moderni come VP9, AV1 e HEVC. Tieni presente che la spiegazione sarà *molto* semplificata rispetto alla realtà, anche se useremo qualche esempio reale (come H.264) per mostrare il funzionamento di una tecnica.
## 1° passo - partizionamento dell'immagine
Il primo passo è **dividere il fotogramma** in molte **partizioni, sotto-partizioni** e via dicendo.
**Perché si fa?** Ci sono molte ragioni: ad esempio, dividendo l'immagine possiamo calcolare le predizioni più precisamente, e utilizzare le partizioni più piccole per le parti in movimento, mentre quelle più grandi per rappresentare sfondi statici.
Solitamente, i CODEC **organizzano le partizioni** in *slice* (o *tile*), *macro* (o *coding tree unit*) e tante altre sotto-partizioni. La dimensione massima di queste partizioni varia, ad esempio per HEVC è 64x64 pixel, per AVC (H.264) è 16x16, con le sotto-partizioni che possono essere piccole fino a 4x4 pixel.
Ti ricordi che i fotogrammi possono essere di **diverso tipo**? Si può **applicare la stessa idea anche ai blocchi**, per cui possiamo avere I-slice, B-slice, I-macroblock, ecc.
> ### Esercizio: vedere le partizioni
> Puoi usare il software [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (è a pagamento ma esiste una versione di prova che limita l'analisi ai primi 10 fotogrammi). Ecco un esempio che mostra le [partizioni di VP9](/encoding_pratical_examples.md#transcoding).
>
> 
## 2° passo - predizioni
Ora che abbiamo le partizioni, possiamo usarle per fare le predizioni. Per la predizione [inter-frame](#ridondanza-temporale-predizione-inter-frame) dobbiamo raccogliere informazioni sui **vettori del moto e sul residuo**, mentre per la predizione [intra-frame](#ridondanza-spaziale-predizione-intra-frame) ci servono **la direzione della predizione e il residuo**.
## 3° passo - trasformazione
Una volta che abbiamo il blocco residuo (ottenuto da `partizione predetta - partizione reale`), possiamo **trasformarlo** in un modo che ci consenta di vedere **quali pixel possiamo scartare** pur mantenendo la **qualità generale**. Ci sono diverse trasformazioni che ci permettono di farlo.
Anche se esistono [molte altre trasformazioni](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms), daremo uno sguardo più approfondito alla trasformata discreta del coseno (*DCT*). La [DCT](https://it.wikipedia.org/wiki/Trasformata_discreta_del_coseno) ci permette di:
* **convertire** blocchi di pixel in blocchi di **coefficienti di frequenza** della stessa dimensione
* **compattare** l'energia, aiutandoci a eliminare la ridondanza spaziale
* **invertire** il processo, cioè tornare a blocchi di pixel
> Il 2 febbraio 2017, Cintra, R. J. e Bayer, F. M hanno pubblicato l'articolo scientifico [DCT-like Transform for Image Compression Requires 14 Additions Only](https://arxiv.org/abs/1702.00817).
Non preoccuparti se non hai capito a pieno i benefici di ciascun punto, faremo qualche esperimento in modo da capire meglio il reale valore della trasformata.
Prendiamo il seguente **blocco di pixel** (8x8):
Che corrisponde alla seguente immagine (8x8):
**Applicando la DCT** a questo blocco di pixel otteniamo un **blocco dei coefficienti** (8x8):
Il blocco dei coefficienti ottenuto, convertito in un'immagine, appare così:
Come puoi vedere non assomiglia per niente all'immagine originale. Si può anche notare che il **primo coefficiente** è molto diverso dagli altri. Questo perché il primo coefficiente della DCT è quello che rappresenta **tutti i campioni** della matrice di ingresso, qualcosa di **simile a una media**.
Il blocco dei coefficienti ha un'interessante proprietà, e cioè che separa i componenti relativi alle alte frequenze da quelli delle basse frequenze.
In un'immagine, **la maggior parte dell'energia** è concentrata nelle [**basse frequenze**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm), quindi dopo aver applicato la trasformata possiamo **scartare i coefficienti delle alte frequenze**, in modo da **ridurre la quantità di dati** richiesta per descrivere l'immagine, pur senza sacrificare troppo la qualità dell'immagine.
> la frequenza determina quando rapidamente un segnale cambia.
Proviamo ad applicare le conoscenze acquisite: convertiamo l'immagine originale nelle sue frequenze usando la DCT, e poi scartiamo i coefficienti meno importanti.
Per prima cosa, convertiamo il blocco nel **dominio della frequenza**.
Poi, scartiamo parte dei coefficienti (il 67%), prevalentemente nell'angolo in basso a destra del blocco.
Infine, ricostruiamo l'immagine utilizzando il blocco dei coefficienti modificato, e confrontiamo il risultato con l'immagine originale.
L'immagine a destra è simile a quella originale, ma introduce anche molte differenze. Abbiamo però **buttato via il 67,1875%** delle informazioni e comunque siamo in grado di ottenere un'immagine che assomiglia a quella originale. Il prossimo passo è capire come modificare i coefficienti in modo più intelligente, per ottenere così una migliore qualità dell'immagine.
> **Ogni coefficiente viene calcolato utilizzando tutti i pixel**
>
> È importante evidenziare che ogni coefficiente del blocco non corrisponde direttamente a un singolo pixel, ma è una somma pesata di tutti i pixel. Il grafico che segue mostra come il primo e il secondo coefficiente vengono calcolati, utilizzando persi che sono diversi per ogni indice.
>
> 
>
> Fonte: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> Puoi anche provare a [osservare visivamente la DCT con la formazione di un'immagine](/dct_better_explained.ipynb). Ad esempio, qua puoi vedere [la formazione della lettera A](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT) utilizzando diversi pesi per i coefficienti.
>
> 
> ### Esercizio: scartare diversi coefficienti
> Puoi sperimentare con [la trasformata DCT](/uniform_quantization_experience.ipynb).
## 4° passo - quantizzazione
Quando scartiamo dei coefficienti, come abbiamo fatto alla fine dell'ultimo passo, stiamo già facendo una sorta di quantizzazione. Questo passo è quello in cui decidiamo quali informazioni "perdere", e lo facciamo **quantizzando i coefficienti per ottenere della compressione**.
Come possiamo quantizzare un blocco di coefficienti? Un metodo semplice potrebbe essere una quantizzazione uniforme, in cui prendiamo un blocco e **dividiamo tutti i valori per lo stesso numero** (10), per poi arrotondare.
Come possiamo **invertire** (ri-quantizzare) il blocco dei coefficienti? È sufficiente **moltiplicare i valori ottenuti per lo stesso numero** con cui li abbiamo divisi (10).
**Questo approccio non è però il migliore**, perché non tiene in considerazione l'importanza dei diversi coefficienti. Anziché un singolo valore per la divisione, potremmo utilizzare una **matrice di quantizzazione**, che sfrutterebbe quindi le proprietà della DCT quantizzando maggiormente l'angolo in basso a destra e in modo minore quello in alto a sinistra. La [compressione JPEG](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html) utilizza un metodo simile, e puoi vedere un esempio di matrice di quantizzazione [nel codice sorgente](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40) di un compressore JPEG.
> ### Esercizio: quantizzazione
> Puoi sperimentare [con la quantizzazione](/dct_experiences.ipynb).
## 5° passo - codifica dell'entropia
Dopo aver quantizzato i dati (i blocchi/slice/fotogrammi), possiamo comprimerli ulteriormenti utilizzando metodi di compressione lossless (senza perdita). Ci sono moltissimi algoritmi per comprimere i dati, e andremo a vederne alcuni. Per comprendere a fondo i concetti legati alla compressione dei dati, una lettura consigliata è il fantastico libro [Understanding Compression: Data Compression for Modern Developers](https://www.amazon.it/Understanding-Compression-Data-Modern-Developers/dp/1491961538/).
### Codifica VLC:
Supponiamo di avere un flusso di simboli: **a**, **e**, **r** e **t**, con le seguenti probabilità (valori da 0 a 1) di comparire nel flusso.
| | a | e | r | t |
|-------------|-----|-----|-----|-----|
| probabilità | 0,3 | 0,3 | 0,2 | 0,2 |
Possiamo assegnare un codice binario (preferibilmente piccolo) al simbolo più probabile, e un codice più lungo ai simboli meno probabili.
| | a | e | r | t |
|----------------|-----|-----|-----|------|
| probabilità | 0,3 | 0,3 | 0,2 | 0,2 |
| codice binario | 0 | 10 | 110 | 1110 |
Proviamo ora a comprimere il flusso rappresentato dalla parola **eat**. Assumendo di utilizzare 8 bit per ogni simbolo (carattere), avremmo bisogno di **24 bit** per rappresenta la parola, senza utilizzare nessun metodo di compressione. Ma se invece utilizziamo i codici binari associati alle lettere, possiamo risparmiare spazio.
Il primo passo è codificare il simbolo **e**, che corrisponde a `10`, per poi passare a **a**, con cui otteniamo `[10][0]` e infine il simbolo **t**, per ottenere `[10][0][1110]`, o `1001110`. Il risultato richiede soltanto **7 bit** per essere memorizzato, 3,4 volte volte in meno rispetto ai 24 bit iniziali.
È importante notare che ogni codice binario deve avere un prefisso unico (*proprietà del prefisso*). La [codifica di Huffman](https://it.wikipedia.org/wiki/Codifica_di_Huffman) aiuta a calcolare questi codici. Nonostante questa tecnica abbia qualche problema, ci sono ancora [alcuni codec video che la offrono](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding) come opzione.
Sia il codificatore (encoder) che il decodificatore (decoder) **devono conoscere la tabella simboli-codici**, per cui è necessario salvare anche la tabella.
### Codifica aritmetica:
Supponiamo di avere il flusso di simboli **a**, **e**, **r**, **s** e **t**, le cui probabilità sono rappresentate dalla tabella.
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| probabilità | 0,3 | 0,3 | 0,15 | 0,05 | 0,2 |
Usando le probabilità possiamo associare degli intervalli ai simboli, ordinandoli per probabilità decrescente.
Ora codifichiamo il flusso **eat**: prendiamo il primo simbolo **a**, il cui intervallo va **da 0,3 a 0,6** (non incluso). Lo dividiamo nuovamente, usando le stesse proporzioni della divisione originale, solo che applicate al sottointervallo.
Continuiamo con la codifica del flusso **eat**, prendendo il simbolo **a** che corrisponde ora all'intervallo che va **da 0,3 a 0,39**. Infine ripetiamo il processo con il simbolo **t**, ottenendo l'ultimo intervallo tra **0,354 e 0,372**.
Dobbiamo ora semplicemente prendere un qualsiasi valore contenuto tra **0,354 e 0,372**, ad esempio **0,36**, ma potremmo prenderne uno qualsiasi. Con **solo** questo numero siamo in grado di ricostruire il flusso di simboli originale **eat**. Se ci pensi, è come se stessimo tracciando una riga che attraversa gli intervalli e i sottointervalli per codificare il flusso.
Il **processo inverso** (la decodifica) è ugualmente facile. Avendo a disposizione il numero **0,36** e l'intervallo originale possiamo seguire la stessa procedura, usando il numero per scoprire sottointervallo per sottointervallo il flusso di simboli associato.
In pratica, usando il primo intervallo troviamo che il numero è compreso nell'intervallo del simbolo **e**, per cui prendiamo quell'intervallo e lo dividiamo di nuovo. Ripetiamo quanto appena fatto per trovare che il valore **0,36** è compreso nell'intervallo di **a**, che è quindi il secondo simbolo del flusso. Infine troviamo l'ultimo simbolo **t**, che va a completare il flusso finale **eat**.
Sia l'encoder che il decoder **devono conoscere** la tabella dei simboli con le probabilità, per cui la tabella deve essere salvata e trasmessa.
Interessante vero? Ci devono essere persone incredibilmente intelligenti per riuscire a inventare una soluzione del genere, che oggi viene usata [in molti codec video](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding).
L'idea che deve restare è che si può prendere il flusso di bit (bitstream) risultante dalla quantizzazione e comprimerlo in modo lossless, cioè senza perdita di informazioni. Sicuramente questo articolo manca di molti dettagli, spiegazioni, vantaggi e compromessi, ma puoi [imparare facilmente di più se sei uno sviluppatore](https://www.amazon.it/Understanding-Compression-Data-Modern-Developers/dp/1491961538/). I nuovi codec stanno iniziando ad usare differenti sistemi di codifica dell'entropia, [come ANS](https://en.wikipedia.org/wiki/Asymmetric_numeral_systems).
> ### Esercizio: CABAC vs CAVLC
> Puoi [generare due video, uno con codifica dell'entropia CABAC e l'altro con CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc), e **confrontare il tempo di codifica**, così come la **dimensione del file finale**.
## 6° passo - formato bitstream
Dopo aver completato tutti questi passaggi, ora dobbiamo **compattare tutti i fotogrammi compressi con le loro informazioni di contesto**. Dobbiamo informare in modo esplicito il decoder delle **decisioni che sono state prese dall'encoder durante la codifica**, tra cui la profondità di bit, lo spazio colore, la risoluzione, le predizioni (vettori di moto inter-frame, direzione della predizione intra-frame), il profilo, il livello, il frame rate, il tipo di fotogramma, il numero di fotogramma e molto altro.
Stiamo per studiare, superficialmente, il bitstream del codec H.264. La prima cosa da fare è [generare un bitstream H.264 minimo\*](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream), usando [ffmpeg](http://ffmpeg.org/).
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> \*ffmpeg aggiunge, in modo predefinito, tutti i parametri di codifica come un **NAL SEI**. Presto definiremo che cos'è un NAL.
Il comando genera un bitstream H.264 "raw" (senza formato contenitore), con un **singolo fotogramma**, di dimensione 64x64 pixel, con spazio colore yuv420 (cioè YCbCr 4:2:0) e usando l'immagine seguente come fotogramma.
> 
### Bitstream di H.264
Lo standard AVC (H.264) stabilisce che le informazioni vengano trasmesse in **macro frame** (nel significato legato alle reti di telecomunicazioni), chiamati **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (Network Abstraction Layer). L'obiettivo principale dei NAL è fornire una rappresentazione del video che sia adatta per la trasmissione in rete, considerato che lo standard deve funzionare sulle tv (basate su flussi) e tramite Internet (basato su pacchetti), tra gli altri.
Viene usato un **[segnale di sincronizzazione](https://en.wikipedia.org/wiki/Frame_synchronization)** (*synchronization marker*) per delimitare le unità NAL. Ogni segnale di sincronizzazione corrisponde al valore fisso `0x00 0x00 0x01`, ad eccezione del primo che ha il valore `0x00 0x00 0x00 0x01`. Se eseguiamo il comando **hexdump** sul bitstream H.264 che abbiamo generato, riusciamo facilmente ad identificare almeno tre unità NAL all'inizio del file.
Come detto prima, il decodificatore ha bisogno non solo dei dati dell'immagine, ma anche di dettagli come il numero di fotogramma, i colori, i parametri usati e altro. Il **primo byte** di ciascun NAL definisce la sua categoria e il **tipo**.
| ID del tipo di NAL | Descrizione |
|--- |---|
| 0 | Non definito |
| 1 | Slice codificato di un'immagine non IDR |
| 2 | Slice codificato di una partizione A |
| 3 | Slice codificato di una partizione B |
| 4 | Slice codificato di una partizione C |
| 5 | **IDR** Slice codificato di un'immagine IDR |
| 6 | **SEI** Informazioni aggiuntive (*supplemental enhancement information*) |
| 7 | **SPS** Insieme parametri di sequenza (*sequence parameter set*) |
| 8 | **PPS** Insieme parametri d'immagine (*picture parameter set*) |
| 9 | Delimitatore di unità di accesso |
| 10 | Fine della sequenza |
| 11 | Fine del flusso |
| ... | ... |
Solitamente il primo NAL di un bitstream è di tipo **SPS**. Questo tipo di NAL contiene informazioni generiche sulle variabili di codifica, come il **profilo**, il **livello**, la **risoluzione** e altro.
Se saltiamo il primo segnale di sincronizzazione possiamo decodificare il **primo byte** per capire il **tipo del primo NAL** del bitstream di esempio.
Ad esempio in questo caso il primo byte dopo la sincronizzazione è `01100111`, dove il primo bit (`0`) si riferisce al campo **forbidden_zero_bit**, i due bit successivi (`11`) indicano il campo **nal_ref_idc**, che stabilisce se il NAL è un campo di riferimento oppure no, e infine gli ultimi cinque bit (`00111`) sono il campo **nal_unit_type**, cioè il tipo di NAL, in questo caso **SPS** (7).
Il secondo byte (`bin=01100100, hex=0x64, dec=100`) di un NAL SPS è il campo **profile_idc**, che indica il profilo che è stato usato per la codifica. In questo esempio è stato usato il profilo ["Constrained High"](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles), che è un profilo "alto" senza il supporto agli slice di tipo B (bi-predittive).
Se leggiamo le specifiche del bitstream H.264 troveremo diversi valori per i campi **nome parametro**, **categoria** e **descrizione**. Ad esempio consideriamo i campi `pic_width_in_mbs_minus_1` e `pic_height_in_map_units_minus_1`.
| Nome parametro | Categoria | Descrizione |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: intero senza segno [codificato con Exp-Golomb](https://pythonhosted.org/bitstring/exp-golomb.html)
Facendo un po' di calcoli possiamo utilizzare il valore di questi campi per ricavare la **risoluzione**. Ad esempio una risoluzione di `1920 x 1080` può essere ottenuta dando al parametro `pic_width_in_mbs_minus_1` il valore di `119 ( (119 + 1) * dimensione_macroblocco = 120 * 16 = 1920)`. Abbiamo risparmiato spazio, memorizzando `119` anziché `1920`.
Se continuiamo a esaminare il video creato con un visualizzatore binario (es. `xxd -b -c 11 v/minimal_yuv420.h264`), possiamo saltare all'ultimo NAL che è il fotogramma vero e proprio.
Il valore dei primi 6 byte è `01100101 10001000 10000100 00000000 00100001 11111111`. Come abbiamo visto dal primo byte si può ricavare il tipo di NAL, in questo caso `00101`, che corrisponde a **Slice IDR (5)**. Continuiamo con l'ispezione.
Utilizzando le specifiche dell'header degli slice H.264 possiamo decodificare il tipo di slice (**slice_type**), il numero del fotogramma (**frame_num**), assieme ad altre importanti informazioni.
Per ottenere il reale valore di alcuni campi (`ue(v), me(v), se(v), te(v)`) dobbiamo decodificarli utilizzando un metodo speciale chiamato [Exponential-Golomb](https://pythonhosted.org/bitstring/exp-golomb.html), che è un **modo molto efficiente per codificare valori variabili**, specialmente quando vengono usati molti valori predefiniti.
> I valori **slice_type** e **frame_num** sono 7 (slice di tipo I) e 0 (primo fotogramma).
Possiamo vedere il **bitstream come un protocollo**, e se vuoi o devi imparare di più a proposito del bitstream fai riferimento alle [specifiche H.264 dell'ITU](http://www.itu.int/rec/T-REC-H.264-201610-I). Ecco un diagramma semplificato che mostra dove vengono memorizzati i dati dell'immagine vera e propria (YCbCr/YUV compresso).
Possiamo esplorare altri bitstream come [quello di VP9](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf) o anche il nostro **nuovo miglior amico** [**AV1**](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
). Si assomigliano? [No, ma una volta imparato uno capire gli altri è facile](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/).
> ### Esercizio: ispezionare il bitstream H.264
> Possiamo [generare un video con un singolo fotogramma](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) e usare [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) per ispezionare il suo bitstream H.264. Infatti puoi anche vedere il [codice sorgente che si occupa del parsing del bitstream H.264 (AVC)](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp).
>
> 
>
> In alternativa possiamo usare il software [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (è a pagamento ma esiste una versione di prova che limita l'analisi ai primi 10 fotogrammi).
>
> 
## Ripasso
Molti dei **codec moderni utilizzano lo stesso modello di codifica che abbiamo imparato**. Ad esempio, diamo un'occhiata al diagramma a blocchi del codec video Thor, che contiene tutti i passi che abbiamo studiato. A questo punto dovresti essere in grado almeno di comprendere meglio le innovazioni e gli articoli che riguardano questi argomenti.
Inizialmente abbiamo calcolato che ci servono [139 GB di spazio per archiviare un file di un'ora nel formato 720p30](#sottocampionamento-della-crominanza). Se applichiamo le tecniche imparate qui, come **la predizione intra-frame e inter-frame, la trasformata, la quantizzazione, la codifica dell'entropia**, assumendo che spendiamo **0,031 bit per pixel** possiamo raggiungere la stessa qualità percepita utilizzando soltanto **368 MB anziché 139 GB**.
> Abbiamo scelto di usare **0,031 bit per pixel** basandoci sull'esempio precedente.
## Come fa H.265 a comprimere più di H.264?
Ora che sappiamo come funziona un codec, risulta più facile capire come i nuovi codec sono in grado di memorizzare risoluzioni più alte usando meno bit.
Confronteremo AVC e HEVC, tenendo in mente che il processo è quasi sempre un compromesso tra la complessità dell'algoritmo (cicli di CPU) e il tasso di compressione.
HEVC ha **partizioni** (e **sottopartizioni**) più grandi e più numerose rispetto ad AVC, inoltre ha **più direzioni di predizione intra-frame**, una **migliore codifica dell'entropia**, e altro. Tutto insieme fa in modo che H.265 sia in grado di comprimere il 50% in più rispetto ad H.264.
# Streaming online
## Architettura generale
[TODO]
## Download progressivo vs streaming adattivo
[TODO]
## Protezione dei contenuti
Possiamo usare un semplice **sistema a token** (gettone) per proteggere i contenuti. Un utente senza token che prova a richiedere un video viene bloccato dalla CDN, mentre un utente con un token valido può riprodurre il contenuto. Funziona in modo abbastanza simile alla maggior parte dei sistemi di autenticazione sul web.
L'utilizzo di questo sistema consente comunque all'utente di scaricare e ridistribuire il video. Un sistema **DRM (digital rights management)** permette di evitare anche questo problema.
I sistemi di produzione nel mondo reale in genere usano entrambe le tecniche, per offrire sia autorizzazione che autenticazione.
### DRM
#### Sistemi principali
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### Cos'è?
DRM sta per *gestione dei diritti digitali* (*Digital Rights Management*) ed è un modo per **aggiungere una protezione del diritto d'autore ai contenuti digitali**, quindi anche al video e all'audio. Nonostante sia molto usato, [non è universalmente accettato](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### Perché?
I creatori di contenuti (principalmente gli studi) vogliono proteggere la proprietà intellettuale dalla copia, per prevenire la ridistribuzione non autorizzata dei contenuti digitali.
#### Come?
Descriveremo un modello generico ed astratto di DRM in un modo molto semplificato.
Sia dato un **contenuto C1** (es. uno streaming video HLS o DASH), con un **player P1** (es. shaka-clappr, exo-player o iOS) e un **dispositivo D1** (es. uno smartphone, una tv, un tablet o un computer), che utilizzano un **sistema DRM che chiamiamo DRM1** (es. Widevine, PlayReady, FairPlay).
Il contenuto C1 viene cifrato con una **chiave simmetrica K1** fornita dal sistema DRM1, così da ottenere il **contenuto cifrato C'1**.
Il player P1 del dispositivo D1 possiede due chiavi (asimmetriche), cioè una **chiave privata PRK1** (questa chiave è protetta1 e conosciuta solo da **D1**) e una **chiave pubblica PUK1**.
> **1protetta**: questa protezione può essere ottenuta sia **tramite hardware**, memorizzando la chiave all'interno di un chip speciale in sola lettura che agisce come una [scatola nera](https://it.wikipedia.org/wiki/Modello_black_box) per fornire la decifratura, sia **tramite software** (in modo meno sicuro). Il sistema DRM offre dei modi per sapere quale tipo di protezione il dispositivo ha a disposizione.
Quando il **player P1 vuole riprodurre il contenuto C'1**, deve contrattare con il **sistema DRM1**, fornendogli la sua chiave pubblica **PUK1**. Il sistema DRM1 ritorna la **chiave K1 cifrata** con la chiave pubblica **PUK1** del client. A livello teorico, questa risposta è qualcosa che **soltanto D1 è in grado di decifrare**.
`K1P1D1 = enc(K1, PUK1)`
Il player **P1** utilizza il suo sistema DRM locale (che potrebbe essere un [SoC](https://it.wikipedia.org/wiki/System-on-a-chip), una parte di software o hardware specializzata), **in grado di decifrare** il contenuto utilizzando la sua chiave privata PRK1. In questo modo può **decifrare la chiave simmetrica K1 da K1P1D1** e quindi riprodurre **C'1**. Nel migliore dei casi, le chiavi non sono esposte nella memoria RAM.
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# Come usare jupyter
Assicurati di avere **Docker installato** e avvia `./s/start_jupyter.sh`. Segui poi le istruzioni mostrate nel terminale.
# Conferenze
* [DEMUXED](https://demuxed.com/) - puoi [guardare le presentazioni degli ultimi 2 eventi](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# Riferimenti
Il contenuto più ricco è qua, puoi trovare le informazioni che hanno ispirato questo testo e da cui sono stati estratti i concetti. Puoi approfondire la tua conoscenza leggendo questi link, libri, video, ecc.
Corsi online e tutorial:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Libri:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Specifiche bitstream:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
Software:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
Codec non ITU:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml
* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
* https://jmvalin.ca/papers/AV1_tools.pdf
Concetti di codifica:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
* https://videoblerg.wordpress.com/2017/11/10/ffmpeg-and-how-to-use-it-wrong/
Sequenze video per i test:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
Varie:
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* http://www.csc.villanova.edu/~rschumey/csc4800/dct.html
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
================================================
FILE: README-ja.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
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# はじめに
これは、ビデオ技術に関するやさしい解説資料です。ソフトウェア開発者やエンジニアを対象にしていますが、**誰でも理解できる**解説にしたいと思っています。このアイディアは、[ビデオ技術初学者のためのミニワークショップ](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p)から生まれました。
できるだけ**簡潔な言葉、多くの視覚的要素、具体的な例**を使うことで、誰でもデジタルビデオの概念が理解できることを目標にしています。気軽に訂正や提案を送り、改善してください。
本資料には**ハンズオン**による解説が含まれています。ハンズオンに取り組む方は、事前にdockerをインストールし、このレポジトリをクローンしておいてください。
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **注意**: `./s/ffmpeg` や `./s/mediainfo` コマンドは、そのプログラムが**Dockerコンテナ上**で実行されることを意味しています。コンテナの中には、必要な依存関係が全て含まれています。
**ハンズオンはすべて、このレポジトリをクローンしたフォルダで実行してください**。 **jupyter examples**については、`./s/start_jupyter.sh`でサーバーを起動して、表示されるURLをブラウザで開いてください。
# 変更履歴
* DRMシステムの追加
* 1.0.0版のリリース
* 簡体字訳の追加
* FFmpeg oscilloscopeフィルターの例を追加
# 目次
- [はじめに](#はじめに)
- [目次](#目次)
- [基本用語](#基本用語)
* [カラー画像を符号化する別の方法](#カラー画像を符号化する別の方法)
* [ハンズオン: 画像と色の実験](#ハンズオン-画像と色の実験)
* [DVDの画面アスペクト比は4:3](#dvdの画面アスペクト比は43)
* [ハンズオン: ビデオプロパティを調べる](#ハンズオン-ビデオプロパティを調べる)
- [冗長性除去](#冗長性除去)
* [色、明るさと私たちの目](#色、明るさと私たちの目)
+ [カラーモデル](#カラーモデル)
+ [YCbCrとRGB間の変換](#ycbcrとrgb間の変換)
+ [クロマサブサンプリング](#クロマサブサンプリング)
+ [ハンズオン: YCbCrヒストグラムを調べる](#ハンズオン-ycbcrヒストグラムを調べる)
* [フレームの種類](#フレームの種類)
+ [Iフレーム (イントラ、キーフレーム)](#iフレーム-イントラ、キーフレーム)
+ [Pフレーム (予測)](#pフレーム-予測)
- [ハンズオン: Iフレームが１つだけのビデオ](#ハンズオン-iフレームが１つだけのビデオ)
+ [Bフレーム (双方向予測)](#bフレーム-双方向予測)
- [ハンズオン: Bフレーム付きのビデオとの比較](#ハンズオン-bフレーム付きのビデオとの比較)
+ [まとめ](#まとめ)
* [時間的冗長性 (インター予測)](#時間的冗長性-インター予測)
- [ハンズオン: 動きベクトルを見る](#ハンズオン-動きベクトルを見る)
* [空間的冗長性 (イントラ予測)](#空間的冗長性-イントラ予測)
- [ハンズオン: イントラ予測を調べる](#ハンズオン-イントラ予測を調べる)
- [ビデオコーデックの仕組み](#ビデオコーデックの仕組み)
* [何か? なぜ? どのように?](#何か-なぜ-どのように)
* [歴史](#歴史)
+ [AV1の誕生](#av1の誕生)
* [一般的コーデック](#一般的コーデック)
* [ステップ１ - 画像分割](#ステップ１---画像分割)
+ [ハンズオン: パーティションを調べる](#ハンズオン-パーティションを調べる)
* [ステップ２ - 予測](#ステップ２---予測)
* [ステップ３ - 変換](#ステップ３---変換)
+ [ハンズオン: 種々の係数を捨てる](#ハンズオン-種々の係数を捨てる)
* [ステップ４ - 量子化](#ステップ４---量子化)
+ [ハンズオン: 量子化](#ハンズオン-量子化)
* [ステップ５ - エントロピー符号化](#ステップ５---エントロピー符号化)
+ [可変長符号](#可変長符号)
+ [算術符号](#算術符号)
+ [ハンズオン: CABAC対CAVLC](#ハンズオン-cabac対cavlc)
* [ステップ６ - ビットストリームフォーマット](#ステップ６---ビットストリームフォーマット)
+ [H.264ビットストリーム](#h264ビットストリーム)
+ [ハンズオン: H.264ビットストリームを調べる](#ハンズオン-h264ビットストリームを調べる)
* [おさらい](#おさらい)
* [どのようにH.265はH.264よりも良い圧縮率を実現しているのか?](#どのようにh265はh264よりも良い圧縮率を実現しているのか)
- [オンラインストリーミング](#オンラインストリーミング)
* [一般的なアーキテクチャ](#一般的なアーキテクチャ)
* [プログレッシブダウンロードとアダプティブストリーミング](#プログレッシブダウンロードとアダプティブストリーミング)
* [コンテンツ保護](#コンテンツ保護)
- [jupyterの使い方](#jupyterの使い方)
- [カンファレンス](#カンファレンス)
- [参考文献](#参考文献)
# 基本用語
**画像**は、**二次元マトリクス**として考えることができます。さらに次元を増やして**三次元マトリクス**にすることで画像の色を表現することも可能です。
画像の色を[原色 (赤、緑、青)](https://ja.wikipedia.org/wiki/%E5%8E%9F%E8%89%B2)で表現すると、三つの平面を定義することになります。一つめが**赤**、二つ目が**緑**、そして三つ目が**青**です。
マトリクスのそれぞれの要素を**ピクセル** (画素)と呼びます。一つのピクセルはその色の**強度** (通常は数値)を表します。例えば、**赤色のピクセル**は、緑が0、青が0、赤が最大の強度により表現できます。**ピンク色のピクセル**も同様に3つの値で表現できます。0から255の数値で表現することにより、ピンクピクセルは**赤=255、緑=192、青=203**と定義できます。
> #### カラー画像を符号化する別の方法
> 色を表現する方法は、他にもたくさんあります。例えば、RGBモデルでは各ピクセルで3バイトを必要としますが、インデックスパレットは1バイトしか必要ありません。そういったモデルでは、色を表現するために三次元モデルを使わずに二次元モデルを使用できるでしょう。メモリを節約できますが、色の選択肢を狭めることになります。
>
> 
例えば、下の画像をみてください。1番左の画像は色付けされており、他の画像は赤、緑、青の強度を表す平面です(グレートーンで表示しています)。
**赤色**が最も多く使われていることが分かります(左から二番目の顔の最も明るい部分)。一方**青色** は服の一部と**マリオの目にしかみられません**(最後の顔) 。**マリオのひげ**に対しては、**どの色もあまり使われていない**(最も暗い部分)ことが分かります。
各色の強度は**ビット深度**とよばれる一定量のビットで表現されます。色(平面)ごとに**8ビット**(0から255の値で表現する)を使う場合、**24ビット**(8ビット x 3次元 R/G/B)の**色深度**を持つことになり、2の24乗種類の色を使えることが推測できます。
> [画像がどのように万物をビットとしてとらえるのか](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm)を学ぶと **良い**でしょう
もう一つの画像のプロパティは **解像度**です。解像度は長さあたりのピクセルの数です。解像度はよく幅 x 高さとして表現されます。例えば、下記は**4×4**の画像です。
> #### ハンズオン: 画像と色の実験
> [jupyter](#jupyterの使い方) (python、numpy、matplotlib、その他)を使って、[画像と色の実験](/image_as_3d_array.ipynb)をしましょう。
>
> [(エッジ検出, シャープ化, ぼかし等の)画像フィルタがどのように動くか](/filters_are_easy.ipynb)を学びましょう。
画像やビデオの作業をする時にみるもう一つのプロパティは **アスペクト比**です。アスペクト比は、画像やピクセルの幅と高さの比率を表します。
動画や画像が**16x9**であると言うときは、たいてい**画面アスペクト比 (DAR)** のことを指します。しかし、個々のピクセルを様々な形状にすることができ、これを **ピクセルアスペクト比 (PAR)** といいます。
> #### DVDの画面アスペクト比は4:3
> DVDの実際の解像度は704x480ですが、10:11のピクセルアスペクト比を持っているため、4:3のアスペクト比を保っています(704x10/480x11)。
最後に、**ビデオ**を**単位時間**内の***n*フレームの並び**として定義でき、もう一つの特性と見ることができます。*n*はフレームレートもしくは秒間フレーム数 (FPS)です。
ビデオを表すために必要な秒間あたりのビット数は**ビットレート**です。
> ビットレート = 幅 x 高さ x ビット深度 x フレームレート
例えば、圧縮を全く使わないなら、フレームレートが30で、ピクセルあたりが24ビットで、解像度が480x240のビデオは、**秒間あたり82,944,000ビット**もしくは82.944 Mbps (30x480x240x24)が必要です。
**ビットレート**がほとんど一定なら、固定ビットレート(**CBR**)と呼ばれます。ビットレートが変動するなら、可変ビットレート (**VBR**)と呼ばれます。
> このグラフはフレームが真っ黒の間はあまりビットを使わない、制約付きのVBRを示しています。
>
> 
黎明期に、技術者たちが**帯域幅を増やさずに**ビデオ画面で認識できるフレームレートを倍にする技術を思いつきました。この技術は**インターレース動画**として知られています。基本的には一つ目の「フレーム」で画面の半分を送り、次の「フレーム」で残りの半分を送ります。
今日では **プログレッシブスキャン技術**を使って画面に描画されます。プログレッシブは、動画を描画、保存、転送する手段の一つで、各フレームの全走査線を順番に描画します。
これで**画像**がどのようにデジタル表現されるのかが分かりました。**色**がどのように配置され、ビデオを表すのにどのくらいの**秒間あたりのビット**が必要なのか、それが固定なのか(CBR)可変なのか(VBR)、**解像度**と**フレームレート**、他にもインターレースやピクセルアスペクト比、その他のたくさんの用語を学びました。
> #### ハンズオン: ビデオプロパティを調べる
> [ffmpegやmediainfoを使って説明したプロパティのほとんどを調べる](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)ことができます。
# 冗長性除去
圧縮なしにビデオを扱うことは不可能であることを学びました。解像度が720pで30fpsの場合、**1時間のビデオ一つ**に**278GB*が必要**です。(PKZIP、Gzip、PNGで使われている)DEFLATE のような**可逆圧縮アルゴリズム一つを使うだけでは**必要な帯域幅を十分に削減**できない**ため、ビデオを圧縮する別の方法を見つける必要があるのです。
> * この数値は1280 x 720 x 24 x 30 x 3600 (幅、高さ、ピクセルあたりのビット数、フレームレート、秒単位での時間)を掛けることで算出しました
これを成し遂げるために、**私たちの視覚の仕組みを利用する**ことができます。私たちは色よりも明るさを見分けることが得意です。また、ビデオには少しの変化しかない多くの画像が含まれている**時間的繰り返し**があります。それぞれのフレームもまた、多くの領域では同じか似ている色が使われている**画像内の繰り返し**があります。
## 色、明るさと私たちの目
私たちの目は[色よりも明るさにより敏感](http://vanseodesign.com/web-design/color-luminance/)です。この画像をみてそれを自分自身で確認できます。
左側の**正方形Aと正方形B**の色は**同じ**であることが分からないとしても大丈夫です。私たちの脳は**色よりも明暗に注意をはらう**ことで錯覚を起こさせているのです。右側では、同じ色のコネクターがあるため、私たち（私たちの脳）は、それらは実際は同じ色であるということを簡単に気づきます。
> **私たちの目がどのように機能するかの簡単な説明**
>
> [眼は複雑な器官](http://www.biologymad.com/nervoussystem/eyenotes.htm)で、たくさんのパーツから成り立っていますが、主に錐体細胞と桿体細胞に関心があります。眼は [1億2000万の桿体細胞と600万の錐体細胞を含んでいる](https://en.wikipedia.org/wiki/Photoreceptor_cell)のです。
>
> **ものすごく簡単にする**ため、眼のパーツの機能のうち色と明るさに焦点を当てましょう。**[桿体細胞](https://en.wikipedia.org/wiki/Rod_cell)は主に明るさに対して責任を持っています**。一方 **[錐体細胞](https://en.wikipedia.org/wiki/Cone_cell)は色に対して責任を持っています**。異なった色素を持つ3種類の錐体があり、名前は[S錐体(青)、M錐体(緑)、L錐体(赤)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg)です。
>
> 私たちは錐体細胞(色)よりも多くの桿体細胞(明るさ)を持っているため、色よりも明暗をより識別することができることが推論できます。
>
> 
>
> **コントラスト感度特性 (Contrast sensitivity functions)**
>
> 実験心理学をはじめさまざまな分野の研究者が、人間の視覚について多くの理論を作り上げてきました。そのひとつが「コントラスト感度特性」です。コントラスト感度特性は光の時空間に関するものです。まず、観察者にある光を提示し、光を変化させていきます。この特性は、観察者が変化に気付いたと報告するまで、どれだけ光を変化させる必要があるかを示します。複数形の `functions` となっているのは、白黒だけでなく、カラーでも感度を測定できるためです。実験結果から、多くの場合、人間の目は色よりも明るさに敏感だとわかりました。
私たちは**輝度**(画像の明るさの度合い)により敏感であることが分かりました。この特性を利用しましょう。
### カラーモデル
**RGBモデル**を使って[カラー画像がどのように](#基本用語)機能するのか最初に学びましたが、他のモデルも存在します。実は、輝度(明るさ)と色度(色)を別にするモデルが存在します。それは**YCbCr***として知られています。
> * 同じ分離を行うモデルはもっと存在します。
このカラーモデルは明るさを表すために**Y**を使い、２つの色チャンネル**Cb** (青の色差)と**Cr** (赤の色差)を使います。[YCbCr](https://en.wikipedia.org/wiki/YCbCr)はRGBから生成することができ、RGBに戻すこともできます。下の写真のように、このモデルを使ってフルカラーの画像を作ることができます。
### YCbCrとRGB間の変換
中には **緑なしで色**の全てを生成できるのかと異議を唱える方もいるでしょう。
この質問に答えるために、RGBからYCbCrへの変換を一通り説明します。**[ITU-Rグループ*](https://en.wikipedia.org/wiki/ITU-R)** によって推奨される **[BT.601標準](https://en.wikipedia.org/wiki/Rec._601)** からの係数を使います。最初のステップは、**輝度を計算する**ことです。ITUに提案されている定数を使い、RGB値を置き換えます。
```
Y = 0.299R + 0.587G + 0.114B
```
輝度をえると次に、**色を分ける** (青の色差と赤の色差)ことができます。
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
それを**戻す**ことができ、**YCbCrを使って緑**を得ることもできます。
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * グループや標準はデジタルビデオではよくでてきます。彼らは何が標準なのかを定義します。例えば[4Kとは何か？どのフレームレート、解像度、カラーモデルを使うべきか？](https://en.wikipedia.org/wiki/Rec._2020)などです。
一般的に**ディスプレイ** (モニター、テレビ、スクリーン等) は様々な方法で構成された**RGBモデルだけ**を利用します。下の写真でいくつか拡大したもの示しています。
### クロマサブサンプリング
画像は輝度と色度成分で表現することができるので、人間の視覚システムが色度よりも輝度に敏感であることを利用して情報を選択的に削減することができます。**クロマサブサンプリング**は**色度の解像度を輝度の解像度より小さくする**画像符号化技術です。
色度の解像度をどのくらい小さくすべきでしょうか？！解像度と結合 (`最終的な色 = Y + Cb + Cr`)の仕方をどのようにするかを定義したいくつかの方式がすでに存在しています。
これらの方式はサブサンプリングシステムとして知られ、３つの部分からなる比`a:x:y`として表現されます。これは `a x 2`ブロックの輝度ピクセルに対する色度解像度を定義しています。
* `a`：横方向のサンプルの基本数。通常は４。
* `x`：1ライン目の`a`に現れる色信号サンプルの数。(`a`に対する水平解像度)
* `y`：1ライン目と2ライン目での変化数
> 4:1:0は例外で、`4 x 4`ブロックの輝度に１つの色信号サンプルだけを含んでいます。
現代のコーデックでよく使われる方式は**4:4:4(サブサンプリング無し)**、**4:2:2、4:1:1、4:2:0、4:1:0、3:1:1**です。
> **YCbCr 4:2:0結合**
>
> これがYCbCr 4:2:0を使って結合された画像の断片です。１ピクセルに１２ビットしか使わないことに注意してください。
>
> 
クロマサブサンプリングの主要な形式で符号化された同じ画像を見て下さい。一行目の画像は最終的なYCbCrで、二行目の画像は色度解像度を示しています。実に小さな劣化で素晴らしい結果です。
[解像度が720pで30fpsのビデオを1時間ファイルに保存するためには278GBの領域](#冗長性除去)が必要になることを先に計算しました。**YCbCr 4:2:0**を使うと、**半分のサイズ(139 GB)***にすることができます。しかしまだ理想には程遠いです。
> * 幅、高さ、ピクセルごとのビット数、フレームレートを掛けることによりこの値を計算しました。先に計算した時は２４ビット必要でしたが、今は１２ビットしか必要ありません。
> ### ハンズオン: YCbCrヒストグラムを調べる
> [ffmpegでYCbCrヒストグラムを調べる](/encoding_pratical_examples.md#generates-yuv-histogram)ことができます。 このシーンでは青の寄与率がより高くなっています。 [ヒストグラム](https://en.wikipedia.org/wiki/Histogram)でそのことが示されます。
>
> 
### 色、輝度、明るさ、ガンマについての映像
輝度や明るさ、ガンマ、色について解説している素晴らしい動画があります。ぜひご覧ください。
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
> ### ハンズオン: YCbCr 強度を調べる
> [FFmpegのoscilloscope filter](https://ffmpeg.org/ffmpeg-filters.html#oscilloscope)を使って、映像のYの強度を可視化できます。
> ```bash
> ffplay -f lavfi -i 'testsrc2=size=1280x720:rate=30000/1001,format=yuv420p' -vf oscilloscope=x=0.5:y=200/720:s=1:c=1
> ```
> 
## フレームの種類
次に**時間的な冗長性**の削減を試みましょう。しかし、その前にいくつかの基本的用語について押さえておきましょう。30 fpsの動画があり、下記が最初の4フレームであるとします。
  
**青い背景**のようにフレーム間に**たくさんの繰り返し**を見ることができます。背景はフレーム0からフレーム3まで変化しません。この問題に取り組むために、これらを3種類のフレームに**抽象的に分類**しましょう。
### Iフレーム (イントラ、キーフレーム)
Iフレーム(参照、キーフレーム、イントラ)は**自己完結的なフレーム**です。Iフレームは他に依存せずに描画することができ、静止画と似ています。最初のフレームは普通はIフレームですが、他の種類のフレームの間にも規則的にIフレームが挿入されています。
### Pフレーム (予測)
現在の画像は、ほとんど毎回**１つ前のフレームを使って描画する**ことができるという事実をPフレームは利用しています。例えば、2番目のフレームでは、ボールが前に動いたという変化しかありません。**1つ前のフレームを参照し、そのフレームとの差分だけを用いてフレーム1を再構築**できます。
 <- 
> #### ハンズオン: Iフレームが１つだけのビデオ
> Pフレームはより小さなデータしか使わないので、全体を[Iフレームは１つだけで、他は全てPフレームのビデオ](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)にエンコードしたらどうでしょう？
>
> このビデオをエンコードした後、再生してビデオの**前方にシーク**してください。シーク先に移るのに**時間がかかる**ことに気づくでしょう。これは、描画のために**Pフレームが参照フレームを必要とする** (例えばIフレーム)ためです。
>
> もう１つ手軽にできるテストとして、まず１つのI-Frameだけを使ってビデオをエンコードして、次に[２秒間隔でIフレームを挿入してエンコード](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second)してから、**それぞれのエンコード結果のサイズを比較**してください。
### Bフレーム (双方向予測)
過去と未来のフレーム両方を参照したら、さらに高い圧縮率を得ることができるのではないでしょうか？！それがBフレームの基本です。
 <-  -> 
> #### ハンズオン: Bフレーム付きのビデオとの比較
> ２つの方法でエンコードしてみましょう。１つはBフレーム付きで、もう一方は[Bフレームなし](/encoding_pratical_examples.md#no-b-frames-at-all)でエンコードして、ファイルサイズおよび画質を確認してください。
### まとめ
これらの異なる種類のフレームは**より高い圧縮率を得る**ために使われます。次の節でどうやってこれが行われるのか見ていきます。しかし、今のところは**Iフレームは重く、Pフレームは比較的軽いですが、最も軽いのはBフレーム**と考えておいてよいでしょう。
## 時間的冗長性 (インター予測)
**時間的繰り返し**を削減するために何ができるか見ていきましょう。この種の冗長性は**インター予測**という技術で解決することができます。
**できるだけ少ないビットを使って**、連続したフレーム0とフレーム1をエンコードしてみましょう。
１つできることとして、引き算があります。単純に**フレーム0からフレーム1を引く**と、**エンコード**する必要がある**差分**を得ることができます。
しかし、さらに少ないビットしか使わない**もっとよい方法**があると言ったらどうでしょう！まず、`フレーム0`をきちんと定められた区画の集合であるとします。そして`フレーム0`のそれぞれのブロックを`フレーム1`上にマッチさせようとします。.これは **動き推定**として考えることができます。
> ### Wikipedia - ブロック動き補償
> "**ブロック動き補償**は現在のフレームを重ならないブロックに分けて、動き補償ベクトルは**これらのブロックがどこからのものかを表します** (前回のフレームが重ならないブロックに分けられ、動き補償ベクトルはこれらのブロックがどこへ行くかを表すというのは、よくある誤解です)。 元のブロックは元のフレーム内で通常は重なり合います。ビデオ圧縮アルゴリズムの中には、 すでに送信された異なったいくつかのフレームの断片から現在のフレームを組み立てるものもあります。"
ボールが`x=0、y=25`から`x=6、y=26`へ移動したと推定することができます。**x**と**y**の値は**動きベクトル**です。ビットを節約するためのもう１つの**さらなるステップ**は、前回のブロック位置と予測されるブロック位置との**動きベクトルの差分だけをエンコードする**ことです。 最終的な動きベクトルは`x=6 (6-0)、y=1 (26-25)`のようになります。
> 実際には、この**ボールは分割されてn個の区画にまたがるでしょう**。しかし処理は同じです
フレーム上の物体は**3D空間で移動します**。ボールは背景の方に動くと小さくもなります。 **完全にマッチするブロックを見つけられない**ことはよくあります。 推定画像と実際の画像を重ねると下記のようになります。
しかし、**動き推定**を適用すると、単純なフレーム差分手法を使うよりも**エンコードするデータがより小さくなる**ことが分かります。
> ### 可視化された実際の動き補償
> この手法は全てのブロックに適用され、高い確率でボールは１つ以上のブロックに配置されます。
> 
> 引用元: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
[jupyterを使ってこれらの概念を実験](/frame_difference_vs_motion_estimation_plus_residual.ipynb)できます。
> #### ハンズオン: 動きベクトルを見る
> [ffmpegでインター予測 (動きベクトル)付きのビデオを生成する](/encoding_pratical_examples.md#generate-debug-video)ことができます。
>
> 
>
> または、[Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (有料ですが、最初の10フレームに制限された無料お試し版もあります)を使うこともできます。
>
> 
## 空間的冗長性 (イントラ予測)
ビデオの中の**1つ1つのフレーム**を分析すると、**たくさんの相関性のある領域**が存在することが分かります。
例を通してみていきましょう。このシーンはほとんど青と白で構成されています。
これは `Iフレーム`で、予測のために**前のフレームを使えません**が、圧縮することはできます。赤いブロックを選択してエンコードしてみましょう。**隣接する部分を見る**と、**その周りの色に傾向**があることを**推定**することができます。
このフレームでは**色が垂直方向に広がり**続けることを**予測**してみます。**未知のピクセルの色が隣接するピクセルの色を持つ**ことを意味します。
**予測が間違うかもしれません**。そのため、この手法(**イントラ予測**)を適用してから、**実際の値を引いて**差分を生成する必要があります。その差分は予測前よりもさらに圧縮しやすいマトリクスになります。
他にも様々な予測方法があります。ここで紹介した方法は、直線状に並んだ領域の予測を行い、ブロックの上の行のピクセルをブロック内の各行にコピーしていました。ここにさらに角度成分を加え、左隣のピクセルの値も利用してブロック内のピクセル値を予測する方法もあります。また左隣と上隣のピクセル値の平均を利用する、DC予測という方法もあります。
> #### ハンズオン: イントラ予測を調べる
> [ffmpegでマクロブロックとそれらの予測付きのビデオを生成する](/encoding_pratical_examples.md#generate-debug-video)ことができます。[それぞれのブロックの色の意味](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes)を理解するためにffmpegのドキュメントを調べてください。
>
> 
>
> または[Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (有料ですが、最初の10フレームに制限された無料お試し版もあります)を使うことができます。
>
> 
# ビデオコーデックの仕組み
## What? Why? How?
**What?** デジタルビデオを圧縮、解凍するソフトウェア / ハードウェアの部品。**Why?** 制限された帯域とストレージの下でより高い品質のビデオへの要求が、市場と社会で高まっているため。毎秒30フレーム、ピクセルあたり24ビット、解像度が480x240のビデオに[必要な帯域を計算](#基本用語)したのを覚えていますか。圧縮なしでは**82.944 Mbps**でした。テレビやインターネットでHD/FullHD/4Kを配信するためには圧縮するしかありません。**How?** ここで主な技法について簡単に見ていきます。
> **コーデック 対 コンテナ**
>
> 初心者がよく誤解することの１つに、デジタルビデオコーデックと[デジタルビデオコンテナ](https://en.wikipedia.org/wiki/Digital_container_format)を混同するというものがあります。**コンテナ**はビデオ(と音声もありえる)のメタデータとペイロードである**圧縮されたビデオ**を包括するラッパーフォーマットとして考えることができます。
>
> たいてい、ビデオファイルの拡張子はそのビデオコンテナを定義します。例えば、ファイル`video.mp4`はおそらく **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14)** コンテナで、`video.mkv`という名前のファイルはおそらく **[matroska](https://en.wikipedia.org/wiki/Matroska)** です。コーデックとコンテナフォーマットを確実に調べるためには、[ffmpegかmediainfo](/encoding_pratical_examples.md#inspect-stream)が使えます。
## 歴史
一般的なコーデックの内部動作に入って行く前に、いくつかの古いビデオコーデックについて少し理解するために過去を振り返ってみましょう。
ビデオコーデックである[H.261](https://en.wikipedia.org/wiki/H.261)は1990年(厳密には1988年)に生まれました。H.261は**64 kbit/sのデータレート**で動作するように設計されました。クロマサブサンプリングやマクロブロックなどの考えをすでに使っていました。1995年に、**H.263**ビデオコーデック標準が発表され2001年まで拡張され続けました。
2003年に**H.264/AVC**の初版が完成しました。同じ年に **On2 Technologies** (旧Duck Corporation)という会社が、**ロイヤリティーフリー**で非可逆ビデオ圧縮の **VP3**と呼ばれるビデオコーデックをリリースしました。2008年にこの会社を**Googleが買収**し、同じ年に**VP8**をリリースしました。2012年の12月にGoogleが**VP9**をリリースしました。VP9は(モバイルを含む)**ブラウザ市場のおよそ¾でサポートされています**。
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)**は新しい**ロイヤリティーフリー**でオープンソースのビデオコーデックで、[Alliance for Open Media (AOMedia)](http://aomedia.org/)によって設計されました。AOMediaは**複数の会社: Google、Mozilla、Microsoft、Amazon、Netflix、AMD、ARM、NVidia、Intel、Cisco**と他のいくつかの会社から成り立っています。リファレンスコーデックの**初版** 0.1.0が**2016年4月7日に公開されました**。
> #### AV1の誕生
>
> 2015年の初期にGoogleが[VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1)の開発、Xiph (Mozilla)は[Daala](https://xiph.org/daala/)の開発、Ciscoはオープンソースでロイヤリティーフリーの[Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03)と呼ばれるビデオコーデックの開発に取り組んでいました。
>
> MPEG LAは、当初はHEVC (H.265)の年間ロイヤリティーの上限とH.264の8倍高いライセンス料を発表しましたが、すぐにルールを変更しました。:
> * **年間ロイヤリティーの上限なし**
> * **コンテンツ料金** (収入の0.5%)
> * **h264より10倍高い単位あたり料金**
>
> [alliance for open media](http://aomedia.org/about/)はハードウェアメーカー(Intel、AMD、ARM、Nvidia、Cisco)、コンテンツ配信 (Google、Netflix、Amazon)、ブラウザ開発(Google, Mozilla)、その他の会社によって作られました。
>
> これらの会社にはロイヤリティーフリーのビデオコーデックという共通の目的があり、AV1はより [簡単な特許ライセンス](http://aomedia.org/license/patent/)で誕生しました。**Timothy B. Terriberry**が [AV1の概念、ライセンスモデル、現状](https://www.youtube.com/watch?v=lzPaldsmJbk)についての素晴らしいプレゼンテーションを行いました。この節はこのプレゼンテーションを元に書いています。
>
> **ブラウザーを使ってAV1コーデックを分析**できることを知って驚くことでしょう。https://arewecompressedyet.com/analyzer/ を見てください。
>
> 
>
> 追記: コーデックの歴史についてもっと学びたいなら、背後にある[ビデオ圧縮の特許](https://www.vcodex.com/video-compression-patents/)の基本を学ばなくてはなりません。
## 一般的コーデック
**一般的なビデオコーデックの背後にある主な機構**を紹介していきますが、これらの概念のほとんどは VP9、AV1、HEVCのような最新のコーデックでも役に立ち、使われています。物事をかなり単純にして説明することを理解してください。ときどき実際の例(だいたいはH.264)を使って、技法のデモを行います。
## ステップ１ - 画像分割
最初のステップは、いくつかの**パーティション、サブパーティション**、もっと細かい単位に**フレームを分割**することです。
**しかしなぜ?** たくさんの理由があります。例えば、画像を分割すると小さなパーティションを動きのある小さな部分に使い、より大きなパーティションを静的な背景に使って、予測をより正確に行うことができます。
通常、コーデックは、スライス(もしくはタイル)、マクロ(もしくは符号ツリーユニット)やたくさんのサブパーティションで **パーティションを構成します**。これらのパーティションの最大サイズは様々で、HEVCでは64x64、AVC16x16ですが、サブパーティションは4x4までです。
**フレームはいくつかの形式に分けられている**のを学んだことを覚えていますか？！さて、**これらのアイデアをブロックに適用する**こともできます。つまりIスライス、Bスライス、Iマクロブロックなどを持つことができます。
> ### ハンズオン: パーティションを調べる
> [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (有料ですが、最初の10フレームに制限された無料お試し版もあります)を使うこともできます。これが分析された[VP9パーティション](/encoding_pratical_examples.md#transcoding)です。
>
> 
## ステップ２ - 予測
パーティションに分割すると、それらについて予測を行うことができます。[インター予測](#時間的冗長性-インター予測)のために、**動きベクトルと差分を送信する**必要があり、また[イントラ予測](#空間的冗長性-イントラ予測)のために、**予測方向と差分を送信する**必要があります。
## ステップ３ - 変換
差分ブロック (`予測パーティション - 実際のパーティション`)を生成した後、それを**変換**することで**画質をある程度**保ったまま、どの**ピクセルを捨てられるか**が分かるようになります。それにはいくつかの変換が存在します。
[他の変換](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms)もありますが、離散コサイン変換(DCT)をしっかり見ていきます。[**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform)の主な特徴は:
* **ピクセル**ブロックを同じサイズの**周波数係数**ブロックに**変換する**。
* エネルギーを**偏らせて**、空間的冗長性を削減しやすくする。
* **元に戻せる**、またはピクセルに戻せる
> 2017年2月2日にCintra, R. J.とBayer, F. Mが[14加算のみの画像圧縮用DCT近似変換](https://arxiv.org/abs/1702.00817)という論文を発表しました。
上記の箇条書きの利点を全て理解しなかったとしても心配いりません。その本当の価値を見い出すためにいくつかの実験を試みます。
次の**ピクセルのブロック**(8x8)を例にとりましょう:
これはブロック画像(8x8)を描画します:
このピクセルのブロックに**DCTを適用する**と**係数のブロック** (8x8)を得ます:
この係数のブロックを描画すれば、次の画像が得られるでしょう:
元の画像とは似ても似つかないことが分かり、**最初の係数**は他の係数とは大きく異なっていることにお気づきかもしれません。この最初の係数はDC係数として知られ、入力配列の**サンプル全体**を表します。**平均に似た**何かです。
この係数のブロックは高周波数成分を低周波数成分から切り離すという面白い特性を持っています。
画像では、**エネルギーのほとんど**は[**低周波**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm)に集中されます。それで画像を周波数成分に変換して**高周波数係数を捨て**れば、画質をそれほど犠牲にせずに画像を表現するのに必要な**データ量を削減**できます。
> 周波数は信号がどれだけ速く変化するかを意味します。
では得られた知識を使って、元の画像をDCTを使って周波数(係数のブロック)に変換して、もっとも重要でない係数の部分を捨てる実験をしてみましょう。
まず、画像を**周波数領域**に変換します。
次に、係数の一部(67%)を捨てます。捨てるのはほとんどは右下の部分です。
最後に、この一部が捨てられた係数のブロックから画像を再生成し(元に戻せる必要があることを覚えておいてください)、元の画像と比較します。
この画像は元の画像と似ていますが、多くの相違点が発生しています。**67.1875%を捨てました**が、 少なくとも元の画像に似ている画像を得ることができました。より賢い方法で係数を捨てて、もっと画質を良くすることもできたのですが、それは次のトピックで扱います。
> **それぞれの係数は画素全体を使って形成される**
>
> それぞれの係数は、１つの画素に直接マッピングしているわけではなく、画素全体の重み付き合計であることに留意することは重要です。下記の素晴らしいグラフは、1番目と2番目の係数が、それぞれのインデックスで異なる重みを使って、どのように計算されるかを示しています。
>
> 
>
> 原典: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> DCT基底ごとの[単純な画像の形成を見てDCTを視覚化する](/dct_better_explained.ipynb)こともできます。例えば、下記はそれぞれの係数の重みを使って[１つの文字が形成されていく](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT)過程です。
>
> 
> ### ハンズオン: 種々の係数を捨てる
> [DCT変換](/uniform_quantization_experience.ipynb)を実験しましょう。
## ステップ４ - 量子化
前のステップ (変換)で係数をいくつか捨てるときに、量子化のようなものを行いました。このステップでは、捨てる情報(**損失部分**)を選びます。単純な言葉でいうと、**圧縮を成し遂げるために係数を量子化**します。
どのように係数のブロックを量子化できるでしょうか？１つの単純な方法は、均一量子化でしょう。ブロックを**単一値** (10) **で割り**、小数点を切り捨てます。
どのようにこの係数のブロックを**元に戻せる**(再量子化)でしょうか？**同じ値** (10)**を掛ける**ことにより戻すことができます。
この**やり方は最適な方法ではありません**。それぞれの係数の重要度を考慮していないからです。 単一値の代わりに**量子化マトリクス**を使うことができます。このマトリクスでDCTの特性を活かすことができます。右下を一番量子化して、左上はあまり量子化しません。[JPEGは似たやり方を使っています](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html)。[ソースコード上でこのマトリクスを見る](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40)ことができます。
> ### ハンズオン: 量子化
> [量子化](/dct_experiences.ipynb)を実験しましょう。
## ステップ５ - エントロピー符号化
データ(画像 ブロック/スライス/フレーム)を量子化した後、さらに可逆圧縮することができます。データ圧縮のためのたくさんの方法(アルゴリズム)が存在します。それらのいくつかを簡単に体験していきます。より深い理解のためには、この素晴らしい本[Understanding Compression: Data Compression for Modern Developers](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/)を読むとよいでしょう。
### 可変長符号:
記号のストリームを持っているとします: **a**、**e**、**r**、**t**とそれらの確率(0から1)がこのテーブルで表されています。
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| 確率 | 0.3 | 0.3 | 0.2 | 0.2 |
もっとも確率が高いものには(より小さな)ユニークなバイナリコードを、もっとも確率が低いものにはより大きなバイナリコードを割り当てることができます。
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probability | 0.3 | 0.3 | 0.2 | 0.2 |
| binary code | 0 | 10 | 110 | 1110 |
ストリーム **eat**を圧縮してみましょう。それぞれの記号に8ビット使うと、圧縮なしで**24ビット**使うことになります。しかし、それぞれの記号をそのコードで置き換えると、スペースを節約できます。
まず記号**e**を符号化して`10`になります。２つ目の記号**a**を符号化すると、足されて(算数の方法ではなく) `[10][0]`となります。最後に３つ目の記号**t**を符号化すると、最終的な圧縮されたビットストリームは `[10][0][1110]`もしくは`1001110`となり、(もとより3.4倍小さなスペースである)**7ビット**しか使いません。
それぞれのコードはユニークな接頭符号を持つ必要があることに注意してください [ハフマンがこれらの数字を見つけることを助けてくれます](https://en.wikipedia.org/wiki/Huffman_coding)。いくつかの問題がありますが、この方法は[いくつかのビデオコーデックでまだサポート](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding)しています。これは圧縮を必要とする多くのアプリケーションに有用なアルゴリズムです。
エンコーダーとデコーダーの両方がそのコードの記号テーブルを**知らなくてはいけません**。そのためテーブルも送信する必要があります。
### 算術符号:
記号: **a**, **e**, **r**, **s**, **t** のストリームを持っていて、それらの確率がこの表によって表されると仮定しましょう。
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| probability | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
この表を考慮に入れて、出現順にソートされた全ての可能な記号を含む範囲グラフを作ります。
さて、ストリーム **eat**を符号化してみましょう。最初の記号**e**を取り上げます。それは**0.3以上0.6未満**に位置しています。この部分範囲を取り上げ、それを再び同じ割合で分割します。
ストリーム**eat**の符号化を続けましょう。次に記号**a**を取り上げます。これは**0.3以上0.39未満**に位置しています。そして、最後の記号 **t**を取り上げます。もう一度同じ処理を行うと**0.354以上0.372未満**という最終的な範囲を得ます。
最終範囲**0.354以上0.372未満**から１つの数値を取り上げる必要があります。**0.36**を取り上げましょう。しかしこの部分範囲内ならどんな数値を選んでもかまいません。この数値**だけで**、元のストリーム**eat**を復元することができます。これは、ストリームを符号化する為に範囲の範囲に線を引くかのように捉えることができます。
**逆過程** (別名 復号化) は同様に簡単で、数値**0.36**と元の範囲を使って、同じ処理を行い、この数値から符号化されたストリームを明らかにします。
最初の範囲で、その数値が１つの部分に一致し、それが最初の記号であることに気づきます。それから、その部分範囲を以前行ったようにまた分割します。すると**0.36**が記号**a**に合うことがわかり、同じ処理を繰り返すと、最後の記号である**t**を見つけます(符号前のストリーム*eat*を形成します)。
符号化と復号化の両方は、記号確率テーブルを**知る必要があります**。それでテーブルを送信する必要があります。
素晴らしいですよね。人々は本当に賢く、このような解決策を生み出しました。いくつかの[ビデオコーデック](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding)はこの技法を使っています。(もしくは少なくともオプションとして提供しています)。
この考えは、量子化ビットストリームを可逆圧縮する為のものです。間違いなくこの記事では伝えきれていない詳細、理由、トレードオフ等が山のようにあります。しかし、読者はデベロッパーとして[もっと学ぶべきです](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/)。より新しいコーデックは別の[ANSのようなエントロピー符号化アルゴリズム](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)を使おうとしています。
> ### ハンズオン: CABAC対CAVLC
> [１つはCABAC、もう一方はCAVLCを使って２つのストリームを生成](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc)し、生成する為にかかる**時間と最終的なサイズを比較**してみましょう。
## ステップ６ - ビットストリームフォーマット
これらのステップ全てを行った後、**圧縮されたフレームとこれらのステップまでのコンテキストを一つにまとめる**必要があります。 **エンコーダによってなされた決定**をデコーダへ明示的に知らせる必要があります。ビット深度、色空間、解像度、予測情報（動きベクトル、イントラ予測方向）、プロファイルレベル、フレームレート、フレームタイプ、フレーム数、その他多数です。
H.264ビットストリームについて表面的に学んでいきます。最初のステップは[最小限のH.264 *ビットストリームを生成する](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream)ことです。このレポジトリと[ffmpeg](http://ffmpeg.org/)を使って、これができます。
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * ffmpegは、デフォルトで**SEI NAL**として符号化された全パラメータを加えます。NALが何であるかは後ほど定義します。
このコマンドは、**単一フレーム**、64x64、色空間がyuv420、次の画像をフレームとして使っている生のh264ビットストリームを生成します。
> 
### H.264ビットストリーム
AVC (H.264)標準は、情報が **[NAL (Network Abstraction Layer)](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** と呼ばれる **マクロフレーム** (ネットワーク的には)で送信されることを定義しています。NALの主な目的は、"ネットワークフレンドリー"なビデオ表現を提供することです。この標準は、TV (ストリームベース)、インターネット(パケットベース)やその他で動作しなければなりません。
NALユニットの境界を定義する **[同期マーカー](https://en.wikipedia.org/wiki/Frame_synchronization)**があります。それぞれの同期マーカーは、最初は`0x00 0x00 0x00 0x01`で、それ以降は`0x00 0x00 0x01`の値を持ちます。もし生成されたh264ビットストリーム上で**16進ダンプ**を行えば、ファイルの最初の方に、少なくとも３つのNALを見つけることができます。
先に述べたとおり、デコーダは画像データだけではなく、動画、フレーム、色、使用されたパラメータ、その他の詳細を知る必要があります。それぞれのNALの**最初のバイト**は、そのカテゴリと**タイプ**を定義します。.
| NAL タイプID | 説明 |
|--- |---|
| 0 | 未定義 |
| 1 | 非IDRピクチャの符号化スライス |
| 2 | 符号化スライスデータパーティション A |
| 3 | 符号化スライスデータパーティション B |
| 4 | 符号化スライスデータパーティション C |
| 5 | IDRピクチャの**IDR**符号化スライス |
| 6 | **SEI** 付加拡張情報 |
| 7 | **SPS** シーケンスパラメータセット |
| 8 | **PPS** ピクチャパラメータセット |
| 9 | アクセスユニットデリミター |
| 10 | シーケンスの最後 |
| 11 | ストリームの最後 |
| ... | ... |
通常、ビットストリームの最初のNALは**SPS**です。このNALの種類は、**プロファイル**、**レベル**、**解像度**やその他の汎用エンコーディング変数を知らせる役割を持ちます。
最初の同期マーカーを飛ばすと、**最初のバイト**を復号化して**NALのタイプ**が何かを知ることができます。
例えば、同期マーカーの最初のバイトは`01100111`です。最初のビット (`0`)は**forbidden_zero_bit**フィールドで、次の2ビット(`11`)は**nal_ref_idc**フィールドで、このNALが参照フィールドかどうかを示します。残りの5ビット (`00111`)は**nal_unit_type**フィールドで、この例では**SPS** (7) NALユニットです。
SPS NALの２バイト目(`２進数=01100100、１６進数=0x64、１０進数=100`)は**profile_idc**フィールドで、エンコーダが使ったプロファイルを示します。この例では、 **[制約付きハイプロファイル](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)** を使っています。これはB (双方向予測)スライスをサポートしないハイプロファイルです。
SPS NALについてH.264ビットストリーム仕様を読むと、**パラメータ名**、**カテゴリ**、**説明**の表に多くの値を見つけるでしょう。例えば、`pic_width_in_mbs_minus_1`と`pic_height_in_map_units_minus_1`フィールドについて見てみましょう。
| パラメータ名 | カテゴリ | 説明 |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: 符号なし整数 [Exp-Golomb-coded](https://ghostarchive.org/archive/JBwdI)
これらのフィールドの値に対してある計算をすると、**解像度**を得ることができます。`1920 x 1080`を`pic_width_in_mbs_minus_1`が`119 ( (119 + 1) * macroblock_size = 120 * 16 = 1920) `として表現することができます。空間をさらに節約するために、`1920`を符号化する代わりに、`119`を使っています。
生成されたビデオをバイナリビュー (例えば: `xxd -b -c 11 v/minimal_yuv420.h264`)で検査し続けると、最後のNALまで飛ばすことができます。それはフレーム自体です。
最初の６バイトの値を見ましょう: `01100101 10001000 10000100 00000000 00100001 11111111`。すでに知ってる通り、最初のバイトでNALが何かを知ることができます。この例では、(`00101`)で **IDRスライス (5)** です。さらに検査してみます:
仕様の情報を使い、スライスのタイプ (**slice_type**)、フレーム番号(**frame_num**)や他の重要なフィールドを復号することができます。
いくつかのフィールドの値を得るために(`ue(v)、me(v)、se(v)、te(v)`)、それを[Exponential-Golomb](https://ghostarchive.org/archive/JBwdI)と呼ばれる特別なデコーダーを使って、デコードする必要があります。この方法は、デフォルト値が多いケースではたいてい、**変数値を符号化するのにとても効率的**です。
> このビデオの**slice_type**と**frame_num**の値は7 (Iスライス)と0 (最初のフレーム)です。
**ビットストリームをプロトコルとして**見ることができます。このビットストリームについてもっと学びたい、もしくは学ぶ必要があるなら、[ITU H.264 spec.]( http://www.itu.int/rec/T-REC-H.264-201610-I)を参照してください。下記はマクロ図表で、ピクチャデータ(圧縮YUV)がどこに位置するかを示しています。
[VP9ビットストリーム](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf)や[H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf)や、さらには**新しいベストフレドである** [**AV1** ビットストリーム](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8)のビットストリームを探索することができます。[それらはみんな似てますか？いいえ](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/)、しかし1つを学ぶと、他は簡単に理解できます。
> ### ハンズオン: H.264ビットストリームを調べる
> [単一フレームのビデオを生成](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video)し、[mediainfo](https://en.wikipedia.org/wiki/MediaInfo)を使ってH.264ビットストリームを検査してみましょう。実際、[h264 (AVC)ビットストリームをパースするソースコード](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp)を見ることもできます。
>
> 
>
> [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)を使うこともできます。有料ですが、最初の10フレームに制限された無料お試し版もあり、学習目的としては問題ありません。
>
> 
## おさらい
多くの**現代のコーデックが、これまで学んできた同じモデルを使っている**ことに気づくでしょう。実際のビデオコーデックのブロック図をみてみましょう。これは学んできた全てのステップを含んでいます。少なくともコーデック関連の発明や文献についてより理解することができるようになったことになります。
まず[720p解像度で30fpsで1時間のビデオファイルを保存するのに139GBのストレージ](#クロマサブサンプリング)が必要になることを計算しました。ここで学んだ技法つまり**インター予測、イントラ予測、変形、量子化、エントロピー符号化、その他**を駆使して、**ピクセルあたり0.031ビット**で表現できると仮定すると、**139GBに対したった367.82MBだけ**で同じ知覚画質のビデオを保存できます。
> 今回使用したビデオを元に**ピクセルあたり0.031ビット**が必要になることを導きました。
## どのようにH.265はH.264よりも良い圧縮率を実現しているのか?
今、コーデックの仕組みについてより理解しています。そのため、新しいコーデックがより高い解像度をより少ないビットでどのように配信できるのか簡単に理解できます。
AVCとHEVCを比較してみましょう。より多くのCPUサイクル(複雑さ)と圧縮率は、ほとんどいつでもトレードオフであることを心に止めておきましょう。
HEVCはAVCに比べて、より大きく、より多くの**パーティション** (と **サブパーティション**)のオプションを持っています。そしてより多くの**方向性イントラ予測や角度イントラ予測**、**改善されたエントロピー符号化**やその他を持っています。これら全ての改良のおかげで、H.265はH.264に比べて50%以上の圧縮をすることができるのです。
# オンラインストリーミング
## 一般的なアーキテクチャ
[TODO]
## プログレッシブダウンロードとアダプティブストリーミング
[TODO]
## コンテンツ保護
**単純なトークンシステム**を使ってコンテンツを保護することができます。トークンを持っていないユーザーはビデオをリクエストしようとしても、CDNが禁止します。一方有効なトークンを持つユーザーはそのコンテンツを再生することができます。これはたいていのウェブ認証システムとほとんど同じように動作します。
このトークンシステムの一人のユーザーが、あるユーザーにビデオをダウンロードさせて、それを配布させることもできます。**DRM (デジタル著作権管理)** システムを使ってこれを避けることができます。
実際の製品システムで、人々はしばしばこれら両方の技術を使って、認可と認証を提供します。
### DRM
#### メインシステム
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### What?
DRMは[Digital rights management(デジタル著作権管理)](https://sander.saares.eu/categories/drm-is-not-a-black-box/)を意味します。これは、ビデオやオーディオなどの**デジタルメディアに著作権保護を提供する**方法です。多くの場所で使われていますが、[広くは受け入れられていません](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works)。
#### Why?
コンテンツ製作者(たいていはスタジオ)は、デジタルメディアの不正な再配布を防ぐために、 知的財産が複製されることから守りたいためです。
#### How?
DRMの抽象的で一般的な形式を、とても単純な方法で説明していきます。
**コンテンツC1** (例えば hlsやdashビデオストリーミング)と**プレイヤーP1** (例えば shaka-clappr、exo-player、ios)が**デバイスD1** (例えば スマートフォン、テレビ、タブレット、デスクトップ/ノートブック)上にあり、**DRMシステムDRM1** (widevine、playready、FairPlayなど)を使っているとしましょう。
コンテンツC1は、システムDRM1からの**対称鍵K1**で暗号化され、**暗号化コンテンツC'1**を生成します。
デバイスD1のプレイヤーP1は２つの(非対称)鍵、**秘密鍵PRK1** (この鍵は保護され1**D1**にしか知られていない)と**公開鍵PUK1**を持っています。
> **1保護される**: この保護は、**ハードウェアを介して**なされます。例えば、この鍵は、特別な(読み取り専用)チップの中に保存されます。これは、復号を提供する[ブラックボックス](https://en.wikipedia.org/wiki/Black_box)のように働きます。もしくは(あまり安全でない)**ソフトウェアにより**なされます。DRM システムは、与えられたデバイスがどのタイプの保護を持っているかを知る方法を提供します。
**プレイヤーP1がコンテンツC'1を再生したい**とき、**DRMシステムDRM1**を使う必要があり、まず公開鍵**PUK1**を与えます。DRMシステムDRM1は クライアントの公開鍵**PUK1**で**暗号化された鍵K1**を返します。理論上、このレスポンスは**D1だけが復号可能**です。
`K1P1D1 = enc(K1, PUK1)`
**P1**は、DRMローカルシステム (それは特別なハードウェアかソフトウェアである[SOC](https://en.wikipedia.org/wiki/System_on_a_chip)もなりえる)を使います。このシステムは、秘密鍵PRK1を使って、コンテンツを**復号することができます**。**K1P1D1からの対称鍵K1**を復号化して、**C'1を再生**することができます。鍵がRAM上で外にさらされないのがベストです。
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# jupyterの使い方
**dockerがインストール**されていることを確認して、`./s/start_jupyter.sh`を実行し、ターミナル上の指示に従ってください。
# カンファレンス
* [DEMUXED](https://demuxed.com/) - [最後の２つのイベントプレゼンテーションをチェック](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA)することができます。
# 参考文献
ここに最高のコンテンツがあります。このテキストで見られる全ては、ここから抽出されたか、元になっているか、何か影響を受けています。この驚くべきリンク、本、動画などで、知識をより深くすることができます。
オンラインコースとチュートリアル:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* https://wiki.multimedia.cx
* https://mahanstreamer.net
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
本:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/High-Efficiency-Video-Coding-HEVC/dp/3319068946
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
ビットストリーム仕様:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
ソフトウェア:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://mediaarea.net/en/MediaInfo
* https://www.jongbel.com/
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
非ITUコーデック:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://ghostarchive.org/archive/0W0d8 (was: https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml)
* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
エンコードの概念:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
テスト用ビデオシーケンス:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
その他:
* https://github.com/Eyevinn/streaming-onboarding
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* https://web.archive.org/web/20100728070421/http://www.csc.villanova.edu/~rschumey/csc4800/dct.html (was: http://www.csc.villanova.edu/~rschumey/csc4800/dct.html)
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
* https://sander.saares.eu/categories/drm-is-not-a-black-box/
================================================
FILE: README-ko.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
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# Intro
비디오 기술에 대한 친절한 소개 자료입니다. 비록 소프트웨어 개발자/엔지니어를 대상으로 작성 했지만, **누구나 배울 수 있는** 글이 되었으면 합니다. 아이디어는 [비디오 기술 뉴비를 위한 미니 워크샵](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p)에서 비롯되었습니다.
가능한 **쉬운 단어, 많은 시각 자료 그리고 실용적인 예제**들로 디지털 비디오의 컨셉을 소개하며 어디서든 사용할 수 있는 지식이 될 수 있는 것이 목표입니다. 언제나 수정 사항, 제안 등을 보내셔서 개선해주세요.
**실습** 섹션에서는 **도커**가 필요하며 이 레포지토리를 클론하셔야 합니다.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **알림**: `./s/ffmpeg`나 `./s/mediainfo` 명령을 사용하는 경우,필요로 하는 모든 요구사항이 포함된 **도커 앱**을 실행하는 것을 의미합니다.
**모든 실습 내용은 여러분이 클론한 레포지토리 폴더에서 실행되어야 합니다**.
**주피터 예제**의 경우, `./s/start_jupyter.sh`로 서버를 반드시 실행해야하며 URL을 복사해 브라우저에서 사용하시면 됩니다.
# 변경로그
* DRM 시스템이 추가되었습니다.
* 1.0.0 버전이 릴리즈 되었습니다.
* 요약 버전의 중국어 번역이 추가되었습니다.
# 색인
- [Intro](#intro)
- [변경로그](#%EB%B3%80%EA%B2%BD%EB%A1%9C%EA%B7%B8)
- [색인](#%EC%83%89%EC%9D%B8)
- [기초 용어](#%EA%B8%B0%EC%B4%88-%EC%9A%A9%EC%96%B4)
- [중복 제거](#%EC%A4%91%EB%B3%B5-%EC%A0%9C%EA%B1%B0)
- [색상, 휘도, 시각](#%EC%83%89%EC%83%81-%ED%9C%98%EB%8F%84-%EC%8B%9C%EA%B0%81)
- [색상 모델](#%EC%83%89%EC%83%81-%EB%AA%A8%EB%8D%B8)
- [YCbCr <-> RGB 변환](#ycbcr---rgb-%EB%B3%80%ED%99%98)
- [크로마 서브샘플링](#%ED%81%AC%EB%A1%9C%EB%A7%88-%EC%84%9C%EB%B8%8C%EC%83%98%ED%94%8C%EB%A7%81)
- [프레임 유형](#%ED%94%84%EB%A0%88%EC%9E%84-%EC%9C%A0%ED%98%95)
- [I Frame (인트라, 키프레임)](#i-frame-%EC%9D%B8%ED%8A%B8%EB%9D%BC-%ED%82%A4%ED%94%84%EB%A0%88%EC%9E%84)
- [P Frame (predicted)](#p-frame-predicted)
- [B Frame (bi-predictive)](#b-frame-bi-predictive)
- [요약](#%EC%9A%94%EC%95%BD)
- [시간적 중복 (인터 프리딕션)](#%EC%8B%9C%EA%B0%84%EC%A0%81-%EC%A4%91%EB%B3%B5-%EC%9D%B8%ED%84%B0-%ED%94%84%EB%A6%AC%EB%94%95%EC%85%98)
- [공간적 중복 (인트라 프리딕션)](#%EA%B3%B5%EA%B0%84%EC%A0%81-%EC%A4%91%EB%B3%B5-%EC%9D%B8%ED%8A%B8%EB%9D%BC-%ED%94%84%EB%A6%AC%EB%94%95%EC%85%98)
- [코덱은 어떻게 동작할까요?](#%EC%BD%94%EB%8D%B1%EC%9D%80-%EC%96%B4%EB%96%BB%EA%B2%8C-%EB%8F%99%EC%9E%91%ED%95%A0%EA%B9%8C%EC%9A%94)
- [무엇을? 왜? 어떻게?](#%EB%AC%B4%EC%97%87%EC%9D%84-%EC%99%9C-%EC%96%B4%EB%96%BB%EA%B2%8C)
- [역사](#%EC%97%AD%EC%82%AC)
- [일반 코덱](#%EC%9D%BC%EB%B0%98-%EC%BD%94%EB%8D%B1)
- [첫 번째 스텝 - 사진 파티셔닝](#%EC%B2%AB-%EB%B2%88%EC%A7%B8-%EC%8A%A4%ED%85%9D---%EC%82%AC%EC%A7%84-%ED%8C%8C%ED%8B%B0%EC%85%94%EB%8B%9D)
- [두 번째 스텝 - 프리딕션](#%EB%91%90-%EB%B2%88%EC%A7%B8-%EC%8A%A4%ED%85%9D---%ED%94%84%EB%A6%AC%EB%94%95%EC%85%98)
- [세 번째 스탭 - 변환](#%EC%84%B8-%EB%B2%88%EC%A7%B8-%EC%8A%A4%ED%83%AD---%EB%B3%80%ED%99%98)
- [네 번째 단계 - 양자화](#%EB%84%A4-%EB%B2%88%EC%A7%B8-%EB%8B%A8%EA%B3%84---%EC%96%91%EC%9E%90%ED%99%94)
- [다섯번째 단계 - 엔트로피 인코딩](#%EB%8B%A4%EC%84%AF%EB%B2%88%EC%A7%B8-%EB%8B%A8%EA%B3%84---%EC%97%94%ED%8A%B8%EB%A1%9C%ED%94%BC-%EC%9D%B8%EC%BD%94%EB%94%A9)
- [VLC coding:](#vlc-coding)
- [산술적 코딩:](#%EC%82%B0%EC%88%A0%EC%A0%81-%EC%BD%94%EB%94%A9)
- [여섯번째 단계 - 비트 스트림 포맷](#%EC%97%AC%EC%84%AF%EB%B2%88%EC%A7%B8-%EB%8B%A8%EA%B3%84---%EB%B9%84%ED%8A%B8-%EC%8A%A4%ED%8A%B8%EB%A6%BC-%ED%8F%AC%EB%A7%B7)
- [H.264 비트스트림](#h264-%EB%B9%84%ED%8A%B8%EC%8A%A4%ED%8A%B8%EB%A6%BC)
- [리뷰](#%EB%A6%AC%EB%B7%B0)
- [어떻게 H.265는 H.264보다 더 높은 압축률을 달성했는가](#%EC%96%B4%EB%96%BB%EA%B2%8C-h265%EB%8A%94-h264%EB%B3%B4%EB%8B%A4-%EB%8D%94-%EB%86%92%EC%9D%80-%EC%95%95%EC%B6%95%EB%A5%A0%EC%9D%84-%EB%8B%AC%EC%84%B1%ED%96%88%EB%8A%94%EA%B0%80)
- [온라인 스트리밍](#%EC%98%A8%EB%9D%BC%EC%9D%B8-%EC%8A%A4%ED%8A%B8%EB%A6%AC%EB%B0%8D)
- [일반적인 아키텍쳐](#%EC%9D%BC%EB%B0%98%EC%A0%81%EC%9D%B8-%EC%95%84%ED%82%A4%ED%85%8D%EC%B3%90)
- [점진적 다운로드와 적응형 스트리밍](#%EC%A0%90%EC%A7%84%EC%A0%81-%EB%8B%A4%EC%9A%B4%EB%A1%9C%EB%93%9C%EC%99%80-%EC%A0%81%EC%9D%91%ED%98%95-%EC%8A%A4%ED%8A%B8%EB%A6%AC%EB%B0%8D)
- [컨텐츠 보호](#%EC%BB%A8%ED%85%90%EC%B8%A0-%EB%B3%B4%ED%98%B8)
- [DRM](#drm)
- [Main systems](#main-systems)
- [무엇인가?](#%EB%AC%B4%EC%97%87%EC%9D%B8%EA%B0%80)
- [왜?](#%EC%99%9C)
- [어떻게?](#%EC%96%B4%EB%96%BB%EA%B2%8C)
- [How to use jupyter](#how-to-use-jupyter)
- [Conferences](#conferences)
- [References](#references)
# 기초 용어
**이미지**는 2차원 매트릭스로 생각할 수 있습니다. 여기에 **색상**을 고려한다면, **색상 데이터** 제공에 사용되는 **추가적인 차원**이 있는 **3차원 매트릭스**로 여길 수 있습니다.
[삼원색(빨간색, 초록색, 파란색)](https://en.wikipedia.org/wiki/Primary_color)을 사용해 색상들을 표현하는 경우, **빨간색**, **초록색**, **파란색** 순의 세 가지 평면을 정의할 수 있습니다.
이 매트릭스의 각각의 점을 **픽셀**(화소)라 부를 것입니다. 하나의 픽셀은 주어진 색상의 **강도**(보통 숫자 값)를 표현합니다. 예를들어, **빨간 픽셀**은 초록생이 0, 파란색이 0이고 빨간색이 최대인 것을 의미하지요. **핑크 색상 픽셀**은 세 가지 색상의 조합으로 구성되어 있습니다. 대표적인 0부터 255까지의 숫자 범위를 사용하면 핑크색 픽셀은 **빨간색=255, 초록색=192, 파란색=203**으로 정의됩니다.
> #### 색상 이미지를 인코딩하는 또 다른 방법
> 이미지를 구성하는 색상들을 표현하는데에는 다양한 모델들이 존재합니다. 예를들면 RGB 모델을 사용할 때 3바이트를 사용하는 것 대신, 각 픽셀을 표현하는데 한 바이트만 필요한 인덱스 팔레트를 사용할 수 있지요. 이러한 모델에서는 3차원 매트릭스 대신 2차원 매트릭스를 사용하여 색상을 표현할 수 있으며, 메모리를 아낄 수 있지만 색상에 대한 몇 가지 옵션들을 포기해야하죠.
>
> 
예를들어 아래의 사진을 한 번 보세요. 첫 번째 얼굴은 모든 색상이 표현되어있지요. 다른 것들은 빨간색, 초록색, 파란색 평면입니다. (회색 톤으로 나옴)
여기서 **빨간색**(두 번째 얼굴의 밝은 부분들)이 색상에 많이 나타나는 것을 볼 수 있습니다. 반면 **파란색**(마지막 얼굴)은 대부분 **마리오의 눈**과 옷의 일부에만 나타납니다. **모든 색상**의 영향력이 낮은 부분은 **마리오의 수염**(가장 어두운 부분)입니다.
각각의 색상 강도는 특정한 양의 비트를 필요로 합니다. 여기서 양이란 **비트 깊이** 입니다. 각 색상 평면당 8비트(0부터 255 사이의 값을 가지는)를 할애했다고 가정하면 **24비트**의 **색 깊이**(8 비트 * R/G/B의 3가지 평면)를 가지게 되며, 2의 24승 가지의 서로 다른 색상을 사용할 수 있음을 의미합니다.
> [세상에서 비트로 이미지가 캡쳐되는 원리](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm)에 대해 배울 수 있는 **훌륭한 글** 입니다.
이미지의 또 다른 속성은 **해상도**입니다. 해상도는 차원 하나에 포함될 수 있는 픽셀의 수를 의미합니다. 이는 보통 width x height로 표현됩니다. 그 예로 아래에는 **4x4** 이미지가 있습니다.
> #### 실습: 이미지와 색상 가지고 놀기
> [주피터](#how-to-use-jupyter)를 사용하여 [이미지와 색 가지고 놀기](/image_as_3d_array.ipynb)를 해볼 수 있어요.
>
>
> [이미지 필터(외곽선 검출, 강조, 블러)](/filters_are_easy.ipynb)에 대해 학습할 수 있습니다.
이미지나 비디오를 다루다보면 볼 수 있는 또 다른 속성인 **종횡비(aspect ratio)** 가 있습니다. 이는 이미지나 픽셀의 폭과 높이의 비례 관계를 간단하게 표현하는 방법입니다.
사람들이 이 영상 혹은 사진은 **16x9**야라고 한다면 이는 일반적으로 **화면 종횡비(DAR)** 에 대한 이야기를 하는 것입니다. 반면에 각각의 픽셀의 서로 다른 형태에 대한 것은 **픽셀 종횡비(PAR)** 라고 부릅니다.
> #### DVD는 DAR 4:3이다.
> DVD의 실제 해상도는 704x480이지만 10:11(704x10/480x11)의 PAR로 인해 4:3의 종횡비를 유지합니다.
마지막으로 **비디오**는 또다른 차원으로 볼 수 있는 시간의 축에 나열된 **n개의 프레임의 연속**으로 정의할 수 있습니다.
여기서 n은 프레임레이트 혹은 초당 프레임 수 입니다.
비디오의 **비트 레이트**는 동영상을 표시하는데 초당 필요한 비트의 수입니다.
> 비트레이트 = 폭 * 높이 * 비트 깊이 * 초당 프레임 수
예를들어 어떤 영상이 30fps, 24비트 픽셀 깊이, 480x240의 해상도에 압축을 하지 않은 경우라면 **초당 82,944,000비트** 혹은 82.944 Mbps (30x480x240x24)가 필요로 합니다.
**비트 레이트**가 거의 일정한 경우 이를 고정 비트레이트(**CBR**)라고 부르며 가변의 경우에는 가변 비트레이트(**VBR**)라 부릅니다.
> 이 그래프는 프레임이 검은색인 동안에는 그리 많은 비트를 소비하지 않는 제한된 VBR을 보여줍니다.
>
> 
초기에는 엔지니어들이 **여분의 대역폭 소모 없이** 두 배의 인지 가능한 프레임 레이트로 비디오를 표시하는 기술을 개발했습니다. 이 기술은 **인터레이싱 비디오**라고 부릅니다. 이는 1프레임에서 화면의 절반을 보내고 나머지 절반을 그 다음 프레임에 보냅니다.
오늘날의 화면은 대부분 **프로그레시브 스캔 기술**을 사용합니다. 프로그레시브는 각 프레임의 모든 라인을 순서대로 그리는 영상을 출력, 저장, 전송하는 기술입니다.
이제 어떻게 **이미지**가 디지털로 표현되는지, **색상**이 어떻게 배치되어 영상 표현에 어느 정도의 **초당 비트 수**가 필요한지, 그게 고정(CBR)인지 가변(VBR)인지에 대한 것과 **해상도**와 **프레임 레이트**와 인터레이스드, PAR와 같은 많은 용어들을 배웠습니다.
> #### 연습: 비디오 속성 확인
> [ffmpeg이나 mediainfo로 속성 살펴보기](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)
# 중복 제거
압축 없이는 비디오를 사용할 수 없다는 것을 배웠습니다. 30fps인 720p 해상도의 **한 시간 짜리 영상**은 **278GB***를 필요로합니다. DEFLATE(PKZIP, Gzip, PNG에 사용되는)와 같은 **무손실 데이터 압축 알고리즘**을 사용하는 경우 대역폭을 충분히 줄일 수 없으므로 비디오를 압축할 다른 방법을 찾아야합니다.
> * 1280 x 720 x 24 x 30 x 3600 (폭, 높이, 픽셀 당 비트 수, fps, 영상 시간)
이를 위해서는, **우리의 눈을 속여야합니다**. 눈은 색상보다 명암 구분을 더 잘합니다. 영상은 변화가 거의 없는 많은 이미지들로 이루어져있으며, 각 프레임들은 같거나 유사한 색상을 사용하는 많은 영역들이 포함되어 있습니다.
## 색상, 휘도, 시각
시각은 [색상보다 명암에 더 민감하며](http://vanseodesign.com/web-design/color-luminance/), 이를 테스트 해볼 수 있습니다. 이 사진을 보세요.
만일 왼쪽의 이미지에서 사각형 **A와 B가 동일한 색상**이라는 사실을 눈치채지 못하셨다면 정상입니다. 우리의 뇌는 **색상보다는 명암에 더 신경쓰도록** 만들기 때문이지요. 오른쪽의 사진에서는 같은 색상을 연결해주는 커넥터가 있기 때문에 우리의 뇌는 두 사각형이 동일한 색상이라는 사실을 쉽게 파악할 수 있습니다.
> **시각은 어떻게 동작하는가에 대한 간단한 설명**
>
> [눈은 복잡한 장기이며](http://www.biologymad.com/nervoussystem/eyenotes.htm), 많은 것들로 구성되어 있으나 여기서는 대부분은 원추세포와 간상세포에 집중합니다. 눈은 [약 1억 2천개의 간상세포와 6백만개의 원추세포로 구성되어 있습니다](https://en.wikipedia.org/wiki/Photoreceptor_cell).
> **더 간단하게 설명하기 위해**, 색상과 명암을 눈의 각 기능들에 입력한다고 가정해봅시다.
> **[간상세포](https://en.wikipedia.org/wiki/Rod_cell) 는 대부분 빛에 반응하는 반면에**, **[원추세포](https://en.wikipedia.org/wiki/Cone_cell)는 색상에 반응합니다**. 서로 다른 색소를 지닌 세 가지 타입의 원추 세포가 존재합니다: [S-cones (파란색), M-cones (초록색) and L-cones (빨간색)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> 간상 세포(명암)가 원추 세포(색상)보다 많기 때문에, 색상보다 명암을 더 잘 구분할 것이라는 사실을 짐작할 수 있을 것입니다.
>
> 
>
> **대비 인식 능력**
>
> 실험 심리학 및 다른 분야들의 연구자들은 사람의 시각에 대한 많은 이론을 개발했습니다. 그 중 하나를 대비 인식 능력이라고 부릅니다. 이것들은 빛의 공간과 시간과 관련 있으며, 주어진 초기에 관측한 빛이 나타내는 값이, 관측자가 변화가 있다는 사실을 말하기까지 얼마나 많은 변화가 필요한지에 대한 것입니다. `function`이 복수형으로 되어 있는데, 그 이유는 바로 대비 인식 능력은 명암 뿐만 아니라 색상도 관계되어 있기 때문입니다. 이 실험의 결과는 대부분의 경우에 우리의 눈이 색상보다 명암에 더 민감하다는 사실을 말해줍니다.
이제 **루마**(이미지의 명도)에 더 민감하다는 사실을 알게되었으므로 이를 이용해볼 수 있습니다.
### 색상 모델
초반에 배웠던 **RGB 모델**을 이용한 [이미지에 색상 지정하는 방법](#basic-terminology)외에도 다른 모델들도 있다는 것을 배웠습니다. 사실 모델 중 크로미넌스(색상)로부터 루마(명도)를 분리하는 모델이 있습니다. 이는 **YCbCr***이라고도 알려져있습니다.
> * 이와 같은 방식을 사용하는 다른 모델들도 존재합니다.
이 색상 모델은 명도를 표현하는 **Y**와 두 가지 색상 채널인 **Cb**(크로마 블루)와 **Cr**(크로마 레드)를 사용합니다. [YCbCr](https://en.wikipedia.org/wiki/YCbCr)은 RGB로부터 유도 가능하며, 또한 반대로 RGB로 역변환도 가능합니다. 이 모델을 사용하여 아래에 보이는 이미지와 같은 완전한 색상의 이미지를 만들 수 있습니다.
### YCbCr <-> RGB 변환
논쟁이 있을 수 있는데요, **어떻게 초록색을 사용하지 않고** 모든 색상을 표현할 수 있을까요?
이 질문에 답하기 위해 RGB를 YCbCr로 변환하는 일을 같이 해봅시다. **[ITU-R 그룹*](https://en.wikipedia.org/wiki/ITU-R)** 에서 권장하는 **[표준 BT.601](https://en.wikipedia.org/wiki/Rec._601)** 계수를 사용할 것입니다. 첫 번째 단계는 **루마 계산**입니다. ITU에서 제안한 상수를 사용하고 RGB 값을 대체할 것입니다.
```
Y = 0.299R + 0.587G + 0.114B
```
루마를 얻었으니 이제 **색상 분리**(크로마 블루, 크로마 레드)를 할 수 있습니다:
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
또한 이를 **역변환** 가능하며 **YCbCr을 사용하여 초록색**을 얻을 수도 있습니다.
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * 무엇이 표준인지 정의합니다. 가령 [4K란 무엇인가? 어떤 프레임 레이트를 사용해야 하는가? 해상도는? 컬러 모델은?](https://en.wikipedia.org/wiki/Rec._2020)과 같은 것들입니다.
일반적으로 **디스플레이**(모니터, TV, 스크린 등)는 **오직 RGB 모델**만 활용하며 이를 각자 다른 방식으로 배열합니다. 확대된 모습은 다음과 같습니다:
### 크로마 서브샘플링
루마와 크로마로 표현되는 이미지를 인간의 시각이 크로마보다 루마 해상도에 민감하다는 사실을 이용하여 선택적으로 정보를 제거하는데에 있어 이점을 취할 수 있습니다. **크로마 서브샘플링**은 **루마 대비 크로마 정보를 줄여** 이미지를 인코딩하는 기술입니다.
얼마만큼의 크로마 해상도를 줄여야 하는 것일까요?! 이미 답은 나와 있습니다. 해상도와 병합(`final color = Y + Cb + Cr`)을 다루는 스키마가 이미 존재합니다.
이러한 스키마는 서브 샘플링 시스템으로 알려져있으며 루마 픽셀의 `a x 2` 블록에 대한 크로마 해상도를 정의하는 3 부분의 비율 `a:x:y`로 정의됩니다.
* `a`는 수평 샘플링 레퍼런스입니다. (일반적으로 4)
* `x`는 `a` 픽셀의 행(row)의 크로마 샘플 수입니다. (`a`에 대한 수평해상도)
* `y`는 `a` 픽셀의 첫 번째와 두 번째 행 사이의 크로마 샘플의 변화에 대한 개수입니다.
> 예외적으로 4:1:0은 각각의 루마 해상도의 `4 x 4`블록 내의 단일 크로마 샘플을 제공합니다.
현대 코덱에 사용되는 보편적인 기법은 **4:4:4(서브 샘플링 없음)**, **4:2:2, 4:1:1, 4:2:0, 4:1:0 and 3:1:1**입니다.
> **YCbCr 4:2:0 병합**
>
> 여기에 YCbCr 4:2:0을 사용한 이미지의 병합된 조각입니다. 픽셀 당 12 비트만 소모했을 뿐이라는 사실을 기억하세요.
>
> 
동일한 이미지에 대해 주요한 크로마 서브샘플링 타입들로 인코딩한 이미지를 보실 수 있을겁니다. 첫 번째 행의 이미지들은 최종 YCbCr 이미지이며 두 번째 열의 이미지들은 크로마 해상도를 나타냅니다. 적은 손실로 훌륭한 결과를 얻었습니다.
앞서 우리는 [한 시간 분량의 720p 해상도에 30fps인 영상을 저장하기 위해 278GB가 필요하다고 계산](#redundancy-removal)하였습니다. **YCbCr 4:2:0**을 사용하면 이를 **절반(139GB)의 크기***로 줄일 수 있습니다. 하지만 아직 그리 이상적이진 않네요.
> * 이 값은 width, height, 픽셀 당 비트 수 그리고 fps를 곱하여 얻었습니다. 이전에는 24비트가 필요했지만 이젠 12비트면 됩니다.
> ### 실습: YCbCr 히스토그램 확인하기
> [ffmpeg로 YCbCr 히스토그램을 확인할 수 있습니다.](/encoding_pratical_examples.md#generates-yuv-histogram) 이 장면은 [히스토그램](https://en.wikipedia.org/wiki/Histogram)에 보여지는 바와 같이 파란색 비율이 높습니다.
>
> 
## 프레임 유형
이제 계속해서 **시간 상에서의 중복 제거**를 할 수도 있지만 그 전에 몇 가지 기본적인 용어를 정립해보겠습니다. 30fps의 영화가 있다고 가정해봅시다. 여기에 이 영화의 첫 번째부분의 4장의 프레임이 있습니다.
  
**파란색 배경**과 같이 프레임 내에 **많은 중복**을 볼 수 있을겁니다. 프레임 0부터 3까지 변한게 없지요. 이 문제를 해결하기 위해 프레임을 세 가지 유형으로 **추상적으로 분류**할 수 있습니다.
### I Frame (인트라, 키프레임)
`I-frame(레퍼런스, 키프레임, 인트라)`은 **독립적인 프레임**입니다. 렌더링 하기 위해 어떤 것에도 의존적이지 않지요. I-frame은 마치 정적인 사진같습니다. 첫 번째 프레임은 보통 I-frame입니다. 하지만 I-frame은 일반적으로 다른 유형의 프레임의 사이사이에 삽입된다는 사실을 볼 수 있을 겁니다.
### P Frame (predicted)
`P-frame`은 항상 **이전의 프레임을 이용하여 렌더링 될 수 있다는 점**을 이점으로 취할 수 있습니다. 예를 들어 두 번째 프레임에서 변화라곤 공이 앞으로 움직인 것 뿐입니다. **변경된 부분을 사용해서 이 전의 프레임을 참고하여 프레임 1번을 다시 만들 수 있지요.**
 <- 
> #### 실습: I-frame이 한장 뿐인 동영상
> P-frame이 데이터를 적게 사용하는데 왜 전체를 [I-frame 한장에 나머지 전부가 p-frame인 영상](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)으로 인코딩하지 않을까요?
>
> 이 비디오를 인코딩 후 영상을 재생해 비디오의 **앞부분으로 이동**해보면, 이동하는데 **시간이 좀 걸림**을 발견하게 될 것입니다. 왜냐하면 **P-frame은 렌더링하기 위해 레퍼런스 프레임(가령 I-frame)이 필요하기 때문**입니다.
>
> 빨리 테스트 해볼 수 있는 다른 방법은 I-frame 한 장으로 비디오를 인코딩한 뒤, [2초 간격으로 I-frame을 삽입하여 인코딩한 뒤](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second) **각 렌디션의 사이즈를 확인해보세요.**
### B Frame (bi-predictive)
이전과 앞의 프레임을 참조하여 더 나은 압축률을 제공할 수 있다면?! 그것이 바로 B-Frame이라는 것입니다.
 <-  -> 
> #### 실습: B-frame으로 비디오 비교하기
> 두 개의 렌디션을 생성해, 첫 번째에는 B-frame을 주고 다른 것에는 [B-frame을 주지 않고](/encoding_pratical_examples.md#no-b-frames-at-all) 퀄리티를 비롯하여 파일 사이즈도 확인을 해보세요.
### 요약
이러한 프레임들은 **더 나은 압축 성능을 제공**하는데 사용됩니다. 어떻게 이런 일이 가능한지 다음 섹션에서 알아볼 거에요. 하지만 여러분은 이제 **I-frame은 코스트가 높고 P-frame은 좀 더 낮고 B-frame이 제일 낮다는 사실**에 대해 생각할 수 있게 되었습니다.
## 시간적 중복 (인터 프리딕션)
반드시 줄여야 하는 **시간 상에서의 반복**에 대해 알아봅시다. 이러한 중복의 유형은 **인터 프리딕션**이라는 기법을 활용해 해결할 수 있습니다.
프레임 0과 1의 시퀀스를 **더 적은 비트를 활용해** 인코딩 해볼 것 입니다.
시도해 볼 수 있는 한 가지는 바로 제거입니다. 단순히 **프레임 0에서 프레임 1을 빼면** 오로지 **남은 부분만 인코딩**하면 됩니다.
하지만 이보다 더 적은 비트를 사용하는 **더 나은 방법**이 있다면 어떠시겠나요? 우선, `frame 0`을 잘 정의된 파티션의 집합으로 여기고, `frame 0`에 `frame 1`을 매칭시켜보세요. 이를 **모션 추정**으로 생각할 수 있지요.
> ### 위키페디아 - 블록 모션 보정
> "**블록 모션 보정**은 현재 프레임을 겹치지 않은 블록으로 분할하고, 모션 보정 벡터가 **블록이 어디에서 왔는지 알려줍니다.** (일반적인 오개념으로 이전의 프레임이 겹치지 않은 블록으로 분할되어 있고, 모션 보정 벡터가 이 블록들이 어디로 이동할 지 알려주는 정보라고 여기는 것이 있습니다.) 원본 블록은 일반적으로 원본 프레임에 겹칩니다. 몇몇의 비디오 압축 알고리즘은 서로 다른 이전에 전송된 프레임의 일부 조각을 현재 프레임과 합칩니다."
여기서 `x=0, y=25`에서 `x=6, y=26`으로 공이 움직였다고 추정할 수 있습니다. **x**와 **y**의 값들이 **모션 벡터**입니다. 비트를 절약할 수 있는 **추가적인 단계**로는 최종 블록의 위치와 예측되는 벡터의 **모션 벡터의 차이만 인코딩 하는 것**입니다. 따라서 최종적인 모션 벡터는 `x=6 (6-0), y=1 (26-25)`가 될 것입니다.
> 현실 세계에서는 이 **공이 n개의 파티션으로 조각**나지만 과정은 동일합니다.
프레임 상의 객체는 **3차원 상으로 움직이며**, 이 공이 배경으로 이동하는 경우에 더 작아질 수도 있습니다. 일치점을 찾으려 했던 블록과 **완전히 일치하는 것을 찾을 수 없는 일은** 일반적이지요.
그럼에도 **모션 추정**을 적용하는 것이 단순히 델타 프레임 기법을 사용하는 것 보다 **인코딩 할 데이터가 더 적다는** 사실입니다.
> ### 모션 보정은 실제로 어떻게 보이는가
> 이 기법은 모든 블록에 대해 적용되며, 공이 매우 빈번하게 하나 이상의 블록에서 분할됩니다.
> 
> Source: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
[주피터를 활용하여 이러한 기법들을 실험해볼 수 있습니다](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Hands-on: See the motion vectors
> We can [generate a video with the inter prediction (motion vectors) with ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> Or we can use the [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (which is paid but there is a free trial version which limits you to only the first 10 frames).
>
> 
## 공간적 중복 (인트라 프리딕션)
영상의 **각각의 프레임을** 분석해보면 **많은 영역이 연관되어 있다는 사실**을 발견할 수 있을겁니다.
예제를 살펴보죠. 이 장면은 대부분 파란색과 하얀색으로 구성되어 있습니다.
이는 `I-frame`이며 **이전의 프레임을 사용하여** 예측할 수는 없지만, 여전히 압축할 여지는 남아 있지요. **빨간 블록 부분을** 인코딩해볼거에요. **인접한 부분을 살펴보면**, **블록 주변의 색상의 경향을 추산**할 수 있습니다.
프레임이 계속하여 **수직으로 색상이 확산될 것**이라고 예측할 것입니다. 이 의미는 **블록의 인접한 값을 알 수 없는 픽셀의** 색상이 지니고 있다는 것이지요.
**예측이 틀렸을 수도 있습니다.** 그 이유는 바로 이 기법(**인트라 프리딕션**)을 적용한 뒤 잔여 블록을 반환하는 **실제 값을 빼야할** 필요가 있기 때문에, 원본과 비교했을 때 훨씬 더 압축률이 높은 행렬이 생성됩니다.
> #### 실습: 인트라 프리딕션 확인해보기
> [매크로 블록과 프리딕션을 사용해 ffmpeg로 영상을 생성할 수 있습니다.](/encoding_pratical_examples.md#generate-debug-video) [각 블록의 색상의 의미](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes)를 ffmpeg 문서를 확인해 이해해보세요.
>
> 
>
> 혹은 [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)(유료지만 처음 10 프레임 제한이 걸려 있는 무료 트라이얼 버전도 있습니다)를 사용해보세요.
>
> 
# 코덱은 어떻게 동작할까요?
## 무엇을? 왜? 어떻게?
**무엇을?** 코덱은 디지털 비디오를 압축 혹은 해제하는 소프트웨어/하드웨어 입니다. **왜?** 시장과 사회는 제한적인 대역폭과 스토리지를 활용한 높은 퀄리티의 영상을 필요로 합니다. 30fps, 픽셀당 24비트, 480x240 해상도의 비디오에 [필요한 대역폭 계산](#%EA%B8%B0%EC%B4%88-%EC%9A%A9%EC%96%B4)을 떠올려보세요. 압축을 하지 않은 경우 **82.944 Mbps**였지요. TV와 인터넷에서 HD/FullHD/4K를 전송할 유일한 방법입니다. **어떻게?** 여기서는 주요 기법들에 대해 간략히 살펴볼 것입니다.
> **코덱 vs 컨테이너**
>
> 초심자들이 흔히 범하는 실수 중 하나로 디지털 동영상 코덱과 [디지털 비디오 컨테이너](https://en.wikipedia.org/wiki/Digital_container_format)를 혼동하는 것입니다. **컨테이너**를 동영상의 메타 데이터(오디오 포함)를 포함하는 래퍼 포맷으로 여길 수 있으며 **압축된 비디오**를 컨테이너의 페이로드로 볼 수도 있습니다.
>
> 일반적으로 동영상 파일의 확장자는 비디오 파일의 동영상 컨테이너를 정의합니다. 예를들어 `video.mp4` 파일은 **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14)** 컨테이너일 것이고 파일 이름은 `video.mkv`로 **[matroska](https://en.wikipedia.org/wiki/Matroska)** 일 것입니다. 코덱과 컨테이너 포멧을 정확히 확인하려면 [ffmpeg나 mediainfo](/encoding_pratical_examples.md#inspect-stream)를 사용하면 됩니다.
## 역사
일반적인 코덱의 내부 동작을 파헤치기에 앞서, 구세대 동영상 코덱에 대해 좀 더 자세히 살펴보도록 합시다.
비디오 코덱인 [H.261](https://en.wikipedia.org/wiki/H.261)는 1990년(정확히는 1988년)에 탄생하였으며 **64 kibt/s의 데이터 전송비**로 동작하기 위해 설계되었습니다. 이 코덱에서 이미 크로마 서브샘플링, 매크로 블록 등의 아이디어를 사용했습니다. 1995년, **H.263** 비디오 코덱 표준이 탄생하였으며 2001년까지 지속적으로 확장되었습니다.
**H.264/AVC**의 첫 버전은 2003년에 완성되었습니다. 같은 해에, **TrueMotion**이라는 회사가 **로얄티가 없는** **VP3**이라 부르는 영상 압축 코덱을 공개하였습니다. 2008년, **구글이 이 회사를 인수하고** 같은 해에 **VP8**을 공개하였습니다. 2012년 12월, 구글은 **VP9**을 출시하였으며 VP9은 **브라우저 시장의 약 ¾(모바일 포함)에서 지원됩니다.**
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)** 은 **구글, 모질라, 마이크로소프트, 아마존, 넷플릭스, AMD, ARM, 엔비디아, 인텔 그리고 시스코**를 비롯한 여러 회사로 구성된 [오픈 미디어 연합 (AOMedia)](http://aomedia.org/)이 설계를 주도하는 **로얄티 없는** 새로운 오픈 소스 동영상 코덱입니다.
> #### AV1의 탄생
>
> 2015년 초, 구글은 [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1)를, Xiph(모질라)는 [Daala](https://xiph.org/daala/) 그리고 시스코는 [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03)라는 로얄티 프리 비디오 코덱을 만들었습니다.
>
> MPEG LA는 HEVC(H.265)에 대한 애뉴얼 캡을 발표하고, H.264보다 8배 높은 수수료를 발표했으나 태세전환을 하고 규칙을 변경했습니다:
> * **애뉴얼 캡 없음**,
> * **컨텐츠 수수료** (수익의 0.5%) 그리고
> * **단위당 수수료는 H.264보다 약 10배 높음**.
>
> [오픈 미디어 연합](http://aomedia.org/about/)은 하드웨어 제조사(인텔, AMD, ARM, 엔비디아, 시스코), 컨텐츠 딜리버리(구글, 넷플릭스, 아마존), 브라우저 메인테이너(구글, 모질라)를 비롯한 다른 여러 회사들에 의해 설립되었습니다.
>
> 이 회사들은 동일한 목표를 가지고 있었으며, 로얄티 프리의 동영상 코덱과 훨씬 [단순한 특허 라이센스](http://aomedia.org/license/patent/)로 무장한 AV1의 탄생이였죠. **티모시 B. 테리베리**는 [AV1 컨셉, 라이센스 모델, 현황](https://www.youtube.com/watch?v=lzPaldsmJbk)에 대한 멋진 프리젠테이션을 진행했습니다. 이 섹션의 출처이기도 하지요.
>
> **브라우저로 AV1 코덱을 분석** 가능하다는 사실을 알게되면 아주 놀라실거에요. https://arewecompressedyet.com/analyzer/ 에 방문해보세요.
> 
>
> 추신: 코덱의 역사에 대해 더 자세히 알고 싶으면 [동영상 압축 특허](https://www.vcodex.com/video-compression-patents/)에 대한 기초를 공부하셔야합니다.
## 일반 코덱
**일반 코덱의 바탕이 되는 주요 메커니즘을** 소개할 것이며, 이러한 컨셉들의 대부분은 유용하고 VP9, AV1, HEVC와 같은 현대 코덱들에서 사용됩니다. 설명을 아주 굉장히 단순화한다는 점 양해 부탁드립니다. 글의 간간히 현업 예제(대부분 H.264)를 시연해볼 것입니다.
## 첫 번째 스텝 - 사진 파티셔닝
첫 번째 단계는 **프레임을 여러개의 파티션, 서브 파티션** 그 이상으로 **분할**하는 것입니다.
**그런데 왜?** 많은 이유가 있어요. 예로, 사진을 분할하게 되면 정적인 배경에 커다란 파티션을 사용하고, 적은 움직임들에 대해서는 작은 파티션을 사용하면 프리딕션을 보다 더 정확하게 수행할 수 있답니다.
일반적으로 코덱은 **이러한 파티션들을 슬라이스(혹은 타일), 매크로(혹은 코딩 트리 유닛) 그리고 수 많은 서브 파티션**으로 정리를 합니다. 이러한 파티션의 최대 크기는 다양한데, AVC는 16x16을 사용하는 반면 HEVC는 64x64를 설정하지만 서브 파티션들은 4x4의 사이즈까지도 사용합니다.
앞서 공부한 **프레임 유형**을 떠올려봅시다. **이러한 아이디어들을 블록들에도** 적용해볼 수 있지요. 그러므로 I-Slice, B-Slice, I-Macroblock등을 이용할 수 있습니다.
> ### 실습: 파티션 확인해보기
> 여기서도 [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)(유료도 있지만 첫 10프레임 제약의 무료 트라이얼 버전도 있음)를 사용할 것입니다. [VP9 파티션](/encoding_pratical_examples.md#transcoding) 분석본입니다.
>
> 
## 두 번째 스텝 - 프리딕션
파티션을 확보하고 나면, 이제 이에 대한 프리딕션을 수행할 수 있습니다. [인터 프리딕션](#temporal-redundancy-inter-prediction)에 대해서는 **모션 벡터와 나머지**를 전송해야하고, [인트라 프리딕션](#spatial-redundancy-intra-prediction)에 대해서는 **프리딕션의 방향과 나머지를 전송할 것입니다.**
## 세 번째 스탭 - 변환
잔여 블록(`예측된 파티션 - 실제 파티션`)을 확보하고 난 후에는 **변환**을 통하여 **전반적인 품질을** 유지하면서 **버려야 할 픽셀**을 알 수 있게됩니다. 이 정확한 동작을 위한 몇 가지 변환들이 존재합니다.
[다른 다양한 변환](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms)들이 존재하지만, 여기서는 이산 코사인 변환(DCT)에 대해 주도 면밀히 살펴볼 것입니다. [**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform)의 주된 성질로는:
* **픽셀들의** 블록을 동일한 사이즈의 **주파수 계수**의 블록으로 변환함
* 에너지를 **압축**하여 공간적 중복을 쉽게 제거함
* **가역적임**. 바꿔말해 픽셀로 되돌릴 수 있다는 뜻
> 2017년 2월 2일, 신트라 R.J. 와 바이엘 F.M은 [14개의 덧셈만 필요로하는 이미지 압축에 대한 DCT 유사 변환](https://arxiv.org/abs/1702.00817)을 논문으로 발표하였습니다. (역자: 8-포인트 직교 근사 이산 코사인 변환으로, 곱셈이나 비트 시프트가 필요가 없음)
모든 불렛에 소개된 이점들에 대해 이해하지 못한다고 해서 걱정할 필요는 없습니다. 실제 값을 확인하기 위해 몇 가지 실험을 해볼거에요.
다음 **픽셀의 블록** (8x8)을 사용해볼 것입니다:
렌더링된 블록 이미지는 다음과 같습니다(8x8):
픽셀의 블록에 대하여 **이산 코사인 변환을 수행하면** **계수의 블록**(8x8)을 얻게됩니다:
그리고 이 계수의 블록을 렌더링하면 다음과 같은 이미지를 얻게 됩니다:
보시는 바와 같이 원본 이미지같이 생기진 않죠. 눈치 채셨을지 모르겠지만 **첫 번째 계수**는 다른 모든 것들과 아주 다릅니다. 첫 번째 계수는 DC 계수로, 입력 배열에서 **샘플의 모든 것**을 표현하며 **평균과 유사합니다.**
이 계수 블록은 흥미로운 성질을 가지고 있는데, 높은 주파수 성분을 낮은 주파수 성분과 분리한다는 점이지요.
이미지 상에서, **에너지의 대부분은** [**낮은 주파수**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm)에 집중되기 때문에, 이미지를 주파수 성분으로 변환하고 **높은 주파수 계수를 날려버리면**, 이미지 품질을 그리 많이 희생하지 않고도 이미지를 표현하는데 필요한 **데이터의 양을 줄일 수 있습니다.**
> 주파수는 신호가 얼마나 빠르게 변화하는가를 의미합니다.
테스트에서 얻은 지식을 활용하여 DCT를 사용해 원본 이미지를 주파수(계수 블록)를 변환 후 중요하지 않은 계수들을 날려봅시다.
우선, 이를 **주파수 영역**으로 변환합니다.
다음으로, 오른쪽 하단의 대부분에 해당되는 계수의 부분(67%)를 버립니다.
마침내, 폐기한 계수 블록으로부터 이미지를 재구성해보고 이를 원본과 비교해봅시다. (명심하세요. 이는 가역적이여야 합니다.)
여러분이 보시는 바와 같이 원본 이미지와 닮긴 했지만 원본과 비교하면 다른 부분이 많이 보입니다. **67.1875%를 날려버리고도** 원본과 유사한 무언가를 얻을 수 있었지요. 더 높은 이미지 품질을 위해 더 똑똑한 방법으로 계수를 폐기하는 방법도 있지만 이는 다음의 주제입니다.
> **각 계수는 모든 픽셀을 사용하여 만듭니다**
>
> 여기서 기억하셔야 할 중요한 점은 각 계수가 각각의 픽셀과 직접적으로 매핑되지는 않으나 계수는 모든 픽셀의 가중치 합이라는 점입니다. 다음 이 놀라운 도식은 어떻게 첫 번째와 두 번째 계수가 각 인덱스에 대한 고유한 가중치를 사용하여 계산되는지 보여줍니다.
>
> 
>
> 출처: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> 또한 DCT를 기반으로 [간단한 이미지 형성을 보며 DCT를 시각화](/dct_better_explained.ipynb)해볼 수도 있습니다. 그 예로 각 계수 가중치를 사용하여 [알파벳 A가 형성되는 모습입니다.](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT)
>
> 
> ### 실습: 다른 계수 날리기
> [DCT 변환](/uniform_qua ntization_experience.ipynb)
## 네 번째 단계 - 양자화
몇몇 계수를 버릴 때 마지막 단계(변환)에서 일종의 양자화를 수행하였습니다. 이 단계에서는 잃을 정보(간단한 용어로 **손실부**)를 선택하고, **계수를 양자화 하여 압축을 합니다.**
어떻게 계수 블록을 양자화 시킬까요? 간단한 방법으로는 균일 양자화가 있습니다. 균일 양자화는 블록을 가지고 **단일 값(10)으로 이를 나누어 ** 이 값을 내림합니다.
이 계수 블록을 어떻게 **변환**(재양자화) 할까요? 처음에 나누었던 값과 **동일한 값을 곱하여** 수행할 수 있습니다.
**이 방법이 최선은 아닙니다.** 왜냐하면 각 계수의 중요성을 고려하지 않았기 때문입니다. 단일 값 대신 **양자화 행렬**을 사용할 수 있습니다. 이 행렬은 DCT의 특성을 이용하는 것으로, 우측 하단의 대부분을 양자화하고 좌측 상단은 덜한다는 특성으로, [JPEG가 이와 유사한 방법을 사용합니다](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html). [이 행렬을 소스코드](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40)에서 확인할 수 있습니다.
> ### 실습: 양자화
> [양자화](/dct_experiences.ipynb)를 실습해보실 수 있습니다.
## 다섯번째 단계 - 엔트로피 인코딩
데이터(이미지 블록/슬라이스/프레임)를 양자화 하고 나면 손실 없이 압축할 수 있는 방법이 여전히 남아 있습니다. 데이터를 압축하는 많은 방법(알고리즘)이 존재합니다. 여기서는 그 중 일부를 간략히 알아볼 것입니다. 더 깊은 이해를 바란다면 [압축에 대한 이해: 현대 개발자를 위한 데이터 압축](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/)이라는 놀라운 책을 읽어보세요.
### VLC coding:
심볼 스트림이 있다고 가정해봅시다: **a**, **e**, **r**, **t**이며 이 심볼들에 대한 확률(0과 1사이)은 다음 테이블에 표현됩니다.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| 확률 | 0.3 | 0.3 | 0.2 | 0.2 |
가장 가능성이 크고 큰 코드부터 낮은 것까지 고유한 이진 코드(작은 값 추천)를 할당할 수 있습니다.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| 확률 | 0.3 | 0.3 | 0.2 | 0.2 |
| 이진 코드 | 0 | 10 | 110 | 1110 |
스트림 **eat**를 압축해봅시다. 각 심볼당 8비트를 소비한다고 가정하면 압축하지 않은 경우에 24비트를 소비하게 됩니다. 하지만 각각의 심볼을 코드로 대체하면 공간을 절약할 수 있습니다.
첫 번째 단계는 `10`에 해당하는 심볼 **e**를 인코딩하고 두 번째 심볼 **a**를 더하고(산술 연산 아님) `[10][0]` 마지막으로 최종 압축된 비트스트림을 `[10][0][1110]` 혹은 `1001110`으로 만드는 심볼 **t**를 붙여주면 오직 **7비트**만 필요로 합니다. (원본보다 3.4배 적은 공간)
각각의 코드는 반드시 고유한 접두사 코드여야 하며 [허프만이 이러한 숫자들을 찾는 데 도움이 됩니다.](https://en.wikipedia.org/wiki/Huffman_coding) 몇 가지 문제가 있음에도 **비디오 코덱들은 여전히 이 방법을 제공**하며 압축을 필요로하는 많은 애플리케이션들을 위한 알고리즘이기도 합니다.
엔코더와 디코더 모두 **반드시 이 코드가 있는 심볼 테이블을 알고 있어야 합니다.** 그러므로 테이블도 함께 보내야하죠.
### 산술적 코딩:
심볼 스트림이 있다고 가정해봅시다: **a**, **e**, **r**, **s**, **t**이며 이 들의 확률은 다음 테이블과 같이 표현됩니다.
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| 확률 | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
이 테이블을 참고하여 빈도순으로 정렬된 모든 심볼을 포함하는 범위를 만들 수 있습니다.
이제 스트림 **eat**를 인코딩해보죠. 하위 범위 **0.3부터 0.6** 안에 있는 (그러나 포함되진 않음) 첫 번째 심볼 **e**를 선택하고 이 하위 범위를 가지고 이전에 사용한 것과 동일한 비율로 분할해 새로운 범위 내에서 다시 분할합니다.
스트림 **eat**를 이어서 인코딩 해봅시다. 새로운 하위 범위 **0.3부터 0.39**에 포함된 두 번째 심볼 **e**를 취한 뒤 마지막 심볼 **t**로 동일한 과정을 반복하여 마지막 하위 범주인 **0.354부터 0.372**를 얻게됩니다.
마지막 하위 범위인 **0.354부터 0.372**내에 있는 숫자를 선택해봅시다. **0.36**을 골라보죠. 이 하위 범주에 내에 있는 수라면 어떤 것이든 상관 없습니다. 그리고 **이 숫자만 있으면** 원본 스트림인 **eat**를 복구할 수 있습니다. 이는 스트림을 인코딩할 범위의 범위 안에 선을 긋는 것과 같습니다.
**역과정** (소위 말하는 디코딩)은 인코딩과 동일하게 간단합니다. 선택했던 숫자인 **0.36**으로 동일한 과정을 통하여 원래의 범위를 구하는데, 이번에는 이 숫자를 인코딩된 스트림을 밝혀내는데 사용합니다.
첫 번째 범위에서 앞서 선택한 숫자(0.36)가 슬라이스와 일치한다는 사실을 알 수 있습니다. 그러므로 이게 첫 번째 심볼이며 이제 하위 범위로 분할할 차례입니다. 이전과 동일한 과정으로 **0.36**이 심볼 **a**와 일치한다는 사실을 파악할 수 잇죠. 이 과정을 반복하고 나면 마지막 심볼인 **t**에 이르게 됩니다. (원본의 인코딩 된 스트림 *eat*를 구성함)
엔코더와 디코더 모두 심볼 확률 테이블에 대해 **반드시 알고 있어야 합니다.** 그러므로 이 테이블도 보내야겠죠.
깔끔하쥬? 그쥬? 이 해법을 떠올린 사람들은 겁나 똑똑합니다. 몇몇 [비디오 코덱](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding)은 이 기법을 사용합니다. (적어도 이를 옵션으로 제공하거나 말이죠)
이 아이디어는 무손실로 양자화된 비트스트림을 압축하는 것입니다. 이 글은 아주 많은 세부사항, 이유, 절충안 등등이 생략되어 있습니다. 그러나 개발자로서 [더 배우셔야 합니다](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/). 최신 코덱은 다른 종류의 [ANS와 같은 엔트로피 코딩 알고리즘](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)을 사용하려는 시도를 하고 있습니다.
> ### 예제: CABAC vs CAVLC
> [CABAC와 CAVLC 이 두 개의 스트림을 생성](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc)하고 각각의 스트림이 생성하는데 소모하는 **시간을 비교**해 보세요. 또, **결과물의 사이즈도** 비교해보세요.
## 여섯번째 단계 - 비트 스트림 포맷
이 모든 과정을 마친 후에는 **압축된 프레임과 이 과정들에 대한 컨텍스트를 패킹**해야합니다. **엔코더가 결정한 내용들**, 가령 비트 깊이, 색공간, 해상도, 프리딕션 정보(모션 벡터, 인트라 프리딕션 방향), 프로파일, 레벨, 프레임레이트, 프레임 타입, 프레임 넘버 등의 무수히 많은 정보들에 대하여 디코더에게 이를 명시적으로 알려주어야 합니다.
이제 겉핥기로 H.264 비트스트림을 공부해볼거에요. 첫 번째 단계는 [미니멀한 H.264 * 비트스트림](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream)을 생성하는 것으로, 이 저장소와 [ffmpeg](http://ffmpeg.org/)를 사용하면 되지요.
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * ffmpeg는 기본 값으로 모든 인코딩 파라미터에 **SEI NAL**을 붙이는데 NAL이 무엇인지 곧 정의 해볼 것입니다.
이 명령어는 **단일 프레임**, 64x64, 색공간 yuv420의 로우 h264 비트스트림을 생성하며 프레임으로는 다음 이미지를 사용할거에요.
> 
### H.264 비트스트림
AVC(H.264) 표준은 **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (네트워크 추상 계층)이라 부르는 **매크로 프레임** (네트워크 용어)에 전송하는 정보를 정의합니다. NAL의 주된 목적은 "네트워크 친화적" 인 비디오 표현의 프로비전으로, 이 표준은 반드시 TV(스트림 기반), 인터넷(패킷 기반) 등을 비롯한 것들에서 반드시 동작해야합니다.
NAL 유닛의 경계를 정의하는 [동기화 마커](https://en.wikipedia.org/wiki/Frame_synchronization)라는 것이 있습니다. 각 동기화 마커는 맨 첫 번째인 `0x00 0x00 0x00 0x01`를 제외하고 `0x00 0x00 0x01`의 값을 가집니다. 앞서 생성한 h264 비트스트림에 **hexdump**를 떠보면, 파일 시작 부분에 세계의 `NAL`을 확인할 수 있습니다.
앞서 말씀드린 바와 같이, 디코더는 사진 정보 뿐만 아니라 비디오의 세부사항, 색, 사용된 파라미터 등의 여러 정보를 필요로 합니다. 각 NAL의 **첫 번째 바이트**는 이에 대한 카테고리와 **타입**을 정의합니다.
| NAL type id | Description |
|--- |---|
| 0 | 정의되지 않음 |
| 1 | 인코딩된 non-IDR 사진 슬라이스 |
| 2 | 인코딩된 슬라이스 데이터 파티션 A |
| 3 | 인코딩된 슬라이스 데이터 파티션 B |
| 4 | 인코딩된 슬라이스 데이터 파티션 C |
| 5 | **IDR** 인코딩의 IDR 사진 슬라이스 |
| 6 | **SEI** 추가 보완 정보 |
| 7 | **SPS** 시퀀스 파라미터 셋 |
| 8 | **PPS** 사진 파라미터 셋 |
| 9 | 엑세스 단위 델리미터 |
| 10 | 시퀀스의 끝 |
| 11 | 스트림의 끝 |
| ... | ... |
보편적으로 비트스트림의 첫 번째 NAL은 **SPS**입니다. 이 타입은 **프로파일**, **레벨**, **해상도** 등과 같은 일반적인 인코딩 변수를 알려주는 역할을 합니다.
동기화 마커를 건너 뛰는 경우 **첫 번째 바이트**를 디코드하여 **어떤 타입의 NAL**이 첫 번째 바이트인지 알아낼 수 있습니다.
동기화 마커 다음의 첫 번째 바이트는 `01100111`입니다. 이 첫 번째 비트(`0`)는 **forbidden_zero_bit**에 해당하며, 다음 2비트(`11`)는 해당 필드가 이 NAL이 레퍼런스 필드인지 아닌지를 가리키는 **nal_ref_idc**를 알려주며, 나머지 5비트(`00111`)은 `nal_unit_type` 필드를 알려줍니다. 이 경우에는 **SPS** (7) NAL 유닛입니다.
SPS NAL의 두 번째 바이트(`binary=01100100, hex=0x64, dec=100`)는 인코더가 사용한 프로파일을 보여주는 **profile_idc** 필드로, 이 경우에는 **[constrained high-profile](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)** 를 사용했습니다. 이는 B(bi-predictive) 슬라이스의 지원이 없는 하이프로파일입니다.
SPS NAL에 대한 H.264 비트스트림 스펙을 읽는 경우, **파라미터 이름**, **카테고리** 그리고 **디스크립션**에 대한 많은 값을 참고할 것입니다. 예를들어 `pic_width_in_mbs_minus_1`와 `pic_height_in_map_units_minus_1`필드를 봅시다.
| Parameter name | Category | Description |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: unsigned integer [Exp-Golomb-coded](https://pythonhosted.org/bitstring/exp-golomb.html)
이 필드들의 값으로 계산을 때려보면, **해상도**에 이르게 됩니다. `119 ( (119 + 1) * macroblock_size = 120 * 16 = 1920) `의 값과 `pic_width_in_mbs_minus_1`를 사용하여 `1920 x 1080`를 표현할 수 있습니다. `1920`을 인코딩하는 대신 `119`를 사용하여 또 공간을 절약한것이지요.
바이너리 뷰(ex: `xxd -b -c 11 v/minimal_yuv420.h264`)로 앞서 생성한 비디오를 계속하여 검사하는 경우, frame 자체를 의미하는 마지막 NAL은 생략할 수 있습니다.
앞서 생성한 비디오의 첫 번째 6바이트의 값을 볼 수 있습니다: `01100101 10001000 10000100 00000000 00100001 11111111`. 이미 아는 바와 같이 첫 번째 바이트는 NAL의 타입을 의미하며, 이 경우에는 (`00101`)이며 이는 **IDR Slice (5)** 이고 더 자세히 확인해보면:
스펙 정보를 바탕으로 슬라스의 타입(**slice_type**), 프레임 번호(**frame_num**)을 비롯한 다른 중요한 필드들을 디코딩할 수 있습니다.
몇몇 필드(`ue(v), me(v), se(v) 혹은 te(v)`)의 값을 얻기 위해, [Exponential-Golomb](https://pythonhosted.org/bitstring/exp-golomb.html)라 부르는 특별한 디코더를 사용하여 이를 디코딩할 수 있습니다. 이 방법은 대부분 수많은 기본 값들이 존재하는 경우에 **변수 값을 인코딩하는데에 있어 매우 효과적입니다.**
> **slice_type**의 값과 이 비디오의 **frame_num**는 각각 7(I slice)과 0 (첫 번째 프레임)입니다.
**비트스트림을 프로토콜**로 볼 수 있으며 이 비트스트림에 대하여 더 공부하고 싶으시다면 [ITU H.264 spec.](http://www.itu.int/rec/T-REC-H.264-201610-I)를 살펴보세요. 다음은 사진 데이터(압축된 YUV)가 어디에 위치하는지 나타내는 매크로 다이어그램입니다.
[VP9 bitstream](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf)와 같은 다른 비트스트림에 대해서도 살펴볼 수 있습니다. 혹은 우리의 새 베프 [**AV1** bitstream](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
)도 말이죠. [모든 비트스트림들이 비슷할까요? 응 아니야](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/), 하지만 한 번 터득하고 나면 다른 것들도 쉽게 배울 수 있습니다.
> ### 실습: H.264 비트스트림 살펴보기
> [단일 프레임의 비디오를 만들고](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) [mediainfo](https://en.wikipedia.org/wiki/MediaInfo)를 사용하여 생성한 비디오의 H.264 비트스트림을 조사해볼 수 있습니다. 게다가 [h264(AVC)를 파싱하는 소스코드](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp)도 볼 수 있지요.
>
> 
>
> [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer)는 유료이긴 하지만 트라이얼 버전에서는 초반의 10프레임 제한으로 사용해보실 수 있습니다. 학습 용도로는 충분합니다.
>
> 
## 리뷰
**현대 코덱의 대부분은 지금까지 학습한 것과 동일한 모델을 사용한다는 사실**을 명심해주세요. 실제로 Thor 비디오 코덱의 블록 다이어 그램을 살펴봅시다. 여기에는 앞서 학습한 모든 단계가 포함되어 있습니다. 이 아이디어는 이 분야에 대한 혁신과 논문들에 대해 최소한의 이해를 필요로 합니다.
이전에 계산했던 [한 시간 분량의 720p 해상도를 가진 30fps 비디오를 저장하기 위해 139GB의 용량](#%ED%81%AC%EB%A1%9C%EB%A7%88-%EC%84%9C%EB%B8%8C%EC%83%98%ED%94%8C%EB%A7%81)이 필요했었습니다. 여기서 배운 **인터 프리딕션, 인트라 프리딕션, 변환, 양자화, 엔트로피 코딩 등**의 기법을 사용하면, 대략 **픽셀 당 0.031비트**를 소비하며, 볼 수 있을만한 수준의 품질의 비디오에 **139GB와 달리 367.82MB의 용량만을 필요로** 합니다.
> 여기서 제공된 예제 비디오를 기반으로 **픽셀 당 0.031비트**를 사용하도록 선택하였습니다.
## 어떻게 H.265는 H.264보다 더 높은 압축률을 달성했는가
이제 코덱이 어떻게 동작하는지에 대해 많이 배웠으니, 새로운 코덱들이 적은 비트를 가지고 더 높은 해상도를 제공하는 방법들에 대해 쉽게 이해하실 수 있을거에요.
AVC와 HEVC를 비교해볼텐데요, CPU 싸이클(복잡도)와 압축률 간에는 항상 트레이드 오프가 존재한다는 사실을 명심하세요.
HEVC는 AVC에 비해 더 크고 더 많은 **파티션(그리고 서브 파티션)** 옵션, **인트라 프리딕션 방향**, **향상된 엔트로피 코딩** 등을 가지고 있습니다. 이러한 모든 향상은 H.265가 H.264보다 50% 높은 압축 성능을 발휘할 수 있게 해주었죠.
# 온라인 스트리밍
## 일반적인 아키텍쳐
[TODO]
## 점진적 다운로드와 적응형 스트리밍
[TODO]
## 컨텐츠 보호
**간단한 토큰 시스템**을 사용하여 컨텐츠를 보호할 수 있습니다. 토큰이 없는 사용자가 비디오를 요청하면 CDN은 사용자를 막고, 유효한 토큰을 지닌 사용자라면 컨텐츠를 재생할 수 있도록 말이죠. 이는 대부분의 웹 인증 시스템과 굉장히 유사합니다.
이 토큰 시스템만 사용하면 사용자가 비디오를 다운로드 받아 배포가 가능합니다. 그러나 **DRM(디지털 저작권 관리)** 시스템은 이를 막을 수 있습니다.
실제 현업의 시스템과 실무자들은 두 기법을 사용하여 인증과 인가를 제공합니다.
### DRM
#### Main systems
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### 무엇인가?
DRM은 디지털 저작권 관리를 의미하며, **디지털 미디어에 대한 권리 보호**를 제공합니다. 많은 곳에서 사용되고 있으나 [보편적으로는 수긍받지 못하고 있습니다](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### 왜?
컨텐츠 제작자(대부분 스튜디오들)들은 저작물의 무단 배포를 막아 자신의 지적 재산권을 보호하고자 합니다.
#### 어떻게?
극도로 간략화된 DRM의 추상적이고 일반적인 형태만 소개할 것입니다.
**컨텐츠 C1**(예를들어 HLS나 dash 비디오 스트리밍)을 **DRM 시스템 DRM1**(와이드바인, 플레이레디, 페어플레이)을 사용하는 **디바이스 D1**(예를들어 스마트폰, TV, 태블릿, 데스크탑, 노트북)에서 **플레이어 P1**(예를들어 샤카 클래퍼, 엑소 플레이어, ios)로 본다고 해봅시다.
컨텐츠 C1은 시스템 DRM1의 **대칭키 K1**으로 암호화되어 **암호화된 컨텐츠 C'1**을 생성합니다.
디바이스 D1의 플레이어 P1은 **개인키 PRK1**(이 키는 보호되어 있으며1 **D1**만 알고 있음)와 **공개키 PUK1**라는 두 개의 키(비대칭)를 가지고 있습니다.
> **1보호됨**: [블랙박스](https://en.wikipedia.org/wiki/Black_box)처럼 동작하는 내부의 특별한(읽기 전용) 칩에 저장된 키로 해독을 제공하는 **하드웨어를 통한** 방법이 있고, 혹은 DRM 시스템이 제공하는 **소프트웨어를 통한**(덜 안전함) 방법이 있습니다. 이 보호는 해당 디바이스가 어떤 방법(하드웨어적, 소프트웨어적)을 가지고 있는지 알 수 있습니다.
**플레이어 P1이 컨텐츠 C'1을 보고 싶은 경우**, 공개키 **PUK1**를 부여한 **DRM 시스템 DRM1**를 통해 처리해야합니다. DRM시스템 DRM1는 클라이언트의 공개키 **PUK1**를 가지고 **암호화된 키 K1**을 반환합니다. 이론적으로 이 응답은 **D1만이 해독할 수 있습니다**.
`K1P1D1 = enc(K1, PUK1)`
**P1**은 DRM 로컬 시스템([SOC](https://en.wikipedia.org/wiki/System_on_a_chip)가 해당될 수 있으며, 특화된 하드웨어나 소프트웨어)을 사용하며, 이 시스템은 개인키 PRK1을 사용한 컨텐츠를 **해독 가능**합니다. **K1P1D1로부터 비대칭 키 K1**를 해독할 수 있으며 **C'1을 재생할 수 있습니다**. 가장 좋은 점은 RAM을 통해 이 키들이 노출이 되지 않는 다는 것이지요.
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# How to use jupyter
Make sure you have **docker installed** and just run `./s/start_jupyter.sh` and follow the instructions on the terminal.
# Conferences
* [DEMUXED](https://demuxed.com/) - you can [check the last 2 events presentations.](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# References
The richest content is here, it's where all the info we saw in this text was extracted, based or inspired by. You can deepen your knowledge with these amazing links, books, videos and etc.
Online Courses and Tutorials:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Books:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Bitstream Specifications:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
Software:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
Non-ITU Codecs:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml
* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
Encoding Concepts:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
Video Sequences for Testing:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
Miscellaneous:
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* http://www.csc.villanova.edu/~rschumey/csc4800/dct.html
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
================================================
FILE: README-pt.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
[](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)
# Introdução
Essa é uma rápida introdução a tecnologia de vídeo para pessoas desenvolvedoras de software. Entretanto, queremos que esse documento seja fácil o suficiente **para qualquer pessoa aprender**. A idéia nasceu de uma [pequena oficina para pessoas interessadas em tecnologia de vídeo](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p).
O objetivo principal do documento é introduzir alguns conceitos de vídeo digital utilizando um **vocabulário simples, elementos visuais e exemplos práticos**. Além disso, gostaríamos que esse conhecimento estivesse disponível em qualquer lugar. Por favor, fique à vontade para enviar correções, sugestões e melhorias.
Haverão exercícios práticos que irão necessitar do **docker instalado** e esse repositório clonado.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **ATENCÃO**: quando você ver um comando `./s/ffmpeg` ou `./s/mediainfo`, isso quer dizer que estamos executando uma **versão do programa dentro de um container**, sendo assim, esse programa já possui todas as dependências instaladas e configuradas para sua execução.
Todos os **exercícios práticos devem ser executados da pasta que você clonou originalmente** esse repositório. Para os **exemplos que utilizam Jupyter**, você deve inicializar o servidor utilizando `./s/start_jupyter.sh` e acessar a URL no seu browser.
# Registro de alterações
* Adicionado detalhes sobre sistemas de DRM (Digital Rights Management)
* Versão 1.0.0 lançada
* Adicionado tradução para chinês simplificado
* Adicionado exemplo de filtro osciloscópio de FFmpeg
# Índice
- [Introdução](#introdução)
- [Índice](#índice)
- [Terminologia Básica](#terminologia-básica)
* [Outras maneiras de codificar uma imagem em cores](#outras-maneiras-de-codificar-uma-imagem-colorida)
* [Prática: exercício com imagem e cor](#prática-brincando-com-imagens-e-cores)
* [DVD é DAR 4:3](#dvd-é-dar-43)
* [Prática: Verificar propriedades de vídeo](#prática-brincando-com-imagens-e-cores)
- [Remoção de redundância](#remoção-de-redundância)
* [Cores, Luminância e os nossos olhos](#cores-luminância-e-nossos-olhos)
+ [Modelo de cores](#modelo-de-cor)
+ [Conversão entre YCbCr e RGB](#conversão-entre-ycbcr-e-rgb)
+ [Subamostragem Chroma](#subamostragem-chroma)
+ [Prática: Verifique um histograma YCbCr](#prática-verifique-um-histograma-ycbcr)
* [Tipos de Frames](#tipos-de-frames)
+ [I frame (intra, keyframe)](#i-frame-intra-keyframe)
+ [P frame (predicted)](#p-frame-predicted)
- [Prática: Um vídeo com somente um I-frame](#hands-on-a-video-with-a-single-i-frame)
+ [B Frame (bi-predictive)](#b-frame-bi-predictive)
- [Prática: Compare vídeos com B-frame](#hands-on-compare-videos-with-b-frame)
+ [Sumário](#sumário)
* [Redundância Temporal (inter prediction)](#redundância-temporal-inter-prediction)
- [Prática: Veja os vetores de movimento](#prática-veja-os-vetores-de-movimento)
* [Redundância Espacial (intra prediction)](#redundância-espacial-intra-prediction)
- [Prática: Verifique intra prediction](#prática-verifique-intra-prediction)
- [Como um codec de vídeo funciona?](#como-um-codec-de-vídeo-funciona)
* [O quê? Por quê? Como?](#o-quê-por-quê-como)
* [História](#história)
+ [O nascimento de AV1](#o-nascimento-de-av1)
* [Um codec genérico](#um-codec-genérico)
* [Primeiro passo - particionando uma figura](#primeiro-passo-particionando-uma-figura)
+ [Prática: Verifique as partições](#prática-verifique-as-partições)
* [Segundo passo - previsões](#segundo-passo-previsões)
* [Terceiro passo - transformação](#terceiro-passo-transformação)
+ [Prática: jogando com diferentes coeficientes](#prática-jogando-com-diferentes-coeficientes)
* [Quarto passo - quantização](#quartopasso-quantização)
+ [Prática: quantização](#prática-quantização)
* [Quinto passo - Codificando entropia](#quinto-passo-codificando-entropia)
+ [Código VLC](#código-vlc)
+ [Código Aritmético](#código-aritmético)
+ [Prática: CABAC vs CAVLC](#prática-jogando-com-diferentes-coeficientes)
* [Sexto passo - formato bitstream](#sexto-passo-formato-bitstream)
+ [H.264 bitstream](#h264-bitstream)
+ [Prática: Inspecionar o H.264 bitstream](#prática-inspecionar-o-h264-bitstream)
* [Revisão](#revisão)
* [Como H.265 consegue uma melhor razão de compressão comparado com H.264?](#como-h265-consegue-uma-melhor-razão-de-compressão-comparado-com-h264)
- [Transmissão online](#transmissão-online)
* [Arquitetura geral](#arquitetura-geral)
* [Download progressivo e transmissão adaptativa](#download-progressivo-e-transmissão-adaptativa)
* [Proteção de conteúdo](#proteção-de-conteúdo)
- [Como usar o Jupyter](#como-usar-o-jupyter)
- [Conferências](#conferências)
- [Referências](#referências)
# Terminologia Básica
Uma **imagem** podem ser pensada como uma **matriz 2d**. Se nós começamos a pensar sobre **cores**, podemos extrapolar essa idéia inicial e ver essa imagem como uma **matriz 3d** onde as **dimensões adicionais** são utilizadas para armazenar **informações de cor**.
Se escolhemos representar essas cores com [cores primárias (vermelho, verde and azul)](https://pt.wikipedia.org/wiki/Cor_prim%C3%A1ria), podemos definir três planos: o primeiro plano para o **vermelho**, o segundo para o **verde** e o último para a cor **azul**.
Iremos chamar cada ponto nessa matriz de **um pixel** (picture element). Um pixel representa a **intensidade** (geralmente um valor numérico) de uma dada cor. Por exemplo, um **pixel vermelho** significa 0 de verde, 0 de azul e o máximo de vermelho. O **pixel cor rosa** pode ser formado a partir de uma combinação de três cores. Usando uma representação numérica de 0 até 255, onde o pixel rosa é definido por **Vermelho=255, Verde=192 e Azul=203**.
> #### Outras maneiras de codificar uma imagem colorida
> Muitos outros possíveis modelos podem ser utilizados para representar uma imagem colorida. Nós podemos, por exemplo, utilizar uma paleta indexada de cores, onde um único byte representa cada pixel, ao invés de três bytes quando utilizando o modelo RGB. Em um modelo como esse, podemos utilizar uma matriz 2D ao invés de uma matriz 3D para representar cor. Dessa maneira, nós iremos salvar memória mas teremos potencialmente menos opções de cores.
>
> 
Por exemplo, olhe para a figura abaixo. O primeiro rosto ali é totalmente colorido. Os outros rostos são planos vermelho, verde e azul (mostrados em tons de cinza).
Nós podemos ver que a **cor vermelha** será a cor que **contribuíra mais** (as partes mais brilhantes no segundo rosto) para a cor final, enquanto que a contribuição da **cor azul** pode ser vista **mais concentrada nos olhos do Mário** (último rosto) e em algumas partes de suas roupas. Perceba como **todos os planos de cores** (partes escuras) contribuem menos para o **bigode do Mário**.
Cada intensidade de cor requer um determinada quantidade de bits, essa quantidade é chamada de **profundidade de bit**. Digamos que vamos gastar **8 bits** (tendo como valores válidos 0 até 255) por cor (plano), dessa forma, nós temos uma **profundidade de cor** de **24 bits** (8 bits vezes 3 planos RGB). Também podemos inferir que podemos utilizar 2 na 24 (2^24) diferentes cores.
> **É ótimo** para aprender [como uma imagem se transforma em bits](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm).
Uma outra propriedade de uma imagem é a **resolução**, que é basicamente o número de pixels em uma dimensão. É geralmente representada por altura x largura, por exemplo: a imagem **4x4** abaixo:
> #### Prática: brincando com imagens e cores
> Você pode [brincar com imagens e cores](/image_as_3d_array.ipynb) utilizando [jupyter](#how-to-use-jupyter) (python, numpy, matplotlib, etc).
>
> Você também pode aprender [como filtros de imagem (detecção de bordas, nitidez, desfoque...) funcionam](/filters_are_easy.ipynb).
Uma outra propriedade que podemos perceber enquanto trabalhamos com imagens ou vídeo é a **proporção da tela** que descreve um relacionamento proporcional entre altura e largura de uma imagem ou pixel.
Quando as pessoas falam que um filme ou foto é **16x9** geralmente elas estão se referindo ao **Display Aspect Ratio (DAR)**, entretanto nós também podemos ter diferentes formas de pixels e podemos chamar isso de **Pixel Aspect Ratio (PAR)**.
> #### DVD é DAR 4:3
> Mesmo que a resolução real de um DVD seja 704x480, a proporção é mantida em 4:3 considerando que esse filme possui PAR de 10:11 (704x10/480x11)
Finalmente, podemos definir um **vídeo** como uma **sucessão de *n* frames** no **tempo** que podemos perceber como uma outra dimensão, onde *n* é a quantidade de quadros por segundo ou frames per second (FPS).
O número de bits por segundo necessários para exibir um vídeo é sua **taxa de bits**.
> taxa de bits = largura * altura * profundidade de bits * quadros por segundo
Por exemplo, um vídeo com 30 frames por segundo, 24 bits por pixel, resolução de 480x240 necessitará de **82.944.000 bits por segundo** ou 82.944 Mbps (30x480x240x24) se não empregarmos nenhum tipo de compressão.
Quando a **taxa de bits** é quase constante, ela é chamada de taxa de bits constante (**CBR**), mas também pode variar, chamada de taxa de bits variável (**VBR**).
> Este gráfico mostra um VBR restrito que não gasta muitos bits enquanto o quadro é preto.
>
> 
No início, os engenheiros criaram uma técnica para dobrar a taxa de quadros percebida de uma exibição de vídeo **sem consumir largura de banda extra**. Essa técnica é conhecida como **vídeo entrelaçado**; basicamente envia metade da tela em 1 "quadro" e a outra metade no próximo "quadro".
Hoje, as telas são renderizadas principalmente usando a **técnica de varredura progressiva**. Progressivo é uma forma de exibir, armazenar ou transmitir imagens em movimento em que todas as linhas de cada quadro são desenhadas em sequência.
Agora temos uma ideia de como uma **imagem** é representada digitalmente, como suas **cores** são arranjadas, quantos **bits por segundo** gastamos para mostrar um vídeo, se é constante (CBR) ou variável (VBR), com uma determinada **resolução** usando uma determinada **taxa de quadros** e muitos outros termos como entrelaçado, PAR e outros.
> #### Exercício prático: Verifique as propriedades do vídeo
> Você pode [verificar a maioria das propriedades explicadas com ffmpeg ou mediainfo.](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)
# Remoção de redundância
Aprendemos que não é viável usar vídeo sem compressão; **um único vídeo de uma hora** em resolução de 720p com 30fps **requer 278 GB***. Como **usar apenas algoritmos de compactação de dados sem perdas** como DEFLATE (usado em PKZIP, Gzip e PNG), **não** diminuirá suficientemente a largura de banda necessária, precisamos encontrar outras maneiras de compactar o vídeo.
> * Encontramos esse número multiplicando 1280 x 720 x 24 x 30 x 3600 (largura, altura, bits por pixel, fps e tempo em segundos)
Para fazer isso, podemos **explorar como nossa visão funciona**. Distinguimos melhor o brilho do que as cores, as **repetições no tempo**, um vídeo contém muitas imagens com poucas alterações, e as **repetições dentro da imagem**, cada quadro também contém muitas áreas usando o mesmo ou cor semelhante.
## Cores, Luminância e nossos olhos
Nossos olhos são [mais sensíveis à luminosidade do que às cores](http://vanseodesign.com/web-design/color-luminance/), você pode testar isso por si mesmo, olhe para essa imagem.
Se você não consegue ver que as cores dos **quadrados A e B são idênticas** no lado esquerdo, tudo bem, é nosso cérebro nos enganando para **prestarmos mais atenção à luz e à escuridão do que à cor**. Há um conector, da mesma cor, no lado direito para que possamos (nosso cérebro) facilmente perceber que, na verdade, são a mesma cor.
> **Explicação simplista de como nossos olhos funcionam**
> O [olho é um órgão complexo](http://www.biologymad.com/nervoussystem/eyenotes.htm), é composto por muitas partes, mas estamos principalmente interessados nas células cones e bastonetes. O olho [contém cerca de 120 milhões de bastonetes e 6 milhões de células cones](https://en.wikipedia.org/wiki/Photoreceptor_cell).
>
> Para **simplificar bastante**, vamos tentar colocar as cores e a luminosidade na função das partes do olho. As **[células bastonetes](https://en.wikipedia.org/wiki/Rod_cell) são principalmente responsáveis pela luminosidade** enquanto as **[células cones](https://en.wikipedia.org/wiki/Cone_cell) são responsáveis pela cor**, existem três tipos de cones, cada um com um pigmento diferente, a saber: [S-cones (azul), M-cones (verde) e L-cones (vermelho)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> Uma vez que temos muitas mais células bastonetes (luminosidade) do que células cones (cor), pode-se inferir que somos mais capazes de distinguir entre claro e escuro do que cores.
>
> 
>
> **Funções de sensibilidade ao contraste**
>
> Pesquisadores da psicologia experimental e muitos outros campos desenvolveram muitas teorias sobre a visão humana. E uma delas é chamada de funções de sensibilidade ao contraste. Elas estão relacionadas ao espaço e ao tempo da luz e seu valor indica, para uma determinada intensidade de luz inicial, quanto mudança é necessária antes que um observador informe que houve uma mudança. Observe o plural da palavra "função", isso ocorre porque podemos medir as funções de sensibilidade ao contraste não apenas em preto e branco, mas também em cores. O resultado desses experimentos mostra que, na maioria dos casos, nossos olhos são mais sensíveis ao brilho do que à cor.
Uma vez que sabemos que somos mais sensíveis à **luma** (a luminosidade em uma imagem), podemos tentar explorá-la.
### Modelo de cor
Aprendemos primeiro [como funcionam as imagens coloridas](#terminologia-básica) usando o **modelo RGB** (consulte terminologia básica), mas existem outros modelos também. Na verdade, existe um modelo que separa a luminância (brilho) da crominância (cores) e é conhecido como YCbCr*.
> * existem mais modelos que fazem a mesma separação.
Este modelo de cor usa **Y** para representar o brilho e dois canais de cor **Cb** (azul cromático) e **Cr** (vermelho cromático). O [YCbCr](https://en.wikipedia.org/wiki/YCbCr) pode ser derivado do RGB e também pode ser convertido de volta para o RGB. Usando este modelo, podemos criar imagens totalmente coloridas, como podemos ver abaixo.
### Conversão entre YCbCr e RGB
Alguns podem argumentar, como podemos produzir todas as **cores sem usar o verde**?
Para responder a essa pergunta, vamos percorrer uma conversão de RGB para YCbCr. Usaremos os coeficientes do **[standard BT.601](https://en.wikipedia.org/wiki/Rec._601)** recomendado pelo **[grupo ITU-R*](https://en.wikipedia.org/wiki/ITU-R)**. O primeiro passo é **calcular a luminância**, usaremos as constantes sugeridas pelo ITU e substituiremos os valores RGB.
```
Y = 0.299R + 0.587G + 0.114B
```
Uma vez que temos a luminância, podemos **separar as cores** (crominância azul e vermelha):
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
E também podemos **convertê-la de volta** e até mesmo obter o **verde usando YCbCr**.
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * grupos e padrões são comuns em vídeo digital, eles geralmente definem quais são os padrões, por exemplo, [o que é 4K? qual taxa de quadros devemos usar? resolução? modelo de cores?](https://en.wikipedia.org/wiki/Rec._2020)
Geralmente, **monitores** (monitores, televisores, telas, etc.) utilizam **apenas o modelo RGB**, organizado de maneiras diferentes, veja alguns deles ampliados abaixo:
### Subamostragem Chroma
Com a imagem representada como componentes de luminância e crominância, podemos aproveitar a maior sensibilidade do sistema visual humano para a resolução de luminância em vez de crominância para remover seletivamente informações. A **subamostragem de crominância** é a técnica de codificação de imagens usando **menor resolução para crominância do que para luminância**.
Quanto devemos reduzir a resolução de crominância? Descobriu-se que já existem alguns esquemas que descrevem como lidar com a resolução e a combinação (`cor final = Y + Cb + Cr`).
Esses esquemas são conhecidos como sistemas de subamostragem e são expressos como uma razão de 3 partes - `a:x:y` - que define a resolução de crominância em relação a um bloco `a x 2` de pixels de luminância.
* `a` é a referência de amostragem horizontal (geralmente 4)
* `x` é o número de amostras de crominância na primeira linha de pixels `a` (resolução horizontal em relação a `a`)
* `y` é o número de mudanças de amostras de crominância entre a primeira e a segunda linhas de pixels `a`.
> Uma exceção a isso existe com 4:1:0, que fornece uma única amostra de crominância dentro de cada bloco `4 x 4` de resolução de luma.
Os esquemas comuns usados em codecs modernos são: **4:4:4** (sem subsampling), **4:2:2, 4:1:1, 4:2:0, 4:1:0 e 3:1:1**.
> Você pode acompanhar algumas discussões [para aprender mais sobre a subsampling de crominância](https://github.com/leandromoreira/digital_video_introduction/issues?q=YCbCr).
> **YCbCr 4:2:0 merge**
>
> Aqui está um trecho de uma imagem mesclada usando YCbCr 4:2:0, observe que gastamos apenas 12 bits por pixel.
>
> 
Aqui está um trecho de uma imagem mesclada usando YCbCr 4:2:0, observe que gastamos apenas 12 bits por pixel.
Você pode ver a mesma imagem codificada pelos principais tipos de subsampling de croma, as imagens na primeira linha são o YCbCr final, enquanto a última linha de imagens mostra a resolução croma. É realmente uma grande vitória para uma perda tão pequena.
Anteriormente, tínhamos calculado que precisávamos de [278GB de armazenamento para manter um arquivo de vídeo com uma hora de resolução 720p e 30fps](#remoção-de-redundância). Se usarmos **YCbCr 4:2:0** podemos **cortar pela metade esse tamanho (139 GB)***, mas ainda está longe do ideal.
> * encontramos esse valor multiplicando largura, altura, bits por pixel e fps. Anteriormente, precisávamos de 24 bits, agora precisamos apenas de 12.
> ### Prática: Verifique um histograma YCbCr
> Você pode [verificar o histograma YCbCr com o ffmpeg.](/encoding_pratical_examples.md#generates-yuv-histogram) Essa cena tem uma contribuição maior de azul, como mostrado pelo [histograma](https://en.wikipedia.org/wiki/Histogram).
>
> 
### Cor, luma, luminância, revisão de vídeo gamma
Assista a este incrível vídeo explicando o que é luma e aprenda sobre luminância, gamma e cor.
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
> ### Pratica: Verificar a intensidade do YCbCr
> Você pode visualizar a intensidade do Y para uma determinada linha de um vídeo usando o [filtro osciloscópio do FFmpeg]((https://ffmpeg.org/ffmpeg-filters.html#oscilloscope).).
> ```bash
> ffplay -f lavfi -i 'testsrc2=size=1280x720:rate=30000/1001,format=yuv420p' -vf oscilloscope=x=0.5:y=200/720:s=1:c=1
> ```
> 
## Tipos de Frames
Agora podemos prosseguir e tentar eliminar a **redundância temporal**, mas antes disso vamos estabelecer algumas terminologias básicas. Suponha que temos um filme com 30fps, aqui estão seus primeiros 4 quadros.
  
Podemos ver **muitas repetições** dentro dos quadros, como **o fundo azul,** que não muda do quadro 0 ao quadro 3. Para lidar com esse problema, podemos **categorizá-los abstratamente** em três tipos de quadros.
### I Frame (intra, keyframe)
Um quadro I (referência, chave, intra) é um **quadro autocontido**. Ele não depende de nada para ser renderizado, um quadro I se parece com uma foto estática. O primeiro quadro geralmente é um quadro I, mas veremos quadros I inseridos regularmente entre outros tipos de quadros.
### P Frame (predicted)
Um quadro P (previsão) aproveita o fato de que quase sempre a imagem atual pode ser **renderizada usando o quadro anterior.** Por exemplo, no segundo quadro, a única mudança foi a bola que se moveu para frente. Podemos **reconstruir o quadro 1, usando apenas a diferença e referenciando o quadro anterior**.
 <- 
> #### Hands-on: A video with a single I-frame
> Já que um quadro P usa menos dados, por que não codificar um [vídeo com apenas um quadro I e todos os outros quadros sendo P?](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)
>
> Depois de codificar esse vídeo, comece a assisti-lo e faça uma **busca por uma parte avançada** do vídeo, você notará que **leva algum tempo** para realmente ir para essa parte. Isso acontece porque um **quadro P precisa de um quadro de referência** (como um quadro I, por exemplo) para ser renderizado.
>
> Outro teste rápido que você pode fazer é codificar um vídeo usando apenas um quadro I e, em seguida, [codificá-lo inserindo um quadro I a cada 2s](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second) e **verificar o tamanho de cada versão**.
### B Frame (bi-predictive)
E quanto a referenciar quadros passados e futuros para proporcionar uma compressão ainda melhor?! Isso é basicamente o que um quadro B faz.
 <-  -> 
> #### Hands-on: Compare videos with B-frame
> Você pode gerar duas versões, uma com quadros B e outra com [nenhum quadro B](/encoding_pratical_examples.md#no-b-frames-at-all) e verificar o tamanho do arquivo, bem como a qualidade.
### Sumário
Esses tipos de quadros são usados para **proporcionar uma melhor compressão**. Veremos como isso acontece na próxima seção, mas por enquanto podemos pensar que o **quadro I (I-frame) é mais caro enquanto o P é mais barato, mas o mais barato é o quadro B (B-frame).**
## Redundância Temporal (inter prediction)
Vamos explorar as opções que temos para reduzir as **repetições no tempo**, esse tipo de redundância pode ser resolvido com técnicas de **inter-prediction**.
Vamos tentar **usar menos bits** para codificar a sequência de frames 0 e 1.
Uma coisa que podemos fazer é subtração, simplesmente **subtraindo o frame 1 do frame 0**, obtemos apenas o que precisamos para **codificar o residual**.
Mas e se eu te disser que há um **método melhor** que usa ainda menos bits?! Primeiro, vamos tratar o `frame 0` como uma coleção de partições bem definidas e, em seguida, tentaremos combinar os blocos do frame 0 no `frame 1`. Podemos pensar nisso como **estimativa de movimento**.
> ### Wikipedia - compensação de movimento por blocos
> "A **compensação de movimento por blocos** divide o quadro atual em blocos não sobrepostos, e o vetor de compensação de movimento **indica de onde esses blocos vêm** (uma crença comum é que o quadro anterior é dividido em blocos não sobrepostos, e os vetores de compensação de movimento indicam para onde esses blocos se movem). Os blocos de origem geralmente se sobrepõem no quadro de origem. Alguns algoritmos de compressão de vídeo montam o quadro atual a partir de peças de vários quadros transmitidos anteriormente."
Nós poderíamos estimar que a bola se moveu de `x=0, y=25` para `x=6, y=26`, os valores **x** e **y** são os **vetores de movimento**. Um **passo adicional** que podemos fazer para economizar bits é **codificar apenas a diferença** do vetor de movimento entre a última posição do bloco e a prevista, então o vetor de movimento final seria `x=6 (6-0), y=1 (26-25)`.
> Em uma situação do mundo real, essa **bola seria dividida em n partições** mas o processo é o mesmo.
Os objetos na cena se **movem de forma 3D**, a bola pode ficar menor quando se move para o fundo. É normal que **não encontremos a correspondência perfeita** para o bloco que tentamos encontrar. Aqui está uma visão superposta de nossa estimativa em comparação com a imagem real.
Mas podemos ver que quando aplicamos a **estimativa de movimento**, os **dados para codificar são menores** do que quando usamos apenas as técnicas de quadro delta.
> ### Como seria a compensação de movimento real
> Essa técnica é aplicada a todos os blocos, muitas vezes uma bola seria dividida em mais de um bloco.
> 
> Fonte: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
Você pode [praticar esses conceitos usando o jupyter](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Prática: Veja os vetores de movimento
> Podemos [gerar um vídeo com a interpolação (vetores de movimento) usando o ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> Ou podemos usar o [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que é pago, mas há uma versão de teste gratuita que limita você a trabalhar apenas com os primeiros 10 quadros).
>
> 
## Redundância Espacial (intra prediction)
Se analisarmos **cada quadro** em um vídeo, veremos que também há **muitas áreas que estão correlacionadas**.
Vamos caminhar por um exemplo. Esta cena é composta principalmente por cores azuis e brancas.
Este é um `I-frame` e **não podemos usar quadros anteriores** para fazer previsões, mas ainda assim podemos comprimi-lo. Vamos codificar a seleção do bloco vermelho. Se **olharmos para seus vizinhos**, podemos **estimar** que há uma **tendência de cores ao redor dele**.
Vamos **prever** que o quadro continuará a **espalhar as cores verticalmente**, o que significa que as cores dos pixels **desconhecidos terão os valores de seus vizinhos**.
Nossa **previsão pode estar errada**, por essa razão, precisamos aplicar essa técnica (**intra-prediction**) e depois **subtrair os valores reais**, o que nos dá o bloco residual, resultando em uma matriz muito mais compressível em comparação com a original.
Existem muitos tipos diferentes desse tipo de previsão. O que você vê aqui na imagem é uma forma de previsão planar direta, onde os pixels da linha acima do bloco são copiados linha por linha dentro do bloco. A previsão planar também pode envolver um componente angular, onde pixels tanto da esquerda quanto da parte superior são usados para ajudar a prever o bloco atual. E há também a previsão DC, que envolve a média das amostras imediatamente acima e à esquerda do bloco.
> #### Prática: Verifique intra prediction
> Você pode [gerar um vídeo com macroblocos e suas previsões usando o ffmpeg.](/encoding_pratical_examples.md#generate-debug-video) Por favor, verifique a documentação do ffmpeg para entender o [significado de cada cor de bloco](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes).
>
> 
>
> Ou podemos usar o [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que é pago, mas há uma versão de avaliação gratuita que limita você a trabalhar apenas com os primeiros 10 quadros).
>
> 
# Como um codec de vídeo funciona?
## O quê? Por quê? Como?
**O quê?** É um software / hardware que comprime ou descomprime vídeo digital. **Por quê?** O mercado e a sociedade exigem vídeos de alta qualidade com largura de banda ou armazenamento limitado. Lembra quando [calculamos a largura de banda necessária](#terminologia-básica) para 30 quadros por segundo, 24 bits por pixel, resolução de um vídeo de 480x240? Era **82,944 Mbps** sem aplicar compressão. É a única maneira de fornecer HD / FullHD / 4K em TVs e na Internet. **Como?** Daremos uma breve olhada nas principais técnicas aqui.
> **CODEC vs Container**
>
> Um erro comum que iniciantes frequentemente cometem é confundir o codec de vídeo digital e o [formato de contêiner de vídeo digital](https://en.wikipedia.org/wiki/Digital_container_format). Podemos pensar em **contêineres** como um formato de invólucro que contém metadados do vídeo (e possivelmente também de áudio), e o **vídeo comprimido** pode ser visto como sua carga útil.
>
> Geralmente, a extensão de um arquivo de vídeo define seu contêiner de vídeo. Por exemplo, o arquivo `video.mp4` provavelmente é um contêiner **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14)** e um arquivo chamado `video.mkv` provavelmente é um **[matroska](https://en.wikipedia.org/wiki/Matroska)**. Para ter certeza absoluta sobre o codec e o formato do contêiner, podemos usar o [ffmpeg ou mediainfo](/encoding_pratical_examples.md#inspect-stream).
## História
Antes de entrarmos nos detalhes de funcionamento interno de um codec genérico, vamos dar uma olhada para entender um pouco melhor sobre alguns codecs de vídeo antigos.
O codec de vídeo [H.261](https://en.wikipedia.org/wiki/H.261) nasceu em 1990 (tecnicamente em 1988), e foi projetado para trabalhar com **taxas de dados de 64 kbit/s**. Ele já usava ideias como subsampling de croma, bloco macro, entre outros. No ano de 1995, o padrão de codec de vídeo **H.263** foi publicado e continuou a ser estendido até 2001.
Em 2003, a primeira versão do **H.264/AVC** foi concluída. No mesmo ano, a **On2 Technologies** (anteriormente conhecida como Duck Corporation) lançou seu codec de vídeo como uma compressão de vídeo **lossy** e **livre de royalties** chamada **VP3**. Em 2008, o **Google comprou** esta empresa, lançando o **VP8** no mesmo ano. Em dezembro de 2012, o Google lançou o **VP9** e é **suportado por aproximadamente ¾ do mercado de navegadores** (incluindo mobile).
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)** é um novo codec de vídeo **livre de royalties** e de código aberto que está sendo projetado pela [Alliance for Open Media (AOMedia)](http://aomedia.org/), que é composta por **empresas como Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel e Cisco**, entre outras. A **primeira versão** 0.1.0 do codec de referência foi **publicada em 7 de abril de 2016**.
> #### O nascimento de AV1
>
> No momento do início de 2015, a Google estava trabalhando no [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1), a Xiph (Mozilla) estava trabalhando no [Daala](https://xiph.org/daala/) e a Cisco havia disponibilizado como software livre seu codec de vídeo livre de royalties chamado [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03).
>
> Então a MPEG LA anunciou pela primeira vez limites anuais para o HEVC (H.265) e taxas oito vezes mais altas que a do H.264, mas logo em seguida eles mudaram as regras novamente:
> * **sem limite anual**,
> * **taxa de conteúdo** (0,5% da receita) e
> * **taxas por unidade cerca de 10 vezes mais altas que o h264**.
>
> O [alliance for open media](http://aomedia.org/about/) foi criada por empresas fabricantes de hardware (Intel, AMD, ARM, Nvidia, Cisco), fornecedoras de conteúdo (Google, Netflix, Amazon), mantenedoras de navegadores (Google, Mozilla) e outras.
> As empresas tinham um objetivo comum, um codec de vídeo sem royalties e então nasceu o AV1 com uma [licença de patente muito mais simples](http://aomedia.org/license/patent/). **Timothy B. Terriberry** fez uma apresentação incrível, que é a fonte desta seção, sobre a [concepção, modelo de licença e estado atual do AV1](https://www.youtube.com/watch?v=lzPaldsmJbk).
>
> Você ficará surpreso ao saber que pode **analisar o codec AV1 pelo seu navegador**, acesse https://arewecompressedyet.com/analyzer/
>
> 
>
> PS: Se você quiser saber mais sobre a história dos codecs, é preciso aprender o básico por trás das [patentes de compressão de vídeo](https://www.vcodex.com/video-compression-patents/).
## Um codec genérico
Vamos apresentar os **principais mecanismos por trás de um codec de vídeo genérico**, mas a maioria desses conceitos são úteis e usados em codecs modernos, como VP9, AV1 e HEVC. Certifique-se de entender que vamos simplificar MUITO as coisas. Às vezes, usaremos um exemplo real (principalmente H.264) para demonstrar uma técnica.
## Primeiro passo - particionando uma figura
O primeiro passo é **dividir o quadro** em várias **partições, subpartições** e além.
**Mas por quê?** Existem muitas razões, por exemplo, quando dividimos a imagem, podemos trabalhar com previsões mais precisas, usando partições menores para as partes móveis menores, enquanto usamos partições maiores para um fundo estático.
Geralmente, os CODECs **organizam essas partições** em fatias (ou azulejos), macroblocos (ou unidades de árvore de codificação) e muitas subpartições. O tamanho máximo dessas partições varia, o HEVC define 64x64 enquanto o AVC usa 16x16, mas as subpartições podem atingir tamanhos de 4x4.
Lembra que aprendemos como são **tipos de quadros**?! Bem, você pode **aplicar essas ideias a blocos** também, portanto podemos ter I-Slice, B-Slice, I-Macroblock e etc.
> ### Prática: Verifique as partições
> Também podemos usar o [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (que é pago, mas há uma versão de teste gratuita que limita você a trabalhar apenas com os primeiros 10 quadros). Aqui estão as partições [VP9 analisadas](/encoding_pratical_examples.md#transcoding).
>
> 
## Segundo passo - previsões
Uma vez que temos as partições, podemos fazer previsões sobre elas. Para a [predição inter](#redundância-temporal-inter-prediction), precisamos **enviar os vetores de movimento e o residual** e para a [predição intra](#redundância-espacial-intra-prediction), nós **enviaremos a direção da predição e o residual** também.
## Terceiro passo - transformação
Depois de obtermos o bloco residual (`partição prevista - partição real`), podemos **transformá-lo** de uma maneira que nos permite saber quais **pixels podemos descartar** enquanto mantemos a **qualidade geral**. Existem algumas transformações para esse comportamento exato.
Embora existam [outras transformações](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms), vamos olhar mais de perto a transformada discreta de cosseno (DCT). As principais características da [**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform) são:
* **converte** blocos de **pixels** em blocos do mesmo tamanho de **coeficientes de frequência**.
* **compacta** energia, tornando fácil eliminar a redundância espacial.
* é **reversível**, ou seja, você pode voltar para os pixels.
> Em 2 de fevereiro de 2017, Cintra, R. J. e Bayer, F. M publicaram seu artigo [DCT-like Transform for Image Compression Requires 14 Additions Only](https://arxiv.org/abs/1702.00817).
Não se preocupe se você não entendeu os benefícios de cada ponto, tentaremos fazer alguns experimentos para ver o valor real disso.
Vamos pegar o seguinte **bloco de pixels** (8x8):
Que renderiza a seguinte imagem de bloco (8x8):
Quando **aplicamos a DCT** a este bloco de pixels, obtemos o **bloco de coeficientes** (8x8):
E se renderizarmos este bloco de coeficientes, obteremos esta imagem:
Como você pode ver, não se parece em nada com a imagem original, podemos perceber que o **primeiro coeficiente** é muito diferente de todos os outros. Este primeiro coeficiente é conhecido como coeficiente DC que representa **todas as amostras** na matriz de entrada, algo **semelhante a uma média**.
Este bloco de coeficientes tem uma propriedade interessante que é separar os componentes de alta frequência dos de baixa frequência.
Em uma imagem, a **maior parte da energia** estará concentrada nas [**frequências mais baixas**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm), então se transformarmos uma imagem em seus componentes de frequência e **descartarmos os coeficientes de frequência mais alta**, podemos **reduzir a quantidade de dados** necessários para descrever a imagem sem sacrificar muito a qualidade da imagem.
> frequência significa quão rápido um sinal está mudando
Vamos tentar aplicar o conhecimento que adquirimos no teste convertendo a imagem original para sua frequência (bloco de coeficientes) usando a DCT e depois descartando parte dos coeficientes menos importantes.
Primeiro, convertemos para o seu **domínio de frequência**.
Em seguida, descartamos parte (67%) dos coeficientes, principalmente a parte inferior direita.
Finalmente, reconstruímos a imagem a partir deste bloco de coeficientes descartados (lembre-se, precisa ser reversível) e comparamos com o original.
Como podemos ver, a imagem resultante se assemelha à imagem original, mas apresenta muitas diferenças em relação à mesma. Jogamos **fora 67,1875%** e ainda conseguimos algo semelhante ao original. Poderíamos descartar os coeficientes de forma mais inteligente para obter uma melhor qualidade de imagem, mas esse é o próximo tópico.
> **Cada coeficiente é formado usando todos os pixels**
>
> É importante observar que cada coeficiente não mapeia diretamente para um único pixel, mas é uma soma ponderada de todos os pixels. Este gráfico incrível mostra como o primeiro e o segundo coeficiente são calculados, usando pesos únicos para cada índice.
>
> 
>
> Fonte: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> Você também pode tentar [visualizar a DCT observando a formação de uma imagem simples](/dct_better_explained.ipynb) na base da DCT. Por exemplo, aqui está o [caractere A sendo formado](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT) usando o peso de cada coeficiente.
>
> 
> ### Prática: jogando com diferentes coeficientes
> Você pode experimentar com a [transformação DCT](/uniform_quantization_experience.ipynb).
## Quarto passo - quantização
Quando descartamos alguns dos coeficientes, na última etapa (transformação), de alguma forma fizemos uma forma de quantização. Nessa etapa, escolhemos perder informações (a parte **perdida**) ou, em termos simples, **quantizamos os coeficientes para alcançar a compressão**.
Como podemos quantizar um bloco de coeficientes? Um método simples seria uma quantização uniforme, em que pegamos um bloco, **dividimos por um único valor** (10) e arredondamos esse valor.
Como podemos **reverter** (re-quantizar) esse bloco de coeficientes? Podemos fazer isso **multiplicando o mesmo valor** (10) que dividimos primeiro.
Essa **abordagem não é a melhor** porque não leva em conta a importância de cada coeficiente, poderíamos usar uma **matriz de quantizadores** em vez de um único valor. Essa matriz pode explorar a propriedade da DCT, quantizando a maior parte do canto inferior direito e menos o canto superior esquerdo, a [JPEG usa uma abordagem semelhante](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html), você pode verificar o [código-fonte para ver essa matriz](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40).
> ### Prática: quantização
> Você pode experimentar [quantização](/dct_experiences.ipynb).
## Quinto passo - Codificando entropia
Depois de quantizarmos os dados (blocos/fatias/quadros de imagem), ainda podemos comprimi-los de forma lossless. Existem muitas maneiras (algoritmos) de comprimir dados. Vamos experimentar brevemente alguns deles, para uma compreensão mais profunda, você pode ler o incrível livro [Understanding Compression: Data Compression for Modern Developers](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/).
### Código VLC:
Vamos supor que temos uma sequência dos símbolos: **a**, **e**, **r** e **t** e sua probabilidade (de 0 a 1) é representada por esta tabela.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probabilidade | 0.3 | 0.3 | 0.2 | 0.2 |
Podemos atribuir códigos binários exclusivos (preferencialmente pequenos) para os símbolos mais prováveis e códigos maiores para os menos prováveis.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probabilidade | 0.3 | 0.3 | 0.2 | 0.2 |
| código binário | 0 | 10 | 110 | 1110 |
Vamos comprimir a sequência **eat**, assumindo que gastaríamos 8 bits para cada símbolo, gastaríamos **24 bits*8 sem qualquer compressão. Mas, se substituirmos cada símbolo por seu código, podemos economizar espaço.
O primeiro passo é codificar o símbolo **e** que é `10` e o segundo símbolo é **a** que é adicionado (não de uma maneira matemática) `[10][0]` e, finalmente, o terceiro símbolo **t**, que faz com que nosso bitstream comprimido final seja `[10][0][1110]` ou `1001110`, que requer apenas **7 bits** (3.4 vezes menos espaço que o original).
Observe que cada código deve ser um código prefixado único [Huffman pode ajudá-lo a encontrar esses números](https://en.wikipedia.org/wiki/Huffman_coding). Embora tenha alguns problemas, existem [codecs de vídeo que ainda oferecem](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding) esse método e é o algoritmo para muitas aplicações que requerem compressão.
Tanto o codificador quanto o decodificador **precisam saber** a tabela de símbolos com seus códigos, portanto, você também precisa enviar a tabela.
### Código Aritmético:
Vamos supor que temos um fluxo de símbolos: **a**, **e**, **r**, **s** e **t** e suas probabilidades são representadas por esta tabela.
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| probabilidade | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
Com essa tabela em mente, podemos construir intervalos contendo todos os símbolos possíveis classificados pelos mais frequentes.
Agora vamos codificar a sequência **eat**, escolhemos o primeiro símbolo **e**, que está dentro da subfaixa **0,3 a 0,6** (mas não incluso) e pegamos essa subfaixa e a dividimos novamente usando as mesmas proporções usadas antes, mas dentro dessa nova faixa.
Continuando a codificar nossa sequência **eat**, agora pegamos o segundo símbolo **a**, que está dentro da nova subfaixa **0,3 a 0,39**, e depois pegamos nosso último símbolo **t** e fazemos o mesmo processo novamente e obtemos a última subfaixa **0,354 a 0,372**.
Agora precisamos escolher um número dentro da última subfaixa **0,354 a 0,372**, vamos escolher **0,36**, mas poderíamos escolher qualquer número dentro dessa subfaixa. Com **apenas** esse número, seremos capazes de recuperar nossa sequência original **eat**. Se você pensar sobre isso, é como se estivéssemos desenhando uma linha dentro de faixas de faixas para codificar nossa sequência.
O **processo reverso** (também conhecido como decodificação) é igualmente fácil, com nosso número **0,36** e nossa faixa original, podemos executar o mesmo processo, mas agora usando esse número para revelar o fluxo codificado por trás desse número.
Com a primeira faixa, percebemos que nosso número se encaixa na fatia, portanto, é o nosso primeiro símbolo, agora dividimos essa subfaixa novamente, fazendo o mesmo processo anterior e perceberemos que **0,36** se encaixa no símbolo **a** e depois de repetirmos o processo, chegamos ao último símbolo **t** (formando nosso fluxo codificado original *eat*).
Tanto o codificador quanto o decodificador **devem conhecer** a tabela de probabilidade de símbolos, portanto, você precisa enviar a tabela.
Bastante legal, não é? As pessoas são muito inteligentes para criar uma solução dessas, alguns [codecs de vídeo usam](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding) essa técnica (ou pelo menos a oferecem como uma opção).
A ideia é comprimir sem perda o fluxo de bits quantizado, com certeza este artigo está perdendo toneladas de detalhes, razões, compensações e etc. Mas [você deve aprender mais](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/) como desenvolvedor. Novos codecs estão tentando usar diferentes [algoritmos de codificação de entropia como ANS.](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)
> ### Prática: CABAC vs CAVLC
> Você pode [gerar dois fluxos, um com CABAC e outro com CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc) e **comparar o tempo** que cada um leva para ser gerado, bem como **o tamanho final**.
## Sexto passo - formato bitstream
Depois de termos concluído todas essas etapas, precisamos **empacotar os quadros comprimidos e o contexto dessas etapas**. Precisamos informar explicitamente ao decodificador sobre **as decisões tomadas pelo codificador**, como profundidade de bits, espaço de cores, resolução, informações de previsões (vetores de movimento, direção de previsão intra), perfil, nível, taxa de quadros, tipo de quadro, número de quadros e muito mais.
Vamos estudar superficialmente o bitstream H.264. Nosso primeiro passo é [gerar um bitstream H.264 mínimo *](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream), podemos fazer isso usando nosso próprio repositório e [ffmpeg](http://ffmpeg.org/).
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * Por padrão, o ffmpeg adiciona todos os parâmetros de codificação como um **NAL SEI**, em breve definiremos o que é um NAL.
Este comando irá gerar um bitstream H.264 bruto com um **único quadro**, 64x64, com espaço de cor yuv420 e usando a seguinte imagem como quadro.
> 
### H.264 bitstream
O padrão AVC (H.264) define que as informações serão enviadas em **macro quadros** (no sentido de rede), chamados de **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (Camada de Abstração de Rede). O principal objetivo do NAL é fornecer uma representação de vídeo "amigável à rede", este padrão deve funcionar em TVs (baseadas em fluxo), na Internet (baseadas em pacotes) e em outros.
Existe um **[marcador de sincronização](https://en.wikipedia.org/wiki/Frame_synchronization)** para definir os limites das unidades NAL. Cada marcador de sincronização contém o valor `0x00 0x00 0x01`, exceto o primeiro, que é `0x00 0x00 0x00 0x01`. Se executarmos o **hexdump** no bitstream h264 gerado, podemos identificar pelo menos três NALs no início do arquivo.
Como já dissemos antes, o decodificador precisa saber não apenas os dados da imagem, mas também os detalhes do vídeo, quadro, cores, parâmetros usados e outros. O **primeiro byte** de cada NAL define sua categoria e **tipo**.
| id tipo NAL | Descrição |
|--- |---|
| 0 | Indefinido |
| 1 | Fatia codificada de uma imagem não-IDR |
| 2 | Dados de fatia codificada, partição A |
| 3 | Dados de fatia codificada, partição B |
| 4 | Dados de fatia codificada, partição C |
| 5 | **IDR** Fatia codificada de uma imagem IDR |
| 6 | **SEI** Informação adicional de melhoria |
| 7 | **SPS** Conjunto de parâmetros de sequência |
| 8 | **PPS** Conjunto de parâmetros de imagem |
| 9 | Delimitador de unidade de acesso |
| 10 | Fim da sequência |
| 11 | Fim do fluxo |
| ... | ... |
Geralmente, o primeiro NAL de um fluxo de bits é um **SPS**, esse tipo de NAL é responsável por informar as variáveis gerais de codificação como **perfil**, **nível**, **resolução** e outras.
Se pulamos o primeiro marcador de sincronização, podemos decodificar o **primeiro byte** para saber qual **tipo de NAL** é o primeiro.
Por exemplo, o primeiro byte após o marcador de sincronização é `01100111`, onde o primeiro bit (`0`) é para o campo **forbidden_zero_bit**, os próximos 2 bits (`11`) nos dizem o campo `nal_ref_idc` que indica se este NAL é um campo de referência ou não e os próximos 5 bits (`00111`) nos informam o campo **nal_unit_type**, neste caso, é um NAL de **SPS** (7).
O segundo byte (`binário=01100100, hex=0x64, dec=100`) de um SPS NAL é o campo **profile_idc** que mostra o perfil que o codificador usou, neste caso, usamos o **[perfil alto](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)**. O terceiro byte também contém várias flags que determinam o perfil exato (como restrito ou progressivo). Mas, no nosso caso, o terceiro byte é 0x00 e, portanto, o codificador usou apenas o perfil alto.
Quando lemos a especificação do bitstream H.264 para um SPS NAL, encontramos muitos valores para o **nome do parâmetro**, **categoria** e uma **descrição**, por exemplo, vamos olhar para os campos `pic_width_in_mbs_minus_1` e `pic_height_in_map_units_minus_1`.
| Nome do parâmetro | Categoria | Descrição w |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: Inteiro sem sinal (unsigned integer) [Exp-Golomb-coded](https://ghostarchive.org/archive/JBwdI)
Se fizermos algumas contas com o valor desses campos, acabaremos com a **resolução*8. Podemos representar um `1920 x 1080` usando um `pic_width_in_mbs_minus_1` com o valor de `119 ( (119 + 1) * macroblock_size = 120 * 16 = 1920)`, economizando espaço, em vez de codificar `1920`, fizemos isso com `119`.
Se continuarmos a examinar nosso vídeo criado com uma visualização binária (ex: `xxd -b -c 11 v/minimal_yuv420.h264`), podemos pular para o último NAL que é o quadro em si.
Podemos ver seus primeiros 6 bytes: `01100101 10001000 10000100 00000000 00100001 11111111`. Como já sabemos, o primeiro byte nos informa sobre o tipo de NAL que é, neste caso, (`00101`) é um **IDR Slice (5)** e podemos inspecioná-lo ainda mais:
Usando as informações da especificação, podemos decodificar qual tipo de slice (**slice_type**), o número do quadro (**frame_num**) entre outros campos importantes.
Para obter os valores de alguns campos (`ue(v), me(v), se(v) ou te(v)`), precisamos decodificá-los usando um decodificador especial chamado [Exponential-Golomb](https://ghostarchive.org/archive/JBwdI), este método é **muito eficiente para codificar valores variáveis**, principalmente quando há muitos valores padrão.
> Os valores de **slice_type** e **frame_num** deste vídeo são 7 (I slice) e 0 (o primeiro quadro).
Podemos ver o **bitstream como um protocolo**, e se você quiser ou precisar aprender mais sobre esse bitstream, consulte a [especificação ITU H.264](http://www.itu.int/rec/T-REC-H.264-201610-I). Aqui está um diagrama macro que mostra onde os dados da imagem (YUV comprimido) residem.
Podemos explorar outros bitstreams como o [VP9 bitstream](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf) ou até mesmo nosso **novo melhor amigo** [**AV1** bitstream](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
), [eles parecem semelhantes? Não](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/), mas uma vez que você aprende um, pode facilmente aprender os outros.
> ### Prática: Inspecionar o H.264 bitstream
> Podemos [gerar um vídeo de um único quadro](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) e usar o [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) para inspecionar seu bitstream H.264. Na verdade, você pode até ver o [código fonte que analisa o bitstream h264 (AVC)](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp).
>
> 
>
> Também podemos usar o [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer), que é pago, mas há uma versão de teste gratuita que limita o uso apenas aos primeiros 10 quadros, mas isso é ok para fins de aprendizado.
>
> 
## Revisão
Vamos notar que muitos dos **codecs modernos usam o mesmo modelo que aprendemos**. Na verdade, vamos olhar o diagrama de blocos do codec de vídeo Thor, ele contém todos os passos que estudamos. A ideia é que agora você deve ser capaz de entender melhor as inovações e os artigos para a área.
Anteriormente, calculamos que precisávamos de [139 GB de armazenamento para manter um arquivo de vídeo com uma hora de duração em resolução 720p e 30fps](#chroma-subsampling) se usarmos as técnicas que aprendemos aqui, como **previsão inter e intra, transformação, quantização, codificação de entropia e outras** podemos alcançar, assumindo que estamos gastando **0,031 bit por pixel**, o mesmo vídeo de qualidade perceptível **requer apenas 367,82 MB em vez de 139 GB** de armazenamento.
> Escolhemos usar **0,031 bit por pixel** com base no exemplo de vídeo fornecido aqui.
## Como H.265 consegue uma melhor razão de compressão comparado com H.264?
Agora que sabemos mais sobre como os codecs funcionam, fica fácil entender como os novos codecs são capazes de entregar resoluções mais altas com menos bits.
Vamos comparar AVC e HEVC, lembrando que quase sempre é um equilíbrio entre mais ciclos de CPU (complexidade) e taxa de compressão.
HEVC tem opções de **partições** (e **sub-partições**) maiores e mais numerosas do que AVC, mais **direções/ângulos de predições intra**, codificação de entropia melhorada e muito mais, todas essas melhorias tornaram o H.265 capaz de comprimir 50% mais do que o H.264.
# Transmissão online
## Arquitetura geral
[TODO]
## Download progressivo e transmissão adaptativa
[TODO]
## Proteção de conteúdo
Podemos utilizar **um sistema simples de token** para proteger o conteúdo. O usuário sem um token tenta solicitar um vídeo e o CDN o impede, enquanto um usuário com um token válido pode reproduzir o conteúdo. Isso funciona de maneira bastante semelhante à maioria dos sistemas de autenticação da web.
O uso exclusivo deste sistema de tokens ainda permite que um usuário baixe um vídeo e o distribua. Então, os sistemas de **gerenciamento de direitos digitais (DRM)** podem ser usados para tentar evitar isso.
Na vida real, em sistemas de produção, as pessoas frequentemente usam ambas as técnicas para fornecer autorização e autenticação.
### DRM
#### Principais sistemas
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### O que?
DRM significa [gerenciamento de direitos digitais](https://sander.saares.eu/categories/drm-is-not-a-black-box/), é uma **forma de fornecer proteção de direitos autorais para mídia digital**, como vídeo e áudio digital. Embora seja usado em muitos lugares, [não é universalmente aceito](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### Por quê?
Os criadores de conteúdo (principalmente estúdios) querem proteger sua propriedade intelectual contra cópias para evitar redistribuição não autorizada de mídia digital.
#### Como?
Vamos descrever de forma abstrata e genérica uma forma simplificada de DRM.
Dado um **conteúdo C1** (ou seja, um streaming de vídeo hls ou dash), com um **reprodutor P1** (como shaka-clappr, exo-player ou ios) em um **dispositivo D1** (como um smartphone, TV, tablet ou desktop/notebook) usando um **sistema DRM1** (widevine, playready ou FairPlay).
O conteúdo C1 é criptografado com uma **chave simétrica K1** do sistema DRM1, gerando o **conteúdo criptografado C'1**.
O reprodutor P1, de um dispositivo D1, tem duas chaves (assimétricas), uma **chave privada PRK1** (essa chave é protegida1 e conhecida apenas por D1) e uma **chave pública PUK1**.
> **1protegida**: essa proteção pode ser **via hardware**, por exemplo, essa chave pode ser armazenada dentro de um chip especial (somente leitura) que funciona como uma [caixa preta](https://en.wikipedia.org/wiki/Black_box) para fornecer a decodificação, ou **por software** (menos seguro), o sistema DRM fornece meios para saber qual tipo de proteção um determinado dispositivo possui.
Quando o **reprodutor P1 deseja reproduzir** o **conteúdo C'1**, ele precisa lidar com o **sistema DRM1**, fornecendo sua chave pública **PUK1**. O sistema DRM1 retorna a **chave K1 criptografada** com a chave pública do cliente **PUK1**. Em teoria, essa resposta é algo que **apenas D1 é capaz de decifrar**.
`K1P1D1 = enc(K1, PUK1)`
**P1** usa seu sistema local de DRM pode ser um [SOC](https://en.wikipedia.org/wiki/System_on_a_chip), um hardware ou software especializado, este sistema é capaz de decifrar o conteúdo usando sua chave privada PRK1, ele pode decifrar **a chave simétrica K1 do K1P1D1** e **reproduzir C'1**. No melhor dos casos, as chaves não são expostas através da RAM.
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# Como user o Jupyter
Certifique-se de ter o **docker instalado** e execute `./s/start_jupyter.sh` e siga as instruções no terminal.
# Conferências
* [DEMUXED](https://demuxed.com/) - você pode [ver as apresentações dos últimos 2 eventos.](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# Referências
O conteúdo mais rico está aqui, é onde todas as informações que vimos neste texto foram extraídas, baseadas ou inspiradas. Você pode aprofundar seu conhecimento com esses incríveis links, livros, vídeos, etc.
Cursos Onlines e Tutoriais:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* https://wiki.multimedia.cx
* https://mahanstreamer.net
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Livros:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/High-Efficiency-Video-Coding-HEVC/dp/3319068946
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Em material de integração:
* https://github.com/Eyevinn/streaming-onboarding
* https://howvideo.works/
* https://www.aws.training/Details/eLearning?id=17775
* https://www.aws.training/Details/eLearning?id=17887
* https://www.aws.training/Details/Video?id=24750
Especificações de fluxo de bits:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
Programas:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://mediaarea.net/en/MediaInfo
* https://www.jongbel.com/
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
Codecs não-ITU:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://ghostarchive.org/archive/0W0d8 (era: https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml)* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
* https://jmvalin.ca/papers/AV1_tools.pdf
Conceitos de Codificação:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
* https://videoblerg.wordpress.com/2017/11/10/ffmpeg-and-how-to-use-it-wrong/
Sequências de vídeo para testes:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
Diversos:
* https://github.com/Eyevinn/streaming-onboarding
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* https://web.archive.org/web/20100728070421/http://www.csc.villanova.edu/~rschumey/csc4800/dct.html (era: http://www.csc.villanova.edu/~rschumey/csc4800/dct.html)
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
* https://sander.saares.eu/categories/drm-is-not-a-black-box/
================================================
FILE: README-ru.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
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# Интро
Нежное интро к видео технологии, предназначено для программистов и инженеров, хотя информация тут изучаема **любому** заинтересованному. Эта идея родилась во время [мини-семинара для новичков в области видео технологий](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p).
Цель представить концепции цифрового видео **простыми терминами, схемами и практикой**. Не стесняйтесь добавлять свои исправления и предложения для улучшения документа.
**Практические разделы** требуют чтобы у вас был установлен **docker** и клонирован этот репо.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **ПРЕДУПРЕЖДЕНИЕ**: когда вы видите команду `./s/ffmpeg` или `./s/mediainfo`, это означает, что мы запускаем **контейнерную версию** этой программы, которая уже включает в себя все необходимые требования.
Все **практические упражнения выполняются из клонированной папки**. Для **примеров jupyter** вы должны запустить сервер `./s/start_jupyter.sh`, и посетить URL в браузере.
# Changelog
* added DRM system
* released version 1.0.0
* added simplified Chinese translation
# Индекс
- [Интро](#интро)
- [Индекс](#индекс)
- [Основные термины](#основные-термины)
* [Другие способы кодирования цветного изображения](#другие-способы-кодирование-изображений)
* [Практика: экспериментируем цветами и изображениями](#практика-експерементируем-цветами-и-изображениями)
* [DVD это DAR 4:3](#dvd-это-dar-43)
* [Практика: Рассмотр свойсвтва видео](#практика-рассмотр-свойсвтва-видео)
- [Удаление избыточности](#удаление-избыточности)
* [Цвета, яркость и глаза](#цвета-яркость-и-глаза)
+ [Модель цвета](#модель-цвета)
+ [Преобразование между YCbCr и RGB](#преобразование-между-ycbcr-и-rgb)
+ [Цветовая дискретизация](#цветовая-дискретизация)
+ [Практика: Проверка гистограммы YCbCr](#практика-проверка-гистограммы-ycbcr)
* [Типы кадров](#типы-кадров)
+ [I кадр (интра, ключевой кадр)](#i-кадр-интра-ключевой-кадр)
+ [P кадр (предсказанный)](#p-кадр-предсказанный)
- [Практика: Видео с единым I-кадром](#практика-видео-с-единым-i-кадром)
+ [B кадр (двунаправленного предсказания)](#b-кадр-двунаправленного-предсказания)
- [Практика: Сравнивание видео с Б-кадрами](#практика-сравнивание-видео-с-б-кадрами)
+ [Конспект](#конспект)
* [Временная избыточность (меж предсказание)](#временная-избыточность-меж-предсказание)
- [Практика: Обзор векторов движения](#практика-обзор-векторов-движения)
* [Пространственная избыточность (внутреннее предсказание)](#пространственная-избыточность-внутреннее-предсказание)
- [Практика: проверка внутренних предсказаний](#практика-проверка-внутренних-предсказаний)
- [Как действует видео кодек?](#как-действует-видео-кодек)
* [Что? Почему? Как?](#что-почему-как)
* [История](#история)
+ [Рождение AV1](#рождение-av1)
* [Общий кодек](#общий-кодек)
* [1-й шаг - разделение изображения](#1-й-шаг---разделение-изображения)
+ [Практика: Проверка разделов](#практика-проверка-разделов)
* [2-й шаг - предсказания](#2-й-шаг---предсказания)
* [3-й шаг - трансформация](#3-й-шаг---трансформация)
+ [Практика: выбрасывание разных коэффициентов](#практика-выбрасывание-разных-коэффициентов)
* [4-й шаг - квантование](#4-й-шаг---квантование)
+ [Практика: квантование](#практика-квантование)
* [5-й шаг - энтропийное кодирование](#5-й-шаг---энтропийное-кодирование)
+ [VLC-кодирование](#vlc-кодирование)
+ [Арифметическое кодирование](#арифметическое-кодирование)
+ [Практика: CABAC vs CAVLC](#практика-cabac-vs-cavlc)
* [6-й шаг - формат битового потока](#6-й-шаг---формат-битового-потока)
+ [H.264 битовый поток](#h264-битовый-поток)
+ [Практика: Проверка битовога потока H.264](#практика-проверка-битовога-потока-h264)
* [Обзор](#oбзор)
* [Как H.265 обеспечивает лучшую степень сжатия чем H.264?](#как-h265-обеспечивает-лучшую-степень-сжатия-чем-h264)
- [Онлайн трансляция](#онлайн-трансляция)
* [Общая архитектура](#общая-архитектура)
* [Прогрессивная загрузка и адаптивная передача](#прогрессивная-загрузка-и-адаптивная-передача)
* [Защита контента](#защита-контента)
- [Как использовать jupyter](#как-использовать-jupyter)
- [Конференции](#конференции)
- [Ссылки](#ссылки)
# Основные термины
**Изображение** можно представлять в форме **2Д матрицы**. Если мы думаем о **цветах**, то можно экстраполировать это представление в **3Д матрицу**, где **добавочные измерения** показывают **цветную информацию**.
Если мы представляем эти цвета [первичными цветами (красный, зеленый, синий)](https://en.wikipedia.org/wiki/Primary_color), мы определяем три уровня, один для **красного**, один для **зеленого**, и последний для **синего**.
Мы назовем каждую точку на этой матрицы **пикселем** (элемент изображения). Один пиксел отражает **интенсивность** (обычно числовое значение) данного цвета. Например, **красный пиксель** имеет 0 зеленого, 0 синего, и максимально красного. **Розовый пиксель** состоит из комбинации трех цветов - **Красный=255, Зеленый=192, Синиий=203**.
> #### Другие способы кодирование изображений
> Существует множество моделий которые описовают как расппределяються цвета на изображение. Например, в место трех битов, как для модели RGB (Красный, Зеленый, Синий) мы можем изпользовать только один бит. Ето использует меньше памяати но так же дает меньшые количество цветов.
>
> 
На изображение снизу, первый снимок отражает все цветовые каналы. Остальные снимки только отражают красный, зеленый и синий канал.
Мы видим что **красный канал** будет самым ярким (белые части второго лица), и способствует главному оттенку в картинке. **Синего** мало, и видно в четвертой картинке, что он **сконцентрирован в глазах и одежды** Марио. Так же видно, что все каналы мало способствуют усам, самым темным частям картинки.
У каждого канала определённое количество битов, так называема **битовая глубина**. Если у нас **8 битов** (между 0 и 255и) для каждого цвета, то битовая глубина у изображений RGB (8 бит * 3 цвета) в размере **24 бита**, то есть 2^24 разных цвета.
> **Хорошо знать** [как озпбражения переводятся с настоящего мира в биты](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm).
Другая характеристика изображений это **разрешение**, то есть количество пикселей в одном измерение. Обычно оно показывается в формате ширина х высота, как на **4х4** картинке снизу.
> #### Практика: експерементируем цветами и изображениями
> Можно [играца с цветами в картинках](/image_as_3d_array.ipynb) используя [jupyter](#how-to-use-jupyter) (python, numpy, matplotlib и т.д).
>
> Так же можшно понять [как фильтры, (обострение, размытость...) работают](/filters_are_easy.ipynb).
Еще качество с которым мы встречаемся пре работе с видео и изображениями это **соотношение сторон (AR)** которое просто описывает пропорции ширины сравнительно с высотой изображения или пикселя.
Когда луди говорят что изображение или фильм **16х9**, они называют цифры **DAR (Соотношение сторон дисплея)**. Так же и бывает **PAR (Соотношение сторон пикселя)**
> #### DVD это DAR 4:3
>
> Хоть у DVD разрешение 704х480, формат все равно соблюдает DAR 4:3 потому что у него PAR 10:11 (703x10/480x11)
И конечно, "видео" мы определяем как **последствие *n*-кадров** во **времени**, что можно обозначить как дополнительное измерение поверху размера и цвета, где *п* это количество кадров в секунду (FPS).
**битрейт** это сколько нужно битов что бы показать секунду видео.
> битрейт = ширина * высота * битовая глубина * кадры в секунду
Например, видео которое 30 FPS, 24 бита на пиксел, с разрешением 480х240 использует **82,944,000 битов в секунду** или 82 мегабита/с (30*480х240х24) без компрессии/снижения качества.
Когда **бит рейт** постоянный то его называют постоянным битрейтом (**CBR**), когда он меняется со временем тогда это переменный битрейт (**VBR**).
> Этот график показывает ограниченный VBR, который не тратит слишком много битов на черные кадры.
>
> 
Раньше, инженеры придумали технику, что бы увеличить воспринятый FPS в два раза при этом **не увеличения битрейт**. Эта техника называется **чересстрочное видео**, где пол изображения посылается в одном "кадре" и другая половина в следующем "кадре".
Сегодня экраны обычно представляют видео техникой **прогрессивного скана**. Это способ показывания, хранения и перевода изображений в котором все линии каждого кадра нарисованы одна за другой.
Теперь у нас есть представление того как xраница **изображение** в цифровом формате, как его **цвета** расположены, сколько **битов в секунду** мы используем что бы показывать видео, если это постоянный битрейт (CBR) или переменный битрейт (VBR), и как это связано с **разрешением** при данной **чистоте кадра**. Так же мы более знакомы с терминами наподобие **чересстрочное видео**, PAR, и т.д.
> #### Практика: Рассмотр свойсвтва видео
> Вам возможно [увидеть оговоринные свойства с помощью ffmpeg или mediainfo.](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)
# Удаление избыточности
Быстро становиться очевидно что работать с видео без компрессии практически не возможно; **один час видео** разрешение 720p, с частотой кадра 30fps занимает **278GB памяти***. Алгоритмы компрессии без потерь, наподобие DEFLATE (используется в PKZIP, Gzip, PNG) **не сжимают видео достаточно** нам надо искать другие способы сжатья видео.
> * Эту цифру мы получаем умнажая 1280 х 720 х 24 х 30 х 3600 (ширина, высота, витовая глубина, чистота кадра и время в секундах).
Для этого мы можем использовать **характеристики нашего зрения**. Мы разбираем яркость лучше, чем цвета. Так же более заметные **повторы частей изображения во времени**.
## Цвета, яркость и глаза
Наши глаза более чувствительны к [яркости чем к цветам](http://vanseodesign.com/web-design/color-luminance/), проверти для себя на этом изображение:
Если вам не видно, что цвета **квадрата А и Б одинаковые** у картинки с лева, то у вас все в порядке, наш мозг настроен **уделять больше внимания на яркость/темноту чем на цвет**. С право вы видите что цвета и в правду одинаковы.
> **Упрощенное обьяснение функций глаз**
> Глаз [сложный орган](http://www.biologymad.com/nervoussystem/eyenotes.htm), составлин из многих частей, хотя нас интересует шишочные и палочные клетки. Глаз состоит из порядка [120 милион палочных клеток и 6 милион шишочныч клеток](https://en.wikipedia.org/wiki/Photoreceptor_cell).
>
> **Упращая дальше**, давайте посмотрим как цвет и яркость действуют на глаза. **[Палочные клетки](https://en.wikipedia.org/wiki/Rod_cell) по большенству отвецтвенны за восприятие яркости**. **[Шишичные клетки](https://en.wikipedia.org/wiki/Cone_cell) отвецтвенны за восприятие цвета**. Есты три типа шишки, каждая со своей окраской: [S-шишки (Синий), M-шишки (Зеленый) и L-cones (Красный)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> Потому что у нас больше палочных клеток (яркость) чем шишочных (цвет), мы делаем вывод что мы обробатываем больше яркостной информации.
>
> 
>
> **Функции контрастной чувствительности**
>
> Есть много теорий описывая функции человеческое зрение. Одна из них называется "Функции контрастной чувствительности". Они описывают сколько изминения в цвете может произойти перед тем как наблюдающий его замечает. Функции измеряют не только чуствительность к черно белому, нo так же и яркость при цветах. Експерименты раз за разом показывают что мы более чуствительны к яркости чем к цветам.
Зная что мы чувствительны к яркости, мы можем попытаться использовать этот факт.
### Модель цвета
В начале мы изучали как [распределять цвет на изображениях](#основные-термины) используя **модель RGB**. Существуют другие модели - например, **YCbCr*** делит luma/лума (яркость) от chrominance/хроминанц (цветность).
> * есть другие модели которые так же разделяют между яркостью и цветом.
В этой модели **Y** канал яркости. Так же есть два канала цвета, **Cb** (синяя chroma/хрома) и **Cr** (красная chroma/хрома). Формат [YCbCr](https://en.wikipedia.org/wiki/YCbCr) возможно получить от RGB, и так же можно получить RGB от YCbCr. С помощью YCbCr мы можем составлять изображения в полном цвете:
### Преобразование между YCbCr и RGB
Некоторые из вас может спрашивать, как можно показывать **все цвета без зеленого**?
Что бы понять ответ, давайте посмотрим как мы переводим цветовую модель RGB на YCbCr. Мы будем использовать коэффициенты из стандарта **[BT.601](https://en.wikipedia.org/wiki/Rec._601)**, который был создан **[группой ITU-R*](https://en.wikipedia.org/wiki/ITU-R)**. Первый шаг в этом процессе это **высчитать яркость (luma)** и заменить цифры RGB, используя константы рекомендованные группой ITU.
```
Y = 0.299R + 0.587G + 0.114B
```
После яркости, мы можем **получить цвета** (chroma синий и красный):
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
Можно **перевести этот результат обратно**, и даже **получить зеленый используя YCbCr**
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * Цыфровым видео правят группы и стандарты. Группы определяют стандарты, на пример [что такое 4К? Какую чистоту кадра использоватъ? Решение? Цветную модель?](https://en.wikipedia.org/wiki/Rec._2020).
Обычно, **дисплеи** работают только в режиме модели **RGB**, расположенные каким то образом. Некоторые из них показаны тут:
### Цветовая дискретизация
Когда изображение представлено компонентами яркости и цвета, мы можем воспользоваться чувствительностью глаза к яркости, что бы уменьшить количество информации. **Цветовая дискретизация** техника кодированья изображений с **разрешением для цвета меньше чем для яркости**.
Так как же уменьшается разрешение цвета?! Существуют схемы которые описывают, как получить цвет от разных разрешений для одного изображение (`конечный цвет = Y + Cb + Cr`).
Эти схемы называются системами дискретизации, обычно показаны соотношением трех чисел - `a:x:y` которое определяет разрешение цвета по отношению к `a * 2` блоку пикселей яркости.
* `a` горизонтальная ширина (обычно 4)
* `x` количество образцов цвета в первом ряду
* `y` количество изменений цвета между первым и вторым рядом
> В этой схеме есть изклучение, 4:1:0, где выберается только один цвет для каждего `4 x 4` блока разрешения яркости.
В современных кодеках обычно используются форматы: **4:4:4** *(Без дискретизации)*, **4:2:2, 4:1:1, 4:2:0, 4:1:0 и 3:1:1**.
> Вы можете участовать в [разговорах о цветовой подборке тут](https://github.com/leandromoreira/digital_video_introduction/issues?q=YCbCr).
> **Подборка YCbCr 4:2:0**
>
> Тут видно как сливается цветовая информация с яркостной в режшиме YCbCr 4:2:0. Заметите, что мы только используем 12 битов на пиксель.
>
> 
Тут показано изображение кодированное главными типами дискретизации. Первый ряд финальный YCbCr; на втором, видно разрешение цветных каналов. Качество изображения нормальное, при этом памяти используется на много меньше.
Ранее мы вычислили что нам надо [278GB памяти что бы сохранить час 720p/30fps видео](#удаление-избыточности). Используя режим **YCbCr 4:2:0** мы можем **убрать половину размера до 139GB***, хотя это все равно далеко от идеала.
> * это число можно получить умнажая ширину, высоту, биты на пиксел, и чистоту кадра. Раньше мы пользовались 24 битами, теперь нам только надо 12.
> ### Практика: Проверка гистограммы YCbCr
> Возможно проверить [гистограмму YCbCr с помошью ffmpeg.](/encoding_pratical_examples.md#generates-yuv-histogram). У этого изображение выше концентрация синего, что видно на [гистограмме](https://en.wikipedia.org/wiki/Histogram).
>
> 
### Цвет, лума, яркость, гамма
Отличное видео для тех кто хотят приобрести глубже понятие лумы, яркости, гаммы, и цвета.
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
## Типы кадров
Теперь мы можем сосредоточиться на удаление **избыточности во времени**. С начала, нам надо установить основные термины - скажем, что мы работаем с фильмом 30FPS, и первые 4 кадра выглядят так:
  
Мы видим много повторений между кадрами, например у синего фона который не меняется на кадрах 0 - 3. Что бы начать решать эту проблему, мы можем абстрактно классифицировать их как 3 разных типа кадра.
### I кадр (интра, ключевой кадр)
I кадр, (ссылка, ключевой кадр, внутренний) **независимый кадр**. Что бы его нарисовать не нужно дополнительной информации, и он похож на фотографию. Первый кадр обычно I кадр, но мы видем как I кадры регулярно вставляют среди других типов кадра.
### P кадр (предсказанный)
P-кадр пользуется фактом, что почти всегда можно нарисовать изображение **с помощью пре ведущего кадра**. Например, во втором кадре, шар поменял расположение - все остальное осталось одинаковым. Мы можем построить **первый кадр используя только разницу между двумя кадрами**.
 <- 
> #### Практика: Видео с единым I-кадром
> Если P-кадры используют меньше памяти, то почему бы нам не закодировать целое [видео одним I-кадром и оставить все последующие кадры как P-кадры?](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)
>
> После кодировки этого видео, начните смотреть его и попробуйте перескочить к пожемму времини. Станит заметно что эта функция длица **долгое време**. **P-кадрам нужны ссылочные кадры** (например I-кадр) что бы их нарисовать.
>
> Так же можно закодировать видео с одним I-кадром а потом попробовать [закодировать его вставляя I-кадр каждие две секунды](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second), сравнивая размера в конце.
### B кадр (двунаправленного предсказания)
Так же можно ссы лаца на будущие кадры, как и пре ведущие, метод который экономит еще больше памяти - как раз это и делают B-кадры.
 <-  -> 
> #### Практика: Сравнивание видео с Б-кадрами
> Вы можете составить две версии видео, одну с B-кадрами и другую [вообще без B-кадров](/encoding_pratical_examples.md#no-b-frames-at-all). Обратите внимание на размер файла и качество видео.
### Конспект
Эти кадры используются для **более эффективной компрессии**. Мы уведем как это происходит в следующем отделе, на не теперешний момент нам достаточно помнить что **I-кадр дорогой, P-кадр дешевле и B-кадр самый дешевый**.
## Временная избыточность (меж предсказание)
Давайте посмотрим как мы и можем понизить количество **повторов во времени**. Такого типа избыточности можно достичь техниками **меж предсказания**.
По попробуем **потратить меньше битов** кодируя кадры 0 и 1.
Один подход это просто **вычитать разницу между кадром 1 и 0**, и мы получим все что надо что бы **закодировать остаток**.
Не плохо, но есть еще **лучше метод**, который использует еще меньше битов. С начала, давай разрежем `кадр 0` на серию разделов, после чего мы можем попытаться соответствовать разделы с `кадра 0` до `кадра 1`, действие наподобие **оценки двежения**.
> ### Википедиа - компенсация движению блока
> **Компенсаци движению блока** делит текущий кадр на отдлеьные разделы, после чего векторы компенсации движения **показывают от куда эти блоки появились** (распостраненная ошыбка идея того что, разделив преведущий кадр на отдельные разделы, векторы показывают куда эти разделы двигаются). Исходные разделы обычно перекрывают друг друга в исходном кадре. Некотороя компрессия видео текущий кадр из кусков разных ранее переданных кадров.
Мы можем предсказать что шарик сдвинулся от `x=0, y=25` до `x=6, y=26`, и что **х** и **y** числа это **векторы движения**. Что бы сэкономить на битах дальше, мы можем **закодировать только разницу в векторах движения** между последним положением блока и предсказанным, так что финальный вектор движения получается `x=6 (6-0), y=1 (26-25)`.
> При настоящем использование этой техники, шар был бы разделен на `n` разделов. Процесс при этом остается одинаковым.
> Обьекты на кадре **могут двигатся 3Dобразно**, шар может стать меньше когда он уходит в фон. Впольне возможно (и нормально) что мы не сможем найти идеальнoe совпадение для раздела которму мы ищим совпадение. Вот изображение сравняя наше предскозание с настоящим кадром.
Все равно, видно что когда мы используем **предсказание движения** информации которую нам **надо кодировать меньше** чем если мы бы использовали технику делта кадров.
> ### Как выглядет настоящая компенсация движения
> Обычно эта техника используется для всех разделов, и обычно один шар как в преведущих кадрах будет разделен на несколько разделов.
> 
> Ссылка: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
Вы можете [экспериментировать этими техниками в jupyter](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Практика: Обзор векторов движения
> Мы можем [создать видео с внешним предсказанием (векторами движения) с помощью ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> Так же можно использовать [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (програма платная, но есть бесплатная, пробная версия, которая ограничивает пользу на первые 10 кадров).
>
> 
## Пространственная избыточность (внутреннее предсказание)
Если мы проанализируем **каждый кадр** в видео, мы увидим что есть **много областей, которые имеют отношение друг к другу**.
Давайте пройдемся по примеру. Эта сцена в основном состоит из синего и белого.
Это `I-кадр`, и мы **не можем использовать предыдущие кадры** для прогнозирования - при этом, мы все еще можем сжать его. Мы закодируем информацию в красном разделе. Если мы **посмотрим на его соседей**, мы можем понять что существует **тренд цветов вокруг него**.
Мы **предскажем**, что в кадре будут **распространятся цвета по вертикали**, то есть цвета пикселей о которых мы не знаем в этой области **будут содержать цвета соседей**.
Наше **предсказание может быть неверным**, по этому нам надо применить этот метод (**внутреннее предсказание**), а затем **вычесть реальные числа цветов**, что дает нам остаточный раздел и дает нам более сжимаемою матрицу по сравнению с оригиналом.
> #### Практика: проверка внутренних предсказаний
> Вы можете [зделать видео с макро разделами и их предскозаниями в ffmpeg.](/encoding_pratical_examples.md#generate-debug-video) Пожалуйста проверите документацию ffmpeg что бы понять [значение каждего блока цвета](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes).
>
> 
>
> Так же можно использовать [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (програма платная, но есть бесплатная, пробная версия, которая ограничивает пользу на первые 10 кадров).
>
> 
# Как действует видео кодек?
## Что? Почему? Как?
**Что такое видео кодек?** Это система, программная или физическая, которая сжимает или разжимает цифровое видео. **За чем?** Обществу, и в свою очередь рынку нужно видео высокого качества при ограниченном объёме передачи или хранения информации. Помните когда мы [высчитали нужный объем передачи](#основные-термины) для видео у которого 30 кадров в секунду, с битовой глубинной 24 бита и разрешение 480х240? Это было **82.944mbps** без компрессии. Только так можно передавать HD/FullHD/4K видео к телевизору или по интернету. **Как?** Мы проведём поверхностный обзор главный техники здесь.
> **Кодек или контейнер?**
>
> Очень часто путается цыфровой видео кодек и [цыфровой видео контейнер](https://en.wikipedia.org/wiki/Digital_container_format). **Контейнер** можно представлять как оберток который содержит метаданные о видео (и возможно о звуке). **Сжатое видео** можно представить как его дoставочное содержaние.
>
> Обычно, расширение файла указывает тип контейнера. Например, файл `video.mp4` скорее всяго контейнер **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14)**, а файл с названием `video.mkv` скорее всяго контейнер **[matroska](https://en.wikipedia.org/wiki/Matroska)**. Что бы быть уверенным, можно использовать [ffmpeg или mediainfo](/encoding_pratical_examples.md#inspect-stream).
## История
Прежде чем мы углубимся во механизмы универсального кодека, давайте посмотрим в прошлое, чтобы немного лучше понять некоторые старые видеокодеки.
Видео кодек [H.261](https://en.wikipedia.org/wiki/H.261) официально родился в 1990, и был устроен работать при **обмене информации скоростью 64 kbit/s**. Уже используют методы на подобие цветовой десктирезации, макро блоки е т.д. В 1995 году, выпустили кодек **H.263** - этот стандарт прожил до 2001ого, время на протяжение которого над ним постоянно работали и улучшали.
В 2003 году была завершена первая версия **H.264 / AVC**. В том же году компания под названием **TrueMotion** выпустила свой видеокодек **без лицензионных отчислений** для сжатия видео с потерями под названием **VP3**. В 2008 году **Google купил** эту компанию, выпустив **VP8** в том же году. В декабре 2012 года Google выпустил **VP9**, и он **поддерживается примерно на ¾ из всех браузеров** (включая мобильные устройства).
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)** - это новый **бесплатный кодек**, с открытым кодом, который разрабатывается [Альянсом открытых медиа (AOMedia)](http://aomedia.org/), в которую входят **компании: Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel и Cisco** и другие. **Первая версия** 0.1.0 эталонного кодека **была опубликована 7 апреля 2016 года**.
> #### Рождение AV1
>
> В начале 2015 года Google работали над [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1), Xiph (Mozilla) работали над [Daala](https://xiph.org/daala/) и Cisco открыли свой бесплатный видеокодек под названием [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03).
>
> Затем MPEG LA объявили годовые ограничения для HEVC (H.265) и сtoймость использованья в 8 раз выше, чем H.264. Вскоре они снова изменили правила:
> * **нет годового лимита**,
> * **плата за контент**, (0,5% от выручки) и
> * **Плата за единицу продукции примерно в 10 раз выше, чем h264**.
>
> [Альянс для открытых медиа](http://aomedia.org/about/) был создан производителями оборудованья (Intel, AMD, ARM, Nvidia, Cisco), контент доставчиками (Google, Netflix, Amazon) создателями враузеров (Google, Mozilla) и другими.
>
> У компаний была общая цель - бесплатный видео кодек, результат чаго является AV1, у которого [патентная лицензия на много проще](http://aomedia.org/license/patent/). **Тимоти Б. Терриберри** сделал потрясающую презентацию, которая является источником этого раздела, о [концепции AV1, модели лицензии и ее текущем состоянии](https://www.youtube.com/watch?v=lzPaldsmJbk ).
>
> Вы будете удивлены, узнав что возможно **проанализировать кодек AV1 через браузер**: https://arewecompressedyet.com/analyzer/
>
>
>
> PS: Если вы хотите узнать больше об истории кодеков, взгляните на [патенты для сжатия видео](https://www.vcodex.com/video-compression-patents/).
## Общий кодек
Сейчас мы попытаемся описать **основные механизмы универсального видеокодека**, концепций которые полезны и используются в современных кодеках, на примере VP9, AV1 и HEVC. Это будут упрощенные схемы, хотя иногда мы будем использовать реальные примеры (в основном по отношению к H.264) для демонстрации техники.
## 1-й шаг - разделение изображения
Первым шагом действия кодека является **разделение кадра** на несколько **разделов, подразделов** и т.д.
**Зачем?** Есть много причин - разделяя картинку, мы можем точнее обрабатывать прогнозы, используя маленькие разделы для маленьких движущихся частей видео и большие разделы для статического фона.
Обычно кодеки **организуют эти разделы** в срезы (или фрагменты), макро (или блоки схемы кодирования) и множество подразделов. Максимальный размер этих разделов варьируется, HEVC устанавливает 64x64, в то время как AVC использует 16x16, но подразделы могут достигать размеров 4x4.
Вы помните **разные типы кадров**? Возможно **применить эти идеи и к блокам**, поэтому у нас могут быть I-Slice, B-Slice, I-Macroblock и т.д.
> ### Практика: проверка разделов
> Мы можем использовать [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (программа платная, хотя есть бестплатная версия где можно проанализировать первых десять кадров). Вот [разделы VP9](/encoding_pratical_examples.md#transcoding).
>
> 
## 2-й шаг - предсказания
Как только у нас есть разделы, мы можем использовать их для предсказаний. Для [меж кадрового предсказания](#временная-избыточность-меж-предскозание) нам нужно **использовать векторы движения и остаток**, а для [внутрикадрового предсказания](#пространственная-избыточность-внутреннее-предсказание) мы будем **использовать предсказанное направление и остаток**.
## 3-й шаг - трансформация
После того как мы получим остаточный блок (`предсказанный раздел - реальный раздел`), мы можем **трансформировать его** образом которым даст нам знать, какие **пиксели мы можем выбросить**, сохраняя при этом **общее качество**. Есть некоторые трансформации которые тут нам помогут.
Хотя есть [другие трансформации](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms), мы более подробно рассмотрим дискретную косинусную трансформацию, ДСТ (DCT). Основные функции [**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform):
* **Переводит блоки пикселей** в **блоки одинакового размера частотных коэффициентов**.
* **Сжимает** энергию, облегчая устранение пространственной избыточности.
* Является **обратимым процессом**, например, вы можете вернуться к пикселям.
> 2 февраля 2017 г. Синтра Р. Дж. И Байер Ф. М. опубликовали свою статью [ДСТ-подобное преобразование для сжатия изображений требует только 14 дополнений](https://arxiv.org/abs/1702.00817).
Не беспокойтесь, если вы не поняли преимуществ каждого пункта, мы попытаемся провести несколько экспериментов, чтобы понять процесс подробнее.
Давайте возьмем следующий **блок пикселей** (8x8):
Который рендерится как изображение (8x8):
Когда мы **применяем ДСТ** к этому блоку пикселей, мы получаем **блок коэффициентов** (8x8):
И если мы отрендерим этот блок коэффициентов, мы получим это изображение:
Как видно, это не похоже на исходное изображение, и похоже, что **первый коэффициент** очень отличается от всех остальных. Этот первый коэффициент известен как коэффициент DC, представляя себе **все выборки** во входном массиве, что-то **наподобие среднего числа**.
Этот блок коэффициентов обладает интересным свойством, заключающимся в том, что он отделяет высокочастотные компоненты от низкочастотных.
На изображении **большая часть энергии** будет сосредоточена на [**более низких частотах**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labour/zinke/mk/mpeg2beg/whatisit.htm), поэтому если мы трансформируем изображение в его частотные компоненты и **отбросим более высокие частотные коэффициенты**, мы можем **уменьшить объем данных** необходимо описать изображение, не жертвуя качеством сижком сильно.
> частота означает, насколько быстро меняется сигнал
Давайте попробуем применить знания, которые мы получили в тесте, трансформируя исходное изображение в его частоту (блок коэффициентов), используя ДСТ, а затем отбрасывая часть наименее важных коэффициентов.
Сначала мы конвертируем его в **частотную область**.
Далее мы отбрасываем часть (67%) коэффициентов, в основном нижнюю правую часть.
Наконец, мы восстанавливаем изображение из этого отброшенного блока коэффициентов (помните, оно должно быть обратимым) и сравниваем его с оригиналом.
Как видно, оно похоже на исходное изображение, но в нем есть много отличий от оригинала. Мы **выбросили 67,1875%** и все же смогли получить хотя бы что-то похожее на оригинал. Мы могли бы более разумно отбросить коэффициенты, чтобы получить лучшее качество изображения, но это следующая тема.
> **Каждый коэффициент формируется с использованием всех пикселей**
>
> Важно отметить, что каждый коэффициент не отображается напрямую на один пиксель, а представляет собой взвешенную сумму всех пикселей. Этот удивительный график показывает, как рассчитывается первый и второй коэффициент с использованием весов, уникальных для каждого индекса.
>
>
>
> Источник: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> Вы также можете попытаться [визуализировать ДСТ, взглянув на это изображение](/dct_better_explained.ipynb). Например, вот создается [символ "A"](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT) с помощю каждого веса коэффициента.
>
>! [] (https://upload.wikimedia.org/wikipedia/commons/5/5e/Idct-animation.gif)
> ### Практика: выбрасывание разных коэффициентов
> Вы можете поигратся с [ДСТ трансформациями тут](/uniform_quantization_experience.ipynb).
## 4-й шаг - квантование
Когда мы выбрасываем коэффициенты, на последнем шаге (трансформация) мы делали некоторую форму квантования. На этом этапе мы решили потерять информацию или, проще говоря, мы **квантовали коэффициенты для достижения сжатия**.
Как мы можем квантовать блок коэффициентов? Одним простым методом было бы равномерное квантование, где мы берем блок, **делим его на одно значение** (10) и округляем это значение.
Как мы можем **обратить** (переквантовать) этот блок коэффициентов? Мы можем **умножить одним и тем же значением** (10), которым мы разделили.
Этот **подход не лучший**, потому что он не учитывает важность каждого коэффициента. Мы могли бы использовать **матрицу квантователей** вместо одного значения, эта матрица может использовать свойство ДСТ, квантовая больше нижнего правого и меньше верхнего левого, [JPEG использует аналогичный подход](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html). Вы можете проверить [исходный код, чтобы увидеть эту матрицу](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40).
> ### Практока: квантование
> Вы можете поиграть с [квантованием] (/ dct_experiences.ipynb).
## 5-й шаг - энтропийное кодирование
После того как мы квантовали данные (блоки изображения / фрагменты / кадры), мы все еще можем сжимать их без потерь. Существует много способов (алгоритмов) сжатия данных. Мы собираемся кратко познакомиться с некоторыми из них, для более глубокого понимания вы можете прочитать удивительную книгу [Понимание сжатия: сжатие данных для современных разработчиков](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/).
### VLC кодирование:
Предположим, у нас есть поток символов: **a**, **e**, **r** и **t**, и их вероятность (от 0 до 1) представлена этой таблицей.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| вероятность | 0.3 | 0.3 | 0.2 | 0.2 |
Мы можем присвоить уникальные коды (предпочтительно маленькие) наиболее вероятным, а более крупные коды наименее вероятным.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| вероятность | 0.3 | 0.3 | 0.2 | 0.2 |
| код | 0 | 10 | 110 | 1110 |
Давайте сожмем поток **eat**, предполагая что мы потратим 8 битов на каждый символ, то в общей сложности мы тратим **24 бита** без какого-либо сжатия. Но в случае замены каждого символа на его код мы можем сэкономить место.
Первый шаг заключается в кодировании символа **e**, который равен `10`, второй символ **а** добавляется что бы получить `[10][0]` и, наконец, третий символ **t**, который делает наш окончательный сжатый поток битов равным `[10][0][1110]` или `1001110`, которому нужно только **7 битов** (т.е 3.4 раза меньше места, чем оригинальный поток).
Обратите внимание, что каждый код должен иметь уникальный префикс код [Хаффман может помочь вам найти эти номера](https://en.wikipedia.org/wiki/Huffman_coding). Хотя у этого метода есть проблемы, есть [видеокодеки, которые все еще предлагают](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding) его, и это алгоритм для многих приложений, который требует сжатия.
Кодер и декодер **должны знать** таблицу символов с ее кодом, поэтому вам также необходимо отправлять таблицу.
### Арифметическое кодирование:
Давай скажем что у нас есть поток символов: **a**, **e**, **r**, **s**, **t** и их вероятность показана на таблице:
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| вероятность | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
Зная об этой таблице, мы можем построить области со всеми возможными символами, отсортированные теми которые появляются чаще всего.
Теперь давайте закодируем поток **eat**, мы выбираем первый символ **a**, который находится в новом поддиапазоне **0.3 до 0.6** (но не включен), и мы берем этот поддиапазон и снова разделяем его, используя те же пропорции, которые использовались ранее, но в этом новом диапазоне.
Давайте продолжим кодировать наш поток **eat**, мы выберем второй символ **a**, который находится в новом поддиапазоне **0.3 до 0.39**, а затем мы берем наш последний символ **t**, повторяя процесс что бы получить последний поддиапазон **0.354 до 0.372**.
Нам просто нужно выбрать число в последнем поддиапазоне **0.354 до 0.372**, давайте выберем **0.36**, но мы можем выбрать любое число в этом поддиапазоне. С **только** этим номером мы сможем восстановить наш оригинальный поток **eat**. Если подумать об этом, мы как бы нарисовали линию в пределах диапазонов для кодирования нашего потока.
**Обратный процесс** (т.е. декодирование) одинаково прост, с нашим числом **0,36** и нашим исходным диапазоном мы можем запустить тот же процесс, но теперь используя это число, чтобы показать поток, закодированный за этим номером.
С первым диапазоном мы заметили что наше число соответствует срезу, поэтому это наш первый символ, теперь мы снова разбиваем этот поддиапазон, выполняя тот же процесс что и раньше, и мы можем заметить, что **0.36** соответствует символу **a** и после того, как мы повторим процесс, мы подходим к последнему символу **t** (формируя наш оригинальный кодированный поток *eat*).
Кодер и декодер **должны знать** таблицу вероятностей символов, поэтому вам необходимо отправлять таблицу.
Не плохо, правда? Люди чертовски умны, чтобы придумать такое решение, некоторые [видеокодеки используют](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding) эту технику (или, по крайней мере, предлагают ее в качестве опции).
Идея состоит в том, чтобы сжать без потерь квантованный поток битов, наверняка в этой статье отсутствуют тонны деталей, причин, компромиссов и т. Д. Но [вы должны узнать больше](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/) в качестве разработчика. Более новые кодеки пытаются использовать различные [алгоритмы энтропийного кодирования, наподобие ANS.](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)
> ### Практика: CABAC vs CAVLC
> Вы можете [создать два потока, один с CABAC and другой с CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc) and **сравнить время** занятое для их создание, а так же **конечный размер файла**.
## 6-й шаг - формат битового потока
После того, как мы выполнили все эти шаги, нам нужно **упаковать сжатые кадры и контекст к этим шагам**. Нам необходимо сообщить декодеру **о решениях, принятых кодером**, например битовая глубина, цветовое пространство, разрешение, информация о предсказаниях (векторы движения, направление внутреннего предсказания), профиль, уровень, частота кадров, тип кадра, номер кадра и многое другое.
Мы поверхностно изучим поток битов H.264. Наш первый шаг - [создать минимальный битовый поток H.264 *](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream), что мы можем сделать это с помощью нашего собственного репозитория и [ffmpeg](http://ffmpeg.org/).
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * ffmpeg по умолчанию добавляет все параметры кодирования в виде **SEI NAL** - вскоре мы определим, что такое NAL.
Эта команда сгенерирует необработанный поток битов h264 с **одиночным кадром**, 64x64, с цветовым пространством yuv420 и с использованием следующего изображения в качестве кадра.
>
### H.264 битовый поток
Стандарт AVC (H.264) определяет, что информация будет отправляться в **макро кадрах** (в смысле сети), называемых **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (Сетевой Уровень Aбстракции). Основной целью NAL является обеспечение "дружественного к сети" представления видео, этот стандарт должен работать на телевизорах (на основе потоков), в Интернете (на основе пакетов) и т.д.
Существует **[маркер синхронизации](https://en.wikipedia.org/wiki/Frame_synchronization)** для определения границ единиц NAL. Каждый маркер синхронизации содержит значение `0x00 0x00 0x01`, за исключением самого первого, который является` 0x00 0x00 0x00 0x01`. Если мы запустим **hexdump** в сгенерированном потоке битов h264, мы сможем идентифицировать как минимум три NAL в начале файла.
Как мы уже говорили, декодеру надо знать не только данные изображения, но также детали видео, фрейма, цвета, используемые параметры и другие. **Первый байт** каждого NAL определяет его категорию и **тип**.
| NAL type id | Description |
|--- |---|
| 0 | Undefined |
| 1 | Coded slice of a non-IDR picture |
| 2 | Coded slice data partition A |
| 3 | Coded slice data partition B |
| 4 | Coded slice data partition C |
| 5 | **IDR** Coded slice of an IDR picture |
| 6 | **SEI** Supplemental enhancement information |
| 7 | **SPS** Sequence parameter set |
| 8 | **PPS** Picture parameter set |
| 9 | Access unit delimiter |
| 10 | End of sequence |
| 11 | End of stream |
| ... | ... |
Обычно первый NAL битового потока представляет собой **SPS**, этот тип NAL отвечает за информирование общих переменных кодирования, таких как **профиль**, **уровень**, **разрешение** и другие.
Если мы пропустим первый маркер синхронизации, мы можем декодировать **первый байт**, чтобы узнать, какой **тип NAL** является первым.
Например, первый байт после маркера синхронизации равен `01100111`, где первый бит (`0`) находится в поле **forbidden_zero_bit**, следующие 2 бита (`11`) сообщают нам поле **nal_ref_idc** который указывает если этот является NAL опорным полем или нет, а остальные 5 битов (`00111`) информируют нас о поле **nal_unit_type**, в данном случае это **SPS** (7) единица NAL.
Второй байт (`binary = 01100100, hex = 0x64, dec = 100`) SPS NAL - это поле **profile_idc**, которое показывает профиль, который использовал кодировщик, в этом случае мы использовали **[ограниченный высокий профиль](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)**, это высокий профиль без поддержки B (двунаправленных) срезов.
Когда мы читаем спецификацию потока битов H.264 для SPS NAL, мы найдем много значений, например, **parameter name**, **category** и **description**, давайте посмотрим на `pic_width_in_mbs_minus_1` и поля `pic_height_in_map_units_minus_1`.
| Parameter name | Category | Description |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: целое число без знака [Exp-Golomb-кодированный](https://pythonhosted.org/bitstring/exp-golomb.html)
Если мы немного посчитаем значения этих полей, мы получим **разрешение**. Мы можем представить `1920 x 1080`, используя `pic_width_in_mbs_minus_1` со значением `119 ((119 + 1) * macroblock_size = 120 * 16 = 1920)`, опять же экономя место, вместо того, чтобы кодировать `1920`, как мы это сделали с `119`.
Если мы продолжим проверять наше созданное видео в двоичном виде (например: `xxd -b -c 11 v/minimal_yuv420.h264`), мы можем перейти к последнему NAL, который является самим кадром.
Мы можем видеть его первые 6 байтовых значений: `01100101 10001000 10000100 00000000 00100001 11111111`. Поскольку мы уже знаем, что первый байт говорит нам о том, что это за тип NAL, в данном случае (`00101`) это **IDR Slice (5)**, и мы можем проверить его:
Используя информацию спецификации, мы можем декодировать, какой тип слайса (**slice_type**), номер кадра (**frame_num**) среди других важных полей.
Чтобы получить значения некоторых полей (`ue (v), me (v), se (v) или te (v)`)), нам нужно декодировать его, используя специальный декодер под названием [Exponential-Golomb](https://pythonhosted.org/bitstring/exp-golomb.html), этот метод **очень эффективен для кодирования значений переменных**, главным образом, когда существует много значений по умолчанию.
> Значения **slice_type** и **frame_num** этого видео равны 7 (I срез) и 0 (первый кадр).
Мы можем видеть **поток битов как протокол**, и если вы хотите или вам надо узнать больше об этом потоке битов, обратитесь к [спецификации МСЭ H.264](http://www.itu.int/rec/T-REC-H.264-201610-I) Вот макросхема, которая показывает, где находятся данные изображения (сжатые YUV).
Мы можем исследовать другие битовые потоки, такие как [битовый поток VP9](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf) или даже наш **новый лучший друг** [**AV1** поток битов](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
), [они все похожи? Нет](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/), но как только вы выучите один, вы легко сможете понять другие.
> ### Практика: Проверка битовога потока H.264
> Мы можем [создать однокадровое видео](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) и использовать [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) для проверки потока битов H.264. Вы даже можете увидеть [исходный код, который анализирует битовый поток h264 (AVC)](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp).
>
> 
>
> Мы также можем использовать [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer), который платный, хотя существует бесплатная пробная версия, которая ограничивает вас только первые 10 кадров (которых для учебных целей должно быть достаточно).
>
> 
## Обзор
Мы заметим, что многие из **современных кодеков используют ту же модель, которую мы изучили**. Можно посмотреть на блок-схему видеокодека Thor и увидеть что она содержит все шаги, которые мы изучали. Идея состоит в том, что теперь вы в состоянии лучше понять инновации и документы в этой области.
Ранее мы рассчитывали, что нам потребуется [139 ГБ дискового пространства для хранения видеофайла одного часа в длинне при разрешении 720p и 30 к/с](#цветовая-субдискретизация). С помощю методов которые описаны здесь, такие как **меж и внутреннее предсказание, трансформации, квантование, энтропийное кодирование и другие**, то же видео воспринимаемого качества можно сжать до **только 367.82 МБ**.
> Мы решили использовать **0.031 бит на пиксель** на основе примера видео, представленного здесь.
## Как H.265 обеспечивает лучшую степень сжатия чем H.264?
Зная больше о том, как работают кодеки, легко понять как новые кодеки способны обеспечивать более высокое разрешение с меньшим количеством битов.
Мы сравним AVC и HEVC, помня что это почти всегда компромисс между большим количеством циклов ЦП (сложность) и степенью сжатия.
HEVC имеет больше количество **разделов** (и **подразделов**), которые так же и больше в размере чем AVC, больше **направлений внутреннего предсказания**, **улучшенное энтропийное кодирование** и более, все эти улучшения дали H.265 способность сжимать на 50% больше, чем H.264.
# Онлайн трансляция
## Общая архитектура
! [общая архитектура](/i/general_architecture.png "общая архитектура")
[TODO]
## Прогрессивная загрузка и адаптивная передача
[TODO]
## Защита контента
Мы можем использовать **простую систему токенов** для защиты контента. Запрос видео без токена будет запрещен CDN-ом, в то время как пользователь с действительным токеном может воспроизводить контент. Он работает почти так же, как и большинство систем веб-аутентификации.
Использую эту систему токенов позволяет пользователю загружать видео и распространять его. Затем можно использовать систему **DRM (управление цифровыми правами)**, чтобы избежать этого.
В реальных производственных системах люди часто используют оба метода для обеспечения авторизации и аутентификации.
### DRM
#### Основные системы
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### Какой?
DRM зто система цифровых прав, это способ **обеспечить защиту авторских прав на цифровых материалов** - например, цифровое видео и аудио. Хотя он используется во многих местах [он не является общепринятым](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### Зачем?
Создатель контента (в основном, студии) хотят защитить свою интеллектуальную собственность от копирования, чтобы предотвратить несанкционированное распространение цифрового контента.
#### Как?
Мы собираемся описать абстрактную и общую форму DRM в очень упрощенном виде.
Учитывая **контент C1** (т.е. потоковое видео hls или dash), с **проигрывателем P1** (т.е. shaka-clappr, exo-player или ios) в **устройстве D1** (т.е. смартфоне , Телевизор, планшет или настольный компьютер / ноутбук) с использованием **DRM системы DRM1** (widevine, playready или FairPlay).
Контент C1 зашифрован с помощью **симметричного ключа K1** от системы DRM1, генерируя **зашифрованный контент C'1**.
Проигрыватель P1 устройства D1 имеет два ключа (асимметричные): **закрытый ключ PRK1** (этот ключ защищен 1 и известен только **D1**) и **открытый ключ PUK1**.
> ** 1 защищен **: эта защита может быть ** с помощью аппаратного обеспечения **, например, этот ключ может храниться в специальном (только для чтения) чипе, который работает как [черный box] (https://en.wikipedia.org/wiki/Black_box), чтобы обеспечить расшифровку, или ** с помощью программного обеспечения ** (менее безопасно), система DRM предоставляет средства, чтобы узнать, какой тип защиты имеет данное устройство.
Когда ** игрок P1 хочет воспроизвести ** контент ** C'1 **, он должен иметь дело с ** DRM-системой DRM1 **, предоставив свой открытый ключ ** PUK1 **. DRM-система DRM1 возвращает ** ключ K1 в зашифрованном виде ** с открытым ключом клиента ** PUK1 **. Теоретически, этот ответ - это то, что ** только D1 способен расшифровать **.
`K1P1D1 = enc (K1, PUK1)`
**P1** использует свою локальную систему DRM (это может быть [SOC](https://en.wikipedia.org/wiki/System_on_a_chip), специализированное аппаратное или программное обеспечение), эта система **может дешифровать** контент, и используя свой закрытый ключ PRK1, может расшифровать **симметричный ключ K1 от K1P1D1** и **воспроизвести C'1**. В лучшем случае ключи не выставляются через ОЗУ.
``,
K1 = dec (K1P1D1, PRK1)
P1.play (dec (C'1, K1))
``,
# Как использовать Jupyter
Убедитесь, что у вас установлен **docker** и просто запустите `./s/ start_jupyter.sh` и следуйте инструкциям на терминале.
# Конференции
* [DEMUXED](https://demuxed.com/) - вы можете [проверить последние 2 презентации.](Https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# Ссылки
Здесь самый богатый контент, вся информация которую мы видели в этом тексте, была извлечена, основана или вдохновлена. Вы можете углубить свои знания с помощью этих удивительных ссылок, книг, видео и т. д.
Онлайн курсы и учебные пособия:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Books:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Onboarding material:
* https://github.com/Eyevinn/streaming-onboarding
* https://howvideo.works/
* https://www.aws.training/Details/eLearning?id=17775
* https://www.aws.training/Details/eLearning?id=17887
* https://www.aws.training/Details/Video?id=24750
Bitstream Specifications:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/
================================================
FILE: README.md
================================================
[🇺🇸](/README.md "English")
[🇨🇳](/README-cn.md "Simplified Chinese")
[🇯🇵](/README-ja.md "Japanese")
[🇮🇹](/README-it.md "Italian")
[🇰🇷](/README-ko.md "Korean")
[🇷🇺](/README-ru.md "Russian")
[🇧🇷](/README-pt.md "Portuguese")
[🇪🇸](/README-es.md "Spanish")
[](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)
# Intro
A gentle introduction to video technology, although it's aimed at software developers / engineers, we want to make it easy **for anyone to learn**. This idea was born during a [mini workshop for newcomers to video technology](https://docs.google.com/presentation/d/17Z31kEkl_NGJ0M66reqr9_uTG6tI5EDDVXpdPKVuIrs/edit#slide=id.p).
The goal is to introduce some digital video concepts with a **simple vocabulary, lots of visual elements and practical examples** when possible, and make this knowledge available everywhere. Please, feel free to send corrections, suggestions and improve it.
There will be **hands-on** sections which require you to have **docker installed** and this repository cloned.
```bash
git clone https://github.com/leandromoreira/digital_video_introduction.git
cd digital_video_introduction
./setup.sh
```
> **WARNING**: when you see a `./s/ffmpeg` or `./s/mediainfo` command, it means we're running a **containerized version** of that program, which already includes all the needed requirements.
All the **hands-on should be performed from the folder you cloned** this repository. For the **jupyter examples** you must start the server `./s/start_jupyter.sh` and copy the URL and use it in your browser.
# Changelog
* added DRM system
* released version 1.0.0
* added simplified Chinese translation
* added FFmpeg oscilloscope filter example
* added Brazilian Portuguese translation
* added Spanish translation
# Index
- [Intro](#intro)
- [Index](#index)
- [Basic terminology](#basic-terminology)
* [Other ways to encode a color image](#other-ways-to-encode-a-color-image)
* [Hands-on: play around with image and color](#hands-on-play-around-with-image-and-color)
* [DVD is DAR 4:3](#dvd-is-dar-43)
* [Hands-on: Check video properties](#hands-on-check-video-properties)
- [Redundancy removal](#redundancy-removal)
* [Colors, Luminance and our eyes](#colors-luminance-and-our-eyes)
+ [Color model](#color-model)
+ [Converting between YCbCr and RGB](#converting-between-ycbcr-and-rgb)
+ [Chroma subsampling](#chroma-subsampling)
+ [Hands-on: Check YCbCr histogram](#hands-on-check-ycbcr-histogram)
* [Frame types](#frame-types)
+ [I Frame (intra, keyframe)](#i-frame-intra-keyframe)
+ [P Frame (predicted)](#p-frame-predicted)
- [Hands-on: A video with a single I-frame](#hands-on-a-video-with-a-single-i-frame)
+ [B Frame (bi-predictive)](#b-frame-bi-predictive)
- [Hands-on: Compare videos with B-frame](#hands-on-compare-videos-with-b-frame)
+ [Summary](#summary)
* [Temporal redundancy (inter prediction)](#temporal-redundancy-inter-prediction)
- [Hands-on: See the motion vectors](#hands-on-see-the-motion-vectors)
* [Spatial redundancy (intra prediction)](#spatial-redundancy-intra-prediction)
- [Hands-on: Check intra predictions](#hands-on-check-intra-predictions)
- [How does a video codec work?](#how-does-a-video-codec-work)
* [What? Why? How?](#what-why-how)
* [History](#history)
+ [The birth of AV1](#the-birth-of-av1)
* [A generic codec](#a-generic-codec)
* [1st step - picture partitioning](#1st-step---picture-partitioning)
+ [Hands-on: Check partitions](#hands-on-check-partitions)
* [2nd step - predictions](#2nd-step---predictions)
* [3rd step - transform](#3rd-step---transform)
+ [Hands-on: throwing away different coefficients](#hands-on-throwing-away-different-coefficients)
* [4th step - quantization](#4th-step---quantization)
+ [Hands-on: quantization](#hands-on-quantization)
* [5th step - entropy coding](#5th-step---entropy-coding)
+ [VLC coding](#vlc-coding)
+ [Arithmetic coding](#arithmetic-coding)
+ [Hands-on: CABAC vs CAVLC](#hands-on-cabac-vs-cavlc)
* [6th step - bitstream format](#6th-step---bitstream-format)
+ [H.264 bitstream](#h264-bitstream)
+ [Hands-on: Inspect the H.264 bitstream](#hands-on-inspect-the-h264-bitstream)
* [Review](#review)
* [How does H.265 achieve a better compression ratio than H.264?](#how-does-h265-achieve-a-better-compression-ratio-than-h264)
- [Online streaming](#online-streaming)
* [General architecture](#general-architecture)
* [Progressive download and adaptive streaming](#progressive-download-and-adaptive-streaming)
* [Content protection](#content-protection)
- [How to use jupyter](#how-to-use-jupyter)
- [Conferences](#conferences)
- [References](#references)
# Basic terminology
An **image** can be thought of as a **2D matrix**. If we think about **colors**, we can extrapolate this idea seeing this image as a **3D matrix** where the **additional dimensions** are used to provide **color data**.
If we chose to represent these colors using the [primary colors (red, green and blue)](https://en.wikipedia.org/wiki/Primary_color), we define three planes: the first one for **red**, the second for **green**, and the last one for the **blue** color.
We'll call each point in this matrix **a pixel** (picture element). One pixel represents the **intensity** (usually a numeric value) of a given color. For example, a **red pixel** means 0 of green, 0 of blue and maximum of red. The **pink color pixel** can be formed with a combination of the three colors. Using a representative numeric range from 0 to 255, the pink pixel is defined by **Red=255, Green=192 and Blue=203**.
> #### Other ways to encode a color image
> Many other possible models may be used to represent the colors that make up an image. We could, for instance, use an indexed palette where we'd only need a single byte to represent each pixel instead of the 3 needed when using the RGB model. In such a model we could use a 2D matrix instead of a 3D matrix to represent our color, this would save on memory but yield fewer color options.
>
> 
For instance, look at the picture down below. The first face is fully colored. The others are the red, green, and blue planes (shown as gray tones).
We can see that the **red color** will be the one that **contributes more** (the brightest parts in the second face) to the final color while the **blue color** contribution can be mostly **only seen in Mario's eyes** (last face) and part of his clothes, see how **all planes contribute less** (darkest parts) to the **Mario's mustache**.
And each color intensity requires a certain amount of bits, this quantity is known as **bit depth**. Let's say we spend **8 bits** (accepting values from 0 to 255) per color (plane), therefore we have a **color depth** of **24 bits** (8 bits * 3 planes R/G/B), and we can also infer that we could use 2 to the power of 24 different colors.
> **It's great** to learn [how an image is captured from the world to the bits](http://www.cambridgeincolour.com/tutorials/camera-sensors.htm).
Another property of an image is the **resolution**, which is the number of pixels in one dimension. It is often presented as width × height, for example, the **4×4** image below.
> #### Hands-on: play around with image and color
> You can [play around with image and colors](/image_as_3d_array.ipynb) using [jupyter](#how-to-use-jupyter) (python, numpy, matplotlib and etc).
>
> You can also learn [how image filters (edge detection, sharpen, blur...) work](/filters_are_easy.ipynb).
Another property we can see while working with images or video is the **aspect ratio** which simply describes the proportional relationship between width and height of an image or pixel.
When people says this movie or picture is **16x9** they usually are referring to the **Display Aspect Ratio (DAR)**, however we also can have different shapes of individual pixels, we call this **Pixel Aspect Ratio (PAR)**.
> #### DVD is DAR 4:3
> Although the real resolution of a DVD is 704x480 it still keeps a 4:3 aspect ratio because it has a PAR of 10:11 (704x10/480x11)
Finally, we can define a **video** as a **succession of *n* frames** in **time** which can be seen as another dimension, *n* is the frame rate or frames per second (FPS).
The number of bits per second needed to show a video is its **bit rate**.
> bit rate = width * height * bit depth * frames per second
For example, a video with 30 frames per second, 24 bits per pixel, resolution of 480x240 will need **82,944,000 bits per second** or 82.944 Mbps (30x480x240x24) if we don't employ any kind of compression.
When the **bit rate** is nearly constant it's called constant bit rate (**CBR**) but it also can vary then called variable bit rate (**VBR**).
> This graph shows a constrained VBR which doesn't spend too many bits while the frame is black.
>
> 
In the early days, engineers came up with a technique for doubling the perceived frame rate of a video display **without consuming extra bandwidth**. This technique is known as **interlaced video**; it basically sends half of the screen in 1 "frame" and the other half in the next "frame".
Today screens render mostly using **progressive scan technique**. Progressive is a way of displaying, storing, or transmitting moving images in which all the lines of each frame are drawn in sequence.
Now we have an idea about how an **image** is represented digitally, how its **colors** are arranged, how many **bits per second** do we spend to show a video, if it's constant (CBR) or variable (VBR), with a given **resolution** using a given **frame rate** and many other terms such as interlaced, PAR and others.
> #### Hands-on: Check video properties
> You can [check most of the explained properties with ffmpeg or mediainfo.](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#inspect-stream)
# Redundancy removal
We learned that it's not feasible to use video without any compression; **a single one hour video** at 720p resolution with 30fps would **require 278GB***. Since **using solely lossless data compression algorithms** like DEFLATE (used in PKZIP, Gzip, and PNG), **won't** decrease the required bandwidth sufficiently we need to find other ways to compress the video.
> * We found this number by multiplying 1280 x 720 x 24 x 30 x 3600 (width, height, bits per pixel, fps and time in seconds)
In order to do this, we can **exploit how our vision works**. We're better at distinguishing brightness than colors, the **repetitions in time**, a video contains a lot of images with few changes, and the **repetitions within the image**, each frame also contains many areas using the same or similar color.
## Colors, Luminance and our eyes
Our eyes are [more sensitive to brightness than colors](http://vanseodesign.com/web-design/color-luminance/), you can test it for yourself, look at this picture.
If you are unable to see that the colors of the **squares A and B are identical** on the left side, that's fine, it's our brain playing tricks on us to **pay more attention to light and dark than color**. There is a connector, with the same color, on the right side so we (our brain) can easily spot that in fact, they're the same color.
> **Simplistic explanation of how our eyes work**
> The [eye is a complex organ](http://www.biologymad.com/nervoussystem/eyenotes.htm), it is composed of many parts but we are mostly interested in the cones and rods cells. The eye [contains about 120 million rod cells and 6 million cone cells](https://en.wikipedia.org/wiki/Photoreceptor_cell).
>
> To **oversimplify**, let's try to put colors and brightness in the eye's parts function. The **[rod cells](https://en.wikipedia.org/wiki/Rod_cell) are mostly responsible for brightness** while the **[cone cells](https://en.wikipedia.org/wiki/Cone_cell) are responsible for color**, there are three types of cones, each with different pigment, namely: [S-cones (Blue), M-cones (Green) and L-cones (Red)](https://upload.wikimedia.org/wikipedia/commons/1/1e/Cones_SMJ2_E.svg).
>
> Since we have many more rod cells (brightness) than cone cells (color), one can infer that we are more capable of distinguishing dark and light than colors.
>
> 
>
> **Contrast sensitivity functions**
>
> Researchers of experimental psychology and many other fields have developed many theories on human vision. And one of them is called Contrast sensitivity functions. They are related to spatio and temporal of the light and their value presents at given init light, how much change is required before an observer reported there was a change. Notice the plural of the word "function", this is for the reason that we can measure Contrast sensitivity functions with not only black-white but also colors. The result of these experiments shows that in most cases our eyes are more sensitive to brightness than color.
Once we know that we're more sensitive to **luma** (the brightness in an image) we can try to exploit it.
### Color model
We first learned [how to color images](#basic-terminology) work using the **RGB model**, but there are other models too. In fact, there is a model that separates luma (brightness) from chrominance (colors) and it is known as **YCbCr***.
> * there are more models which do the same separation.
This color model uses **Y** to represent the brightness and two color channels **Cb** (chroma blue) and **Cr** (chroma red). The [YCbCr](https://en.wikipedia.org/wiki/YCbCr) can be derived from RGB and it also can be converted back to RGB. Using this model we can create full colored images as we can see down below.
### Converting between YCbCr and RGB
Some may argue, how can we produce all the **colors without using the green**?
To answer this question, we'll walk through a conversion from RGB to YCbCr. We'll use the coefficients from the **[standard BT.601](https://en.wikipedia.org/wiki/Rec._601)** that was recommended by the **[group ITU-R*](https://en.wikipedia.org/wiki/ITU-R)** . The first step is to **calculate the luma**, we'll use the constants suggested by ITU and replace the RGB values.
```
Y = 0.299R + 0.587G + 0.114B
```
Once we had the luma, we can **split the colors** (chroma blue and red):
```
Cb = 0.564(B - Y)
Cr = 0.713(R - Y)
```
And we can also **convert it back** and even get the **green by using YCbCr**.
```
R = Y + 1.402Cr
B = Y + 1.772Cb
G = Y - 0.344Cb - 0.714Cr
```
> * groups and standards are common in digital video, they usually define what are the standards, for instance, [what is 4K? what frame rate should we use? resolution? color model?](https://en.wikipedia.org/wiki/Rec._2020)
Generally, **displays** (monitors, TVs, screens and etc) utilize **only the RGB model**, organized in different manners, see some of them magnified below:
### Chroma subsampling
With the image represented as luma and chroma components, we can take advantage of the human visual system's greater sensitivity for luma resolution rather than chroma to selectively remove information. **Chroma subsampling** is the technique of encoding images using **less resolution for chroma than for luma**.
How much should we reduce the chroma resolution?! It turns out that there are already some schemas that describe how to handle resolution and the merge (`final color = Y + Cb + Cr`).
These schemas are known as subsampling systems and are expressed as a 3 part ratio - `a:x:y` which defines the chroma resolution in relation to a `a x 2` block of luma pixels.
* `a` is the horizontal sampling reference (usually 4)
* `x` is the number of chroma samples in the first row of `a` pixels (horizontal resolution in relation to `a`)
* `y` is the number of changes of chroma samples between the first and seconds rows of `a` pixels.
> An exception to this exists with 4:1:0, which provides a single chroma sample within each `4 x 4` block of luma resolution.
Common schemes used in modern codecs are: **4:4:4** *(no subsampling)*, **4:2:2, 4:1:1, 4:2:0, 4:1:0 and 3:1:1**.
> You can follow some discussions [to learn more about Chroma Subsampling](https://github.com/leandromoreira/digital_video_introduction/issues?q=YCbCr).
> **YCbCr 4:2:0 merge**
>
> Here's a merged piece of an image using YCbCr 4:2:0, notice that we only spend 12 bits per pixel.
>
> 
You can see the same image encoded by the main chroma subsampling types, images in the first row are the final YCbCr while the last row of images shows the chroma resolution. It's indeed a great win for such small loss.
Previously we had calculated that we needed [278GB of storage to keep a video file with one hour at 720p resolution and 30fps](#redundancy-removal). If we use **YCbCr 4:2:0** we can cut **this size in half (139 GB)*** but it is still far from ideal.
> * we found this value by multiplying width, height, bits per pixel and fps. Previously we needed 24 bits, now we only need 12.
> ### Hands-on: Check YCbCr histogram
> You can [check the YCbCr histogram with ffmpeg.](/encoding_pratical_examples.md#generates-yuv-histogram) This scene has a higher blue contribution, which is showed by the [histogram](https://en.wikipedia.org/wiki/Histogram).
>
> 
### Color, luma, luminance, gamma video review
Watch this incredible video explaining what is luma and learn about luminance, gamma, and color.
[](http://www.youtube.com/watch?v=Ymt47wXUDEU)
> ### Hands-on: Check YCbCr intensity
> You can visualize the Y intensity for a given line of a video using [FFmpeg's oscilloscope filter](https://ffmpeg.org/ffmpeg-filters.html#oscilloscope).
> ```bash
> ffplay -f lavfi -i 'testsrc2=size=1280x720:rate=30000/1001,format=yuv420p' -vf oscilloscope=x=0.5:y=200/720:s=1:c=1
> ```
> 
## Frame types
Now we can move on and try to eliminate the **redundancy in time** but before that let's establish some basic terminology. Suppose we have a movie with 30fps, here are its first 4 frames.
  
We can see **lots of repetitions** within frames like **the blue background**, it doesn't change from frame 0 to frame 3. To tackle this problem, we can **abstractly categorize** them as three types of frames.
### I Frame (intra, keyframe)
An I-frame (reference, keyframe, intra) is a **self-contained frame**. It doesn't rely on anything to be rendered, an I-frame looks similar to a static photo. The first frame is usually an I-frame but we'll see I-frames inserted regularly among other types of frames.
### P Frame (predicted)
A P-frame takes advantage of the fact that almost always the current picture can be **rendered using the previous frame.** For instance, in the second frame, the only change was the ball that moved forward. We can **rebuild frame 1, only using the difference and referencing to the previous frame**.
 <- 
> #### Hands-on: A video with a single I-frame
> Since a P-frame uses less data why can't we encode an entire [video with a single I-frame and all the rest being P-frames?](/encoding_pratical_examples.md#1-i-frame-and-the-rest-p-frames)
>
> After you encoded this video, start to watch it and do a **seek for an advanced** part of the video, you'll notice **it takes some time** to really move to that part. That's because a **P-frame needs a reference frame** (I-frame for instance) to be rendered.
>
> Another quick test you can do is to encode a video using a single I-Frame and then [encode it inserting an I-frame each 2s](/encoding_pratical_examples.md#1-i-frames-per-second-vs-05-i-frames-per-second) and **check the size of each rendition**.
### B Frame (bi-predictive)
What about referencing the past and future frames to provide even a better compression?! That's basically what a B-frame is.
 <-  -> 
> #### Hands-on: Compare videos with B-frame
> You can generate two renditions, first with B-frames and other with [no B-frames at all](/encoding_pratical_examples.md#no-b-frames-at-all) and check the size of the file as well as the quality.
### Summary
These frames types are used to **provide better compression**. We'll look how this happens in the next section, but for now we can think of **I-frame as expensive while P-frame is cheaper but the cheapest is the B-frame.**
## Temporal redundancy (inter prediction)
Let's explore the options we have to reduce the **repetitions in time**, this type of redundancy can be solved with techniques of **inter prediction**.
We will try to **spend fewer bits** to encode the sequence of frames 0 and 1.
One thing we can do it's a subtraction, we simply **subtract frame 1 from frame 0** and we get just what we need to **encode the residual**.
But what if I tell you that there is a **better method** which uses even fewer bits?! First, let's treat the `frame 0` as a collection of well-defined partitions and then we'll try to match the blocks from `frame 0` on `frame 1`. We can think of it as **motion estimation**.
> ### Wikipedia - block motion compensation
> "**Block motion compensation** divides up the current frame into non-overlapping blocks, and the motion compensation vector **tells where those blocks come from** (a common misconception is that the previous frame is divided up into non-overlapping blocks, and the motion compensation vectors tell where those blocks move to). The source blocks typically overlap in the source frame. Some video compression algorithms assemble the current frame out of pieces of several different previously-transmitted frames."
We could estimate that the ball moved from `x=0, y=25` to `x=6, y=26`, the **x** and **y** values are the **motion vectors**. One **further step** we can do to save bits is to **encode only the motion vector difference** between the last block position and the predicted, so the final motion vector would be `x=6 (6-0), y=1 (26-25)`
> In a real-world situation, this **ball would be sliced into n partitions** but the process is the same.
The objects on the frame **move in a 3D way**, the ball can become smaller when it moves to the background. It's normal that **we won't find the perfect match** to the block we tried to find a match. Here's a superposed view of our estimation vs the real picture.
But we can see that when we apply **motion estimation** the **data to encode is smaller** than using simply delta frame techniques.
> ### How real motion compensation would look
> This technique is applied to all blocks, very often a ball would be partitioned in more than one block.
> 
> Source: https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
You can [play around with these concepts using jupyter](/frame_difference_vs_motion_estimation_plus_residual.ipynb).
> #### Hands-on: See the motion vectors
> We can [generate a video with the inter prediction (motion vectors) with ffmpeg.](/encoding_pratical_examples.md#generate-debug-video)
>
> 
>
> Or we can use the [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (which is paid but there is a free trial version which limits you to only work with the first 10 frames).
>
> 
## Spatial redundancy (intra prediction)
If we analyze **each frame** in a video we'll see that there are also **many areas that are correlated**.
Let's walk through an example. This scene is mostly composed of blue and white colors.
This is an `I-frame` and we **can't use previous frames** to predict from but we still can compress it. We will encode the red block selection. If we **look at its neighbors**, we can **estimate** that there is a **trend of colors around it**.
We will **predict** that the frame will continue to **spread the colors vertically**, it means that the colors of the **unknown pixels will hold the values of its neighbors**.
Our **prediction can be wrong**, for that reason we need to apply this technique (**intra prediction**) and then **subtract the real values** which gives us the residual block, resulting in a much more compressible matrix compared to the original.
There are many different types of this sort of prediction. The one you see pictured here is a form of straight planar prediction, where the pixels from the row above the block are copied row to row within the block. Planar prediction also can involve an angular component, where pixels from both the left and the top are used to help predict the current block. And there is also DC prediction, which involves taking the average of the samples right above and to the left of the block.
> #### Hands-on: Check intra predictions
> You can [generate a video with macro blocks and their predictions with ffmpeg.](/encoding_pratical_examples.md#generate-debug-video) Please check the ffmpeg documentation to understand the [meaning of each block color](https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7#AnalyzingMacroblockTypes).
>
> 
>
> Or we can use the [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (which is paid but there is a free trial version which limits you to only work with the first 10 frames).
>
> 
# How does a video codec work?
## What? Why? How?
**What?** It's a piece of software / hardware that compresses or decompresses digital video. **Why?** Market and society demands higher quality videos with limited bandwidth or storage. Remember when we [calculated the needed bandwidth](#basic-terminology) for 30 frames per second, 24 bits per pixel, resolution of a 480x240 video? It was **82.944 Mbps** with no compression applied. It's the only way to deliver HD/FullHD/4K in TVs and the Internet. **How?** We'll take a brief look at the major techniques here.
> **CODEC vs Container**
>
> One common mistake that beginners often do is to confuse digital video CODEC and [digital video container](https://en.wikipedia.org/wiki/Digital_container_format). We can think of **containers** as a wrapper format which contains metadata of the video (and possible audio too), and the **compressed video** can be seen as its payload.
>
> Usually, the extension of a video file defines its video container. For instance, the file `video.mp4` is probably a **[MPEG-4 Part 14](https://en.wikipedia.org/wiki/MPEG-4_Part_14)** container and a file named `video.mkv` it's probably a **[matroska](https://en.wikipedia.org/wiki/Matroska)**. To be completely sure about the codec and container format we can use [ffmpeg or mediainfo](/encoding_pratical_examples.md#inspect-stream).
## History
Before we jump into the inner workings of a generic codec, let's look back to understand a little better about some old video codecs.
The video codec [H.261](https://en.wikipedia.org/wiki/H.261) was born in 1990 (technically 1988), and it was designed to work with **data rates of 64 kbit/s**. It already uses ideas such as chroma subsampling, macro block, etc. In the year of 1995, the **H.263** video codec standard was published and continued to be extended until 2001.
In 2003 the first version of **H.264/AVC** was completed. In the same year, **On2 Technologies** (formerly known as the Duck Corporation) released their video codec as a **royalty-free** lossy video compression called **VP3**. In 2008, **Google bought** this company, releasing **VP8** in the same year. In December of 2012, Google released the **VP9** and it's **supported by roughly ¾ of the browser market** (mobile included).
**[AV1](https://en.wikipedia.org/wiki/AOMedia_Video_1)** is a new **royalty-free** and open source video codec that's being designed by the [Alliance for Open Media (AOMedia)](http://aomedia.org/), which is composed of the **companies: Google, Mozilla, Microsoft, Amazon, Netflix, AMD, ARM, NVidia, Intel and Cisco** among others. The **first version** 0.1.0 of the reference codec was **published on April 7, 2016**.
> #### The birth of AV1
>
> Early 2015, Google was working on [VP10](https://en.wikipedia.org/wiki/VP9#Successor:_from_VP10_to_AV1), Xiph (Mozilla) was working on [Daala](https://xiph.org/daala/) and Cisco open-sourced its royalty-free video codec called [Thor](https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03).
>
> Then MPEG LA first announced annual caps for HEVC (H.265) and fees 8 times higher than H.264 but soon they changed the rules again:
> * **no annual cap**,
> * **content fee** (0.5% of revenue) and
> * **per-unit fees about 10 times higher than h264**.
>
> The [alliance for open media](http://aomedia.org/about/) was created by companies from hardware manufacturer (Intel, AMD, ARM , Nvidia, Cisco), content delivery (Google, Netflix, Amazon), browser maintainers (Google, Mozilla), and others.
>
> The companies had a common goal, a royalty-free video codec and then AV1 was born with a much [simpler patent license](http://aomedia.org/license/patent/). **Timothy B. Terriberry** did an awesome presentation, which is the source of this section, about the [AV1 conception, license model and its current state](https://www.youtube.com/watch?v=lzPaldsmJbk).
>
> You'll be surprised to know that you can **analyze the AV1 codec through your browser**, go to https://arewecompressedyet.com/analyzer/
>
> 
>
> PS: If you want to learn more about the history of the codecs you must learn the basics behind [video compression patents](https://www.vcodex.com/video-compression-patents/).
## A generic codec
We're going to introduce the **main mechanics behind a generic video codec** but most of these concepts are useful and used in modern codecs such as VP9, AV1 and HEVC. Be sure to understand that we're going to simplify things a LOT. Sometimes we'll use a real example (mostly H.264) to demonstrate a technique.
## 1st step - picture partitioning
The first step is to **divide the frame** into several **partitions, sub-partitions** and beyond.
**But why?** There are many reasons, for instance, when we split the picture we can work the predictions more precisely, using small partitions for the small moving parts while using bigger partitions to a static background.
Usually, the CODECs **organize these partitions** into slices (or tiles), macro (or coding tree units) and many sub-partitions. The max size of these partitions varies, HEVC sets 64x64 while AVC uses 16x16 but the sub-partitions can reach sizes of 4x4.
Remember that we learned how **frames are typed**?! Well, you can **apply those ideas to blocks** too, therefore we can have I-Slice, B-Slice, I-Macroblock and etc.
> ### Hands-on: Check partitions
> We can also use the [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) (which is paid but there is a free trial version which limits you to only work with the first 10 frames). Here are [VP9 partitions](/encoding_pratical_examples.md#transcoding) analyzed.
>
> 
## 2nd step - predictions
Once we have the partitions, we can make predictions over them. For the [inter prediction](#temporal-redundancy-inter-prediction) we need **to send the motion vectors and the residual** and the [intra prediction](#spatial-redundancy-intra-prediction) we'll **send the prediction direction and the residual** as well.
## 3rd step - transform
After we get the residual block (`predicted partition - real partition`), we can **transform** it in a way that lets us know which **pixels we can discard** while keeping the **overall quality**. There are some transformations for this exact behavior.
Although there are [other transformations](https://en.wikipedia.org/wiki/List_of_Fourier-related_transforms#Discrete_transforms), we'll look more closely at the discrete cosine transform (DCT). The [**DCT**](https://en.wikipedia.org/wiki/Discrete_cosine_transform) main features are:
* **converts** blocks of **pixels** into same-sized blocks of **frequency coefficients**.
* **compacts** energy, making it easy to eliminate spatial redundancy.
* is **reversible**, a.k.a. you can reverse to pixels.
> On 2 Feb 2017, Cintra, R. J. and Bayer, F. M have published their paper [DCT-like Transform for Image Compression
Requires 14 Additions Only](https://arxiv.org/abs/1702.00817).
Don't worry if you didn't understand the benefits from every bullet point, we'll try to make some experiments in order to see the real value from it.
Let's take the following **block of pixels** (8x8):
Which renders to the following block image (8x8):
When we **apply the DCT** over this block of pixels and we get the **block of coefficients** (8x8):
And if we render this block of coefficients, we'll get this image:
As you can see it looks nothing like the original image, we might notice that the **first coefficient** is very different from all the others. This first coefficient is known as the DC coefficient which represents **all the samples** in the input array, something **similar to an average**.
This block of coefficients has an interesting property which is that it separates the high-frequency components from the low frequency.
In an image, **most of the energy** will be concentrated in the [**lower frequencies**](https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm), so if we transform an image into its frequency components and **throw away the higher frequency coefficients**, we can **reduce the amount of data** needed to describe the image without sacrificing too much image quality.
> frequency means how fast a signal is changing
Let's try to apply the knowledge we acquired in the test by converting the original image to its frequency (block of coefficients) using DCT and then throwing away part of the least important coefficients.
First, we convert it to its **frequency domain**.
Next, we discard part (67%) of the coefficients, mostly the bottom right part of it.
Finally, we reconstruct the image from this discarded block of coefficients (remember, it needs to be reversible) and compare it to the original.
As we can see it resembles the original image but it introduced lots of differences from the original, we **throw away 67.1875%** and we still were able to get at least something similar to the original. We could more intelligently discard the coefficients to have a better image quality but that's the next topic.
> **Each coefficient is formed using all the pixels**
>
> It's important to note that each coefficient doesn't directly map to a single pixel but it's a weighted sum of all pixels. This amazing graph shows how the first and second coefficient is calculated, using weights which are unique for each index.
>
> 
>
> Source: https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
>
> You can also try to [visualize the DCT by looking at a simple image](/dct_better_explained.ipynb) formation over the DCT basis. For instance, here's the [A character being formed](https://en.wikipedia.org/wiki/Discrete_cosine_transform#Example_of_IDCT) using each coefficient weight.
>
> 
> ### Hands-on: throwing away different coefficients
> You can play around with the [DCT transform](/uniform_quantization_experience.ipynb).
## 4th step - quantization
When we throw away some of the coefficients, in the last step (transform), we kinda did some form of quantization. This step is where we chose to lose information (the **lossy part**) or in simple terms, we'll **quantize coefficients to achieve compression**.
How can we quantize a block of coefficients? One simple method would be a uniform quantization, where we take a block, **divide it by a single value** (10) and round this value.
How can we **reverse** (re-quantize) this block of coefficients? We can do that by **multiplying the same value** (10) we divide it first.
This **approach isn't the best** because it doesn't take into account the importance of each coefficient, we could use a **matrix of quantizers** instead of a single value, this matrix can exploit the property of the DCT, quantizing most the bottom right and less the upper left, the [JPEG uses a similar approach](https://www.hdm-stuttgart.de/~maucher/Python/MMCodecs/html/jpegUpToQuant.html), you can check [source code to see this matrix](https://github.com/google/guetzli/blob/master/guetzli/jpeg_data.h#L40).
> ### Hands-on: quantization
> You can play around with the [quantization](/dct_experiences.ipynb).
## 5th step - entropy coding
After we quantized the data (image blocks/slices/frames) we still can compress it in a lossless way. There are many ways (algorithms) to compress data. We're going to briefly experience some of them, for a deeper understanding you can read the amazing book [Understanding Compression: Data Compression for Modern Developers](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/).
### VLC coding:
Let's suppose we have a stream of the symbols: **a**, **e**, **r** and **t** and their probability (from 0 to 1) is represented by this table.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probability | 0.3 | 0.3 | 0.2 | 0.2 |
We can assign unique binary codes (preferable small) to the most probable and bigger codes to the least probable ones.
| | a | e | r | t |
|-------------|-----|-----|------|-----|
| probability | 0.3 | 0.3 | 0.2 | 0.2 |
| binary code | 0 | 10 | 110 | 1110 |
Let's compress the stream **eat**, assuming we would spend 8 bits for each symbol, we would spend **24 bits** without any compression. But in case we replace each symbol for its code we can save space.
The first step is to encode the symbol **e** which is `10` and the second symbol is **a** which is added (not in a mathematical way) `[10][0]` and finally the third symbol **t** which makes our final compressed bitstream to be `[10][0][1110]` or `1001110` which only requires **7 bits** (3.4 times less space than the original).
Notice that each code must be a unique prefixed code [Huffman can help you to find these numbers](https://en.wikipedia.org/wiki/Huffman_coding). Though it has some issues there are [video codecs that still offers](https://en.wikipedia.org/wiki/Context-adaptive_variable-length_coding) this method and it's the algorithm for many applications which requires compression.
Both encoder and decoder **must know** the symbol table with its code, therefore, you need to send the table too.
### Arithmetic coding:
Let's suppose we have a stream of the symbols: **a**, **e**, **r**, **s** and **t** and their probability is represented by this table.
| | a | e | r | s | t |
|-------------|-----|-----|------|------|-----|
| probability | 0.3 | 0.3 | 0.15 | 0.05 | 0.2 |
With this table in mind, we can build ranges containing all the possible symbols sorted by the most frequents.
Now let's encode the stream **eat**, we pick the first symbol **e** which is located within the subrange **0.3 to 0.6** (but not included) and we take this subrange and split it again using the same proportions used before but within this new range.
Let's continue to encode our stream **eat**, now we take the second symbol **a** which is within the new subrange **0.3 to 0.39** and then we take our last symbol **t** and we do the same process again and we get the last subrange **0.354 to 0.372**.
We just need to pick a number within the last subrange **0.354 to 0.372**, let's choose **0.36** but we could choose any number within this subrange. With **only** this number we'll be able to recover our original stream **eat**. If you think about it, it's like if we were drawing a line within ranges of ranges to encode our stream.
The **reverse process** (A.K.A. decoding) is equally easy, with our number **0.36** and our original range we can run the same process but now using this number to reveal the stream encoded behind this number.
With the first range, we notice that our number fits at the slice, therefore, it's our first symbol, now we split this subrange again, doing the same process as before, and we'll notice that **0.36** fits the symbol **a** and after we repeat the process we came to the last symbol **t** (forming our original encoded stream *eat*).
Both encoder and decoder **must know** the symbol probability table, therefore you need to send the table.
Pretty neat, isn't it? People are damn smart to come up with a such solution, some [video codecs use](https://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding) this technique (or at least offer it as an option).
The idea is to lossless compress the quantized bitstream, for sure this article is missing tons of details, reasons, trade-offs and etc. But [you should learn more](https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/) as a developer. Newer codecs are trying to use different [entropy coding algorithms like ANS.](https://en.wikipedia.org/wiki/Asymmetric_Numeral_Systems)
> ### Hands-on: CABAC vs CAVLC
> You can [generate two streams, one with CABAC and other with CAVLC](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#cabac-vs-cavlc) and **compare the time** it took to generate each of them as well as **the final size**.
## 6th step - bitstream format
After we did all these steps we need to **pack the compressed frames and context to these steps**. We need to explicitly inform to the decoder about **the decisions taken by the encoder**, such as bit depth, color space, resolution, predictions info (motion vectors, intra prediction direction), profile, level, frame rate, frame type, frame number and much more.
We're going to study, superficially, the H.264 bitstream. Our first step is to [generate a minimal H.264 * bitstream](/encoding_pratical_examples.md#generate-a-single-frame-h264-bitstream), we can do that using our own repository and [ffmpeg](http://ffmpeg.org/).
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
```
> * ffmpeg adds, by default, all the encoding parameter as a **SEI NAL**, soon we'll define what is a NAL.
This command will generate a raw h264 bitstream with a **single frame**, 64x64, with color space yuv420 and using the following image as the frame.
> 
### H.264 bitstream
The AVC (H.264) standard defines that the information will be sent in **macro frames** (in the network sense), called **[NAL](https://en.wikipedia.org/wiki/Network_Abstraction_Layer)** (Network Abstraction Layer). The main goal of the NAL is the provision of a "network-friendly" video representation, this standard must work on TVs (stream based), the Internet (packet based) among others.
There is a **[synchronization marker](https://en.wikipedia.org/wiki/Frame_synchronization)** to define the boundaries of the NAL's units. Each synchronization marker holds a value of `0x00 0x00 0x01` except to the very first one which is `0x00 0x00 0x00 0x01`. If we run the **hexdump** on the generated h264 bitstream, we can identify at least three NALs in the beginning of the file.
As we said before, the decoder needs to know not only the picture data but also the details of the video, frame, colors, used parameters, and others. The **first byte** of each NAL defines its category and **type**.
| NAL type id | Description |
|--- |---|
| 0 | Undefined |
| 1 | Coded slice of a non-IDR picture |
| 2 | Coded slice data partition A |
| 3 | Coded slice data partition B |
| 4 | Coded slice data partition C |
| 5 | **IDR** Coded slice of an IDR picture |
| 6 | **SEI** Supplemental enhancement information |
| 7 | **SPS** Sequence parameter set |
| 8 | **PPS** Picture parameter set |
| 9 | Access unit delimiter |
| 10 | End of sequence |
| 11 | End of stream |
| ... | ... |
Usually, the first NAL of a bitstream is a **SPS**, this type of NAL is responsible for informing the general encoding variables like **profile**, **level**, **resolution** and others.
If we skip the first synchronization marker we can decode the **first byte** to know what **type of NAL** is the first one.
For instance the first byte after the synchronization marker is `01100111`, where the first bit (`0`) is to the field **forbidden_zero_bit**, the next 2 bits (`11`) tell us the field **nal_ref_idc** which indicates whether this NAL is a reference field or not and the rest 5 bits (`00111`) inform us the field **nal_unit_type**, in this case, it's a **SPS** (7) NAL unit.
The second byte (`binary=01100100, hex=0x64, dec=100`) of an SPS NAL is the field **profile_idc** which shows the profile that the encoder has used, in this case, we used the **[high profile](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC#Profiles)**. Also the third byte contains several flags which determine the exact profile (like constrained or progressive). But in our case the third byte is 0x00 and therefore the encoder has used just high profile.
When we read the H.264 bitstream spec for an SPS NAL we'll find many values for the **parameter name**, **category** and a **description**, for instance, let's look at `pic_width_in_mbs_minus_1` and `pic_height_in_map_units_minus_1` fields.
| Parameter name | Category | Description |
|--- |---|---|
| pic_width_in_mbs_minus_1 | 0 | ue(v) |
| pic_height_in_map_units_minus_1 | 0 | ue(v) |
> **ue(v)**: unsigned integer [Exp-Golomb-coded](https://ghostarchive.org/archive/JBwdI)
If we do some math with the value of these fields we will end up with the **resolution**. We can represent a `1920 x 1080` using a `pic_width_in_mbs_minus_1` with the value of `119 ( (119 + 1) * macroblock_size = 120 * 16 = 1920) `, again saving space, instead of encode `1920` we did it with `119`.
If we continue to examine our created video with a binary view (ex: `xxd -b -c 11 v/minimal_yuv420.h264`), we can skip to the last NAL which is the frame itself.
We can see its first 6 bytes values: `01100101 10001000 10000100 00000000 00100001 11111111`. As we already know the first byte tell us about what type of NAL it is, in this case, (`00101`) it's an **IDR Slice (5)** and we can further inspect it:
Using the spec info we can decode what type of slice (**slice_type**), the frame number (**frame_num**) among others important fields.
In order to get the values of some fields (`ue(v), me(v), se(v) or te(v)`) we need to decode it using a special decoder called [Exponential-Golomb](https://ghostarchive.org/archive/JBwdI), this method is **very efficient to encode variable values**, mostly when there are many default values.
> The values of **slice_type** and **frame_num** of this video are 7 (I slice) and 0 (the first frame).
We can see the **bitstream as a protocol** and if you want or need to learn more about this bitstream please refer to the [ITU H.264 spec.]( http://www.itu.int/rec/T-REC-H.264-201610-I) Here's a macro diagram which shows where the picture data (compressed YUV) resides.
We can explore others bitstreams like the [VP9 bitstream](https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf), [H.265 (HEVC)](http://handle.itu.int/11.1002/1000/11885-en?locatt=format:pdf) or even our **new best friend** [**AV1** bitstream](https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
), [do they all look similar? No](http://www.gpac-licensing.com/2016/07/12/vp9-av1-bitstream-format/), but once you learned one you can easily get the others.
> ### Hands-on: Inspect the H.264 bitstream
> We can [generate a single frame video](https://github.com/leandromoreira/introduction_video_technology/blob/master/encoding_pratical_examples.md#generate-a-single-frame-video) and use [mediainfo](https://en.wikipedia.org/wiki/MediaInfo) to inspect its H.264 bitstream. In fact, you can even see the [source code that parses h264 (AVC)](https://github.com/MediaArea/MediaInfoLib/blob/master/Source/MediaInfo/Video/File_Avc.cpp) bitstream.
>
> 
>
> We can also use the [Intel Video Pro Analyzer](https://software.intel.com/en-us/intel-video-pro-analyzer) which is paid but there is a free trial version which limits you to only work with the first 10 frames but that's okay for learning purposes.
>
> 
## Review
We'll notice that many of the **modern codecs uses this same model we learned**. In fact, let's look at the Thor video codec block diagram, it contains all the steps we studied. The idea is that you now should be able to at least understand better the innovations and papers for the area.
Previously we had calculated that we needed [139GB of storage to keep a video file with one hour at 720p resolution and 30fps](#chroma-subsampling) if we use the techniques we learned here, like **inter and intra prediction, transform, quantization, entropy coding and other** we can achieve, assuming we are spending **0.031 bit per pixel**, the same perceivable quality video **requiring only 367.82MB vs 139GB** of store.
> We choose to use **0.031 bit per pixel** based on the example video provided here.
## How does H.265 achieve a better compression ratio than H.264?
Now that we know more about how codecs work, then it is easy to understand how new codecs are able to deliver higher resolutions with fewer bits.
We will compare AVC and HEVC, let's keep in mind that it is almost always a trade-off between more CPU cycles (complexity) and compression rate.
HEVC has bigger and more **partitions** (and **sub-partitions**) options than AVC, more **intra predictions directions/angles**, **improved entropy coding** and more, all these improvements made H.265 capable to compress 50% more than H.264.
# Online streaming
## General architecture
[TODO]
## Progressive download and adaptive streaming
[TODO]
## Content protection
We can use **a simple token system** to protect the content. The user without a token tries to request a video and the CDN forbids her or him while a user with a valid token can play the content, it works pretty similarly to most of the web authentication systems.
The sole use of this token system still allows a user to download a video and distribute it. Then the **DRM (digital rights management)** systems can be used to try to avoid this.
In real life production systems, people often use both techniques to provide authorization and authentication.
### DRM
#### Main systems
* FPS - [**FairPlay Streaming**](https://developer.apple.com/streaming/fps/)
* PR - [**PlayReady**](https://www.microsoft.com/playready/)
* WV - [**Widevine**](http://www.widevine.com/)
#### What?
DRM means [Digital rights management](https://sander.saares.eu/categories/drm-is-not-a-black-box/), it's a way **to provide copyright protection for digital media**, for instance, digital video and audio. Although it's used in many places [it's not universally accepted](https://en.wikipedia.org/wiki/Digital_rights_management#DRM-free_works).
#### Why?
Content creator (mostly studios) want to protect its intelectual property against copy to prevent unauthorized redistribution of digital media.
#### How?
We're going to describe an abstract and generic form of DRM in a very simplified way.
Given a **content C1** (i.e. an hls or dash video streaming), with a **player P1** (i.e. shaka-clappr, exo-player or ios) in a **device D1** (i.e. a smartphone, TV, tablet or desktop/notebook) using a **DRM system DRM1** (widevine, playready or FairPlay).
The content C1 is encrypted with a **symmetric-key K1** from the system DRM1, generating the **encrypted content C'1**.
The player P1, of a device D1, has two keys (asymmetric), a **private key PRK1** (this key is protected1 and only known by **D1**) and a **public key PUK1**.
> **1protected**: this protection can be **via hardware**, for instance, this key can be stored inside a special (read-only) chip that works like [a black-box](https://en.wikipedia.org/wiki/Black_box) to provide decryption, or **by software** (less safe), the DRM system provides means to know which type of protection a given device has.
When the **player P1 wants to play** the **content C'1**, it needs to deal with the **DRM system DRM1**, giving its public key **PUK1**. The DRM system DRM1 returns the **key K1 encrypted** with the client''s public key **PUK1**. In theory, this response is something that **only D1 is capable of decrypting**.
`K1P1D1 = enc(K1, PUK1)`
**P1** uses its DRM local system (it could be a [SOC](https://en.wikipedia.org/wiki/System_on_a_chip), a specialized hardware or software), this system is **able to decrypt** the content using its private key PRK1, it can decrypt **the symmetric-key K1 from the K1P1D1** and **play C'1**. At best case, the keys are not exposed through RAM.
```
K1 = dec(K1P1D1, PRK1)
P1.play(dec(C'1, K1))
```
# How to use jupyter
Make sure you have **docker installed** and just run `./s/start_jupyter.sh` and follow the instructions on the terminal.
# Conferences
* [DEMUXED](https://demuxed.com/) - you can [check the last 2 events presentations.](https://www.youtube.com/channel/UCIc_DkRxo9UgUSTvWVNCmpA).
# References
The richest content is here, it's where all the info we saw in this text was extracted, based or inspired by. You can deepen your knowledge with these amazing links, books, videos and etc.
Online Courses and Tutorials:
* https://www.coursera.org/learn/digital/
* https://people.xiph.org/~tterribe/pubs/lca2012/auckland/intro_to_video1.pdf
* https://xiph.org/video/vid1.shtml
* https://xiph.org/video/vid2.shtml
* https://wiki.multimedia.cx
* https://mahanstreamer.net
* http://slhck.info/ffmpeg-encoding-course
* http://www.cambridgeincolour.com/tutorials/camera-sensors.htm
* http://www.slideshare.net/vcodex/a-short-history-of-video-coding
* http://www.slideshare.net/vcodex/introduction-to-video-compression-13394338
* https://developer.android.com/guide/topics/media/media-formats.html
* http://www.slideshare.net/MadhawaKasun/audio-compression-23398426
* http://inst.eecs.berkeley.edu/~ee290t/sp04/lectures/02-Motion_Compensation_girod.pdf
Books:
* https://www.amazon.com/Understanding-Compression-Data-Modern-Developers/dp/1491961538/ref=sr_1_1?s=books&ie=UTF8&qid=1486395327&sr=1-1
* https://www.amazon.com/H-264-Advanced-Video-Compression-Standard/dp/0470516925
* https://www.amazon.com/High-Efficiency-Video-Coding-HEVC/dp/3319068946
* https://www.amazon.com/Practical-Guide-Video-Audio-Compression/dp/0240806301/ref=sr_1_3?s=books&ie=UTF8&qid=1486396914&sr=1-3&keywords=A+PRACTICAL+GUIDE+TO+VIDEO+AUDIO
* https://www.amazon.com/Video-Encoding-Numbers-Eliminate-Guesswork/dp/0998453005/ref=sr_1_1?s=books&ie=UTF8&qid=1486396940&sr=1-1&keywords=jan+ozer
Onboarding material:
* https://github.com/Eyevinn/streaming-onboarding
* https://howvideo.works/
* https://www.aws.training/Details/eLearning?id=17775
* https://www.aws.training/Details/eLearning?id=17887
* https://www.aws.training/Details/Video?id=24750
Bitstream Specifications:
* http://www.itu.int/rec/T-REC-H.264-201610-I
* http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=12904&lang=en
* https://storage.googleapis.com/downloads.webmproject.org/docs/vp9/vp9-bitstream-specification-v0.6-20160331-draft.pdf
* http://iphome.hhi.de/wiegand/assets/pdfs/2012_12_IEEE-HEVC-Overview.pdf
* http://phenix.int-evry.fr/jct/doc_end_user/current_document.php?id=7243
* http://gentlelogic.blogspot.com.br/2011/11/exploring-h264-part-2-h264-bitstream.html
* https://forum.doom9.org/showthread.php?t=167081
* https://forum.doom9.org/showthread.php?t=168947
Software:
* https://ffmpeg.org/
* https://ffmpeg.org/ffmpeg-all.html
* https://ffmpeg.org/ffprobe.html
* https://mediaarea.net/en/MediaInfo
* https://www.jongbel.com/
* https://trac.ffmpeg.org/wiki/
* https://software.intel.com/en-us/intel-video-pro-analyzer
* https://medium.com/@mbebenita/av1-bitstream-analyzer-d25f1c27072b#.d5a89oxz8
Non-ITU Codecs:
* https://aomedia.googlesource.com/
* https://github.com/webmproject/libvpx/tree/master/vp9
* https://ghostarchive.org/archive/0W0d8 (was: https://people.xiph.org/~xiphmont/demo/daala/demo1.shtml)
* https://people.xiph.org/~jm/daala/revisiting/
* https://www.youtube.com/watch?v=lzPaldsmJbk
* https://fosdem.org/2017/schedule/event/om_av1/
* https://jmvalin.ca/papers/AV1_tools.pdf
Encoding Concepts:
* http://x265.org/hevc-h265/
* http://slhck.info/video/2017/03/01/rate-control.html
* http://slhck.info/video/2017/02/24/vbr-settings.html
* http://slhck.info/video/2017/02/24/crf-guide.html
* https://arxiv.org/pdf/1702.00817v1.pdf
* https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors
* http://web.ece.ucdavis.edu/cerl/ReliableJPEG/Cung/jpeg.html
* http://www.adobe.com/devnet/adobe-media-server/articles/h264_encoding.html
* https://prezi.com/8m7thtvl4ywr/mp3-and-aac-explained/
* https://blogs.gnome.org/rbultje/2016/12/13/overview-of-the-vp9-video-codec/
* https://videoblerg.wordpress.com/2017/11/10/ffmpeg-and-how-to-use-it-wrong/
Video Sequences for Testing:
* http://bbb3d.renderfarming.net/download.html
* https://www.its.bldrdoc.gov/vqeg/video-datasets-and-organizations.aspx
Miscellaneous:
* https://github.com/Eyevinn/streaming-onboarding
* http://stackoverflow.com/a/24890903
* http://stackoverflow.com/questions/38094302/how-to-understand-header-of-h264
* http://techblog.netflix.com/2016/08/a-large-scale-comparison-of-x264-x265.html
* http://vanseodesign.com/web-design/color-luminance/
* http://www.biologymad.com/nervoussystem/eyenotes.htm
* http://www.compression.ru/video/codec_comparison/h264_2012/mpeg4_avc_h264_video_codecs_comparison.pdf
* https://web.archive.org/web/20100728070421/http://www.csc.villanova.edu/~rschumey/csc4800/dct.html (was: http://www.csc.villanova.edu/~rschumey/csc4800/dct.html)
* http://www.explainthatstuff.com/digitalcameras.html
* http://www.hkvstar.com
* http://www.hometheatersound.com/
* http://www.lighterra.com/papers/videoencodingh264/
* http://www.red.com/learn/red-101/video-chroma-subsampling
* http://www.slideshare.net/ManoharKuse/hevc-intra-coding
* http://www.slideshare.net/mwalendo/h264vs-hevc
* http://www.slideshare.net/rvarun7777/final-seminar-46117193
* http://www.springer.com/cda/content/document/cda_downloaddocument/9783642147029-c1.pdf
* http://www.streamingmedia.com/Articles/Editorial/Featured-Articles/A-Progress-Report-The-Alliance-for-Open-Media-and-the-AV1-Codec-110383.aspx
* http://www.streamingmediaglobal.com/Articles/ReadArticle.aspx?ArticleID=116505&PageNum=1
* http://yumichan.net/video-processing/video-compression/introduction-to-h264-nal-unit/
* https://cardinalpeak.com/blog/the-h-264-sequence-parameter-set/
* https://cardinalpeak.com/blog/worlds-smallest-h-264-encoder/
* https://codesequoia.wordpress.com/category/video/
* https://developer.apple.com/library/content/technotes/tn2224/_index.html
* https://en.wikibooks.org/wiki/MeGUI/x264_Settings
* https://en.wikipedia.org/wiki/Adaptive_bitrate_streaming
* https://en.wikipedia.org/wiki/AOMedia_Video_1
* https://en.wikipedia.org/wiki/Chroma_subsampling#/media/File:Colorcomp.jpg
* https://en.wikipedia.org/wiki/Cone_cell
* https://en.wikipedia.org/wiki/File:H.264_block_diagram_with_quality_score.jpg
* https://en.wikipedia.org/wiki/Inter_frame
* https://en.wikipedia.org/wiki/Intra-frame_coding
* https://en.wikipedia.org/wiki/Photoreceptor_cell
* https://en.wikipedia.org/wiki/Pixel_aspect_ratio
* https://en.wikipedia.org/wiki/Presentation_timestamp
* https://en.wikipedia.org/wiki/Rod_cell
* https://it.wikipedia.org/wiki/File:Pixel_geometry_01_Pengo.jpg
* https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/
* https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping
* https://softwaredevelopmentperestroika.wordpress.com/2014/02/11/image-processing-with-python-numpy-scipy-image-convolution/
* https://tools.ietf.org/html/draft-fuldseth-netvc-thor-03
* https://www.encoding.com/android/
* https://www.encoding.com/http-live-streaming-hls/
* https://web.archive.org/web/20150129171151/https://www.iem.thm.de/telekom-labor/zinke/mk/mpeg2beg/whatisit.htm
* https://www.lifewire.com/cmos-image-sensor-493271
* https://www.linkedin.com/pulse/brief-history-video-codecs-yoav-nativ
* https://www.linkedin.com/pulse/video-streaming-methodology-reema-majumdar
* https://www.vcodex.com/h264avc-intra-precition/
* https://www.youtube.com/watch?v=9vgtJJ2wwMA
* https://www.youtube.com/watch?v=LFXN9PiOGtY
* https://www.youtube.com/watch?v=Lto-ajuqW3w&list=PLzH6n4zXuckpKAj1_88VS-8Z6yn9zX_P6
* https://www.youtube.com/watch?v=LWxu4rkZBLw
* https://web.stanford.edu/class/ee398a/handouts/lectures/EE398a_MotionEstimation_2012.pdf
* https://sander.saares.eu/categories/drm-is-not-a-black-box/
================================================
FILE: clean_docker.sh
================================================
#!/bin/bash
EXITED=$(docker ps -q -f status=exited)
DANGLING=$(docker images -q -f "dangling=true")
DANGLING_VOLUME=$(docker volume ls -qf "dangling=true")
if [ "$1" == "--dry-run" ]; then
echo "==> Would stop containers:"
echo $EXITED
echo "==> And images:"
echo $DANGLING
else
if [ -n "$EXITED" ]; then
docker rm $EXITED
else
echo "No containers to remove."
fi
if [ -n "$DANGLING" ]; then
docker rmi $DANGLING
else
echo "No images to remove."
fi
if [ -n "$DANGLING_VOLUME" ]; then
docker volume rm $DANGLING_VOLUME
else
echo "No volumes to remove."
fi
fi
================================================
FILE: dct_better_explained.ipynb
================================================
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"import numpy as np\n",
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"from scipy import fftpack\n",
"from skimage.util import img_as_ubyte\n",
"import matplotlib.image as mpimg"
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"data": {
"image/png": 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FK0lJLFhJSrKeJxqsxYEAu3btqu0DB4MB/f5e17htzKRmm9RcYLZxTWq2Sc0F9WVb1GcH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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# loading a simple Z character image (8x8)\n",
"img = img_as_ubyte(mpimg.imread('i/z_char_8x8.png'))\n",
"img_h = 8\n",
"gray_img = img[:,:,0]\n",
"\n",
"plt.imshow(img, cmap='gray', interpolation='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All pixel's value"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[255, 255, 255, 255, 255, 255, 255, 255],\n",
" [255, 165, 0, 0, 0, 22, 255, 255],\n",
" [255, 252, 251, 221, 22, 88, 255, 255],\n",
" [255, 255, 250, 59, 28, 238, 255, 255],\n",
" [255, 255, 125, 13, 218, 255, 255, 255],\n",
" [255, 187, 0, 96, 218, 206, 255, 255],\n",
" [255, 171, 0, 0, 0, 0, 255, 255],\n",
" [255, 255, 255, 255, 255, 255, 255, 255]], dtype=uint8)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.set_printoptions(precision=1, linewidth=140, suppress=True)\n",
"gray_img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All coefficient's value"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1533.1, -57.3, 450.2, 49.7, -39.4, 21.9, -77.6, 14. ],\n",
" [ 13.6, 47.2, 7.7, -85.3, -37.5, 49.5, 25.4, -14.8],\n",
" [ 59.4, 14.5, -98.6, -45. , 94.5, 42.7, -46.3, -2.4],\n",
" [ -19. , -95.4, -21.9, 172. , 71.9, -98.6, -44.8, 27.3],\n",
" [ 293.4, 22.1, -200.3, 25.4, -98.1, -81.2, 122.9, 11.7],\n",
" [ -3.7, 45.9, 23.6, -88.7, -43.5, 59.6, 30.2, -22.1],\n",
" [ 251. , 30.1, -223.6, -30.1, 25.9, 6.6, 28.1, -41.6],\n",
" [ 23.3, 38.6, -12.8, -51.8, 5.7, 2.1, -22.9, 20.7]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# transform: 2D DCT\n",
"z_dct = fftpack.dct(fftpack.dct(gray_img.T, norm='ortho').T, norm='ortho')\n",
"\n",
"np.set_printoptions(precision=1, linewidth=140, suppress=True)\n",
"z_dct"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DCT basis"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RokWefvzxxz0d24TBVch5A0yssglvSuDNKqy5ijkQbpphIhWgi24j1g5X8Y5V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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dct_basis = img_as_ubyte(mpimg.imread('i/dct_basis.png'))\n",
"plt.imshow(dct_basis, cmap='gray', interpolation='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reconstructed images"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"partial_z_idct = []\n",
"partial_z_dct = []\n",
"\n",
"for ii in range(img_h*img_h):\n",
" dct_copy = np.copy(z_dct)\n",
" frequency_counter = 0 \n",
" \n",
" for u in range(img_h):\n",
" for v in range(img_h):\n",
" if frequency_counter > ii:\n",
" dct_copy[u,v] = 0\n",
" frequency_counter += 1\n",
" \n",
" partial_z_dct.append(dct_copy)\n",
" \n",
" partial_img_idct = fftpack.idct(fftpack.idct(dct_copy.T, norm='ortho').T, norm='ortho')\n",
" partial_z_idct.append(partial_img_idct)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"image/png": 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4h2xW7nlf0l34zYtsHm7MKTrY532lsqyK4G+8Teey3wF/kczDybZDk+23t+12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\nROcj8f4Ln9Q2+6KEa/GKO/fOdcwHbwNLtdifq8X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7nlv4zL1y2K10PlrVSd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\n+e7bcZsOiX9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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# shows each coefficient evolution towards the image formation\n",
"# here you can see how each DCT basis constributes to the image formation\n",
"# try to visualize them as a summation of the basis throughout the 64 elements \n",
"fig = plt.figure(figsize=(16, 16))\n",
"for ii in range(img_h*img_h):\n",
" plt.subplot(8, 8, ii + 1)\n",
" plt.title(partial_z_dct[ii][int(ii / 8), ii % 8])\n",
" plt.imshow(partial_z_idct[ii], cmap='gray', interpolation='nearest')\n",
" plt.grid(False);\n",
" plt.xticks([]);\n",
" plt.yticks([]);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: dct_experiences.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Image in spatial and frequency domain"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import fftpack\n",
"import matplotlib.image as mpimg"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# loading image\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"\n",
"choosen_y_x = 90\n",
"resolution = 128\n",
"\n",
"# 128 x 128 slice (power of 2)\n",
"img_slice = img[choosen_y_x:(choosen_y_x + resolution), choosen_y_x:(choosen_y_x + resolution), 2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DCT freq and Inversed DCT image"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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tfTSq3GqW5y3zkL9HMaMOK1cgEEA0GoVSCu12W5uH/abqxgErj1YegfHJox0E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\nAQAAAI2CEwAAANAo/wFLK7yxCRWWUAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# transform: 2D DCT\n",
"dct_slice = fftpack.dct(fftpack.dct(img_slice.T, norm='ortho').T, norm='ortho')\n",
"# inverse transform: 2D DCT\n",
"idct_slice = fftpack.idct(fftpack.idct(dct_slice.T, norm='ortho').T, norm='ortho')\n",
"\n",
"f, (plt1, plt2, plt3) = plt.subplots(1, 3)\n",
"\n",
"plt1.axis('off'); plt1.set_title('Zoomed DCT Freq'); plt1.imshow(dct_slice[0:20,0:20], cmap='gray', interpolation='nearest');\n",
"plt2.axis('off'); plt2.set_title('Inverse DCT Freq'); plt2.imshow(idct_slice, cmap='gray');\n",
"plt3.axis('off'); plt3.set_title('Original'); plt3.imshow(img_slice, cmap='gray');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Discarding 80% of the coefficients"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80% of the coefficients were discarded.\n"
]
},
{
"data": {
"image/png": 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VB6et/k/l5c1PPP/xXh6+/7BjKDMy3OcuJgrzUFBQUFBQ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\n6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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# copying dct\n",
"dct_slice_copy = np.copy(dct_slice)\n",
"\n",
"# only below the top left triangle to retain the lowest frequency\n",
"triangle = (50/100) * resolution\n",
"\n",
"# only discard greater than threshold\n",
"threshold = 0.0019\n",
"\n",
"discarded_coefficients = 0\n",
"\n",
"for x in range(resolution):\n",
" for y in range(resolution):\n",
" if ((x + y) > triangle) and (abs(dct_slice_copy[x, y]) > threshold):\n",
" dct_slice_copy[x, y] = 0\n",
" discarded_coefficients += 1\n",
"\n",
"# 2D inverse DCT \n",
"idct_slice = fftpack.idct(fftpack.idct(dct_slice_copy.T, norm='ortho').T, norm='ortho')\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"\n",
"plt1.axis('off'); plt1.set_title('Original'); plt1.imshow(img_slice, cmap='gray', interpolation='nearest')\n",
"plt2.axis('off'); plt2.set_title('Inverse Discarded DCT Freq'); plt2.imshow(idct_slice, cmap='gray', interpolation='nearest')\n",
"\n",
"\n",
"print(\"%d%% of the coefficients were discarded.\" % ((discarded_coefficients / (resolution*resolution)) * 100))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: encoding_pratical_examples.md
================================================
[](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)
# Introduction
Please make sure you run `./setup.sh` first.
# General commands
## Inspect stream
To see some details:
```
./s/mediainfo /files/v/small_bunny_1080p_30fps.mp4
```
To see full details:
```
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps.mp4
# I don't know why Details is not on man page
```
To see only the frame, slice types:
```
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps.mp4 | grep slice_type
```
## Transmuxing
From `mp4` to `ts`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 /files/v/small_bunny_1080p_30fps.ts
```
From `mp4` to `ts` explicitly telling to copy audio and video codec:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:a copy -c:v copy /files/v/small_bunny_1080p_30fps.ts
```
## Transcoding
From `h264` to `vp9`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libvpx-vp9 -c:a libvorbis /files/v/small_bunny_1080p_30fps_vp9.webm
```
From `h264` to `h265`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx265 /files/v/small_bunny_1080p_30fps_h265.mp4
```
From `h264` to `h264` with I-frame at each second (for a 30FPS video):
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 -c:a copy /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second.mp4
```
Count how many `I-slice` (keyframes) were inserted:
```
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second.mp4 | grep "slice_type I" | wc -l
```
## Split and merge smoothly
To work with smaller videos you can split the whole video into segments and you can also merge then after.
```
# spliting into several likely 2s segments
./s/ffmpeg -fflags +genpts -i /files/v/small_bunny_1080p_30fps.mp4 -map 0 -c copy -f segment -segment_format mp4 -segment_time 2 -segment_list video.ffcat -reset_timestamps 1 -v error chunk-%03d.mp4
# joining them
./s/ffmpeg -y -v error -i video.ffcat -map 0 -c copy output.mp4
```
## 1 I-Frames per second vs 0.5 I-Frames per second
From `h264` to `h264` with I-frame at each second (for a 30FPS video):
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 -c:a copy /files/v/small_bunny_1080p_30fps_h264_keyframe_each_one_second.mp4
```
From `h264` to `h264` with I-frame at each two seconds (for a 30FPS video):
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=60:min-keyint=60:no-scenecut=1 -c:a copy /files/v/small_bunny_1080p_30fps_h264_keyframe_each_two_seconds.mp4
```
## 1 I-frame and the rest P-Frames
Generates a video with a `single I frame` and the `rest are P frames`.
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=300:min-keyint=300:no-scenecut=1:bframes=0 -c:a copy /files/v/small_bunny_1080p_30fps_single_I_rest_P.mp4
```
You can check if that's true:
```
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps_single_I_rest_P.mp4 | grep "slice_type I" | wc -l
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps_single_I_rest_P.mp4 | grep "slice_type P" | wc -l
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps_single_I_rest_P.mp4 | grep "slice_type B" | wc -l
```
## No B-frames at all
Generates a video with 0 B-frames.
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1:bframes=0 -c:a copy /files/v/small_bunny_1080p_30fps_zero_b_frames.mp4
```
Check if that's right and also compare the size.
```
./s/mediainfo --Details /files/v/small_bunny_1080p_30fps_zero_b_frames.mp4 | grep "slice_type B" | wc -l
ls -lah v/
```
## CABAC vs CAVLC
Generates `h264` using CAVLC (faster, less cpu intensive, less compression):
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1:no-cabac=1 -c:a copy /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second_CAVLC.mp4
```
Generates `h264` using CABAC ("slower", more cpu intensive, more compression):
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1:coder=1 -c:a copy /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second_CABAC.mp4
```
## Transrating
CBR from `1928 kbps` to `964 kbps`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -b:v 964K -minrate 964K -maxrate 964K -bufsize 2000K /files/v/small_bunny_1080p_30fps_transrating_964.mp4
```
Constrained VBR or ABR from `1928 kbps` to `max=3856 kbps ,min=964 kbps`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -minrate 964K -maxrate 3856K -bufsize 2000K /files/v/small_bunny_1080p_30fps_transrating_964_3856.mp4
```
## Transsizing
From `1080p` to `480p`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -vf scale=480:-1 /files/v/small_bunny_1080p_30fps_transsizing_480.mp4
```
## Demuxing
Extracting `audio` from `container`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -vn -c:a copy /files/v/small_bunny_audio.aac
```
## Muxing
Joining `audio` with `video`:
```
./s/ffmpeg -i /files/v/small_bunny_audio.aac -i /files/v/small_bunny_1080p_30fps.mp4 /files/v/small_bunny_1080p_30fps_muxed.mp4
```
## Generates YUV histogram
It generates a video with color histogram as an overlay.
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -vf "split=2[a][b],[b]histogram,format=yuv420p[hh],[a][hh]overlay" /files/v/small_bunny_yuv_histogram.mp4
```
## Generate debug video
It generates a video with macro blocks debug over the video. Please refer to https://trac.ffmpeg.org/wiki/Debug/MacroblocksAndMotionVectors?version=7 to understand the meaning of each block color.
```
./s/ffmpeg -debug vis_mb_type -i /files/v/small_bunny_1080p_30fps.mp4 /files/v/small_bunny_1080p_30fps_vis_mb.mp4
```
It generates a video with motion vector over the video.
```
./s/ffmpeg -flags2 +export_mvs -i /files/v/small_bunny_1080p_30fps.mp4 -vf codecview=mv=pf+bf+bb /files/v/small_bunny_1080p_30fps_vis_mv.mp4
```
## Generate images from video
Get `images` from `1s video`:
```
./s/ffmpeg -y -i /files/v/small_bunny_1080p_30fps.mp4 -t 00:00:01 /files/v/smallest_bunny_1080p_30fps_%3d.jpg
```
## Generate video from images
```
# from one image
./s/ffmpeg -loop 1 -i /files/v/smallest_bunny_1080p_30fps_001.jpg -c:v libx264 -pix_fmt yuv420p -t 10 /files/v/smallest_bunny_1080p_30fps_frame_001.mp4
# from multiple images (repeating 10s)
./s/ffmpeg -loop 1 -i /files/v/smallest_bunny_1080p_30fps_%03d.jpg -c:v libx264 -pix_fmt yuv420p -t 10 /files/v/smallest_bunny_1080p_30fps_from_images.mp4
```
## Generate a single frame video
It generates a single frame video which is great for learning and analysis.
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.mp4
./s/ffmpeg -i /files/i/minimal.png /files/v/minimal_yuv444.mp4
# we can inspect h264 bitstream
./s/mediainfo --Details /files/v/minimal_yuv420.mp4 | less
```
## Generate a simple video
It generates a video from a sequence of images.
```
# 2 simple images with white background
./s/ffmpeg -i /files/i/solid_background_ball_%d.png -pix_fmt yuv420p /files/v/solid_background_ball_yuv420.mp4
# 4 simple images with background
./s/ffmpeg -i /files/i/smw_background_ball_%d.png -pix_fmt yuv420p /files/v/smw_background_ball_yuv420.mp4
# 4 white images as a video (great to test predicitons)
for i in {1..4}; do cp i/solid_background.png i/solid_background_$i.png; done
./s/ffmpeg -i /files/i/solid_background_%d.png -pix_fmt yuv420p /files/v/solid_background_yuv420.mp4
```
## Generate a single frame h264 bitstream
```
./s/ffmpeg -i /files/i/minimal.png -pix_fmt yuv420p /files/v/minimal_yuv420.h264
# you can check the raw h264 bit stream
hexdump v/minimal_yuv420.h264
```
## Audio sampling
From `original` to `8kHz`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -ar 8000 /files/v/small_bunny_1080p_30fps_8khz.mp4
```
## Audio bit depth
From `original` to `8 bits`:
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps.mp4 -sample_fmt:0:1 u8p /files/v/small_bunny_1080p_30fps_8bits.mp4 -y
```
> Technically speaking, bit depth is only meaningful when applied to pure PCM devices. Non-PCM formats, such as lossy compression systems like MP3, have bit depths that are not defined in the same sense as PCM. In lossy audio compression, where bits are allocated to other types of information, the bits actually allocated to individual samples are allowed to fluctuate within the constraints imposed by the allocation algorithm.
## Adaptive bitrate streaming
[HLS](https://tools.ietf.org/html/draft-pantos-http-live-streaming-20) streaming:
### A VOD stream with 1s chunk size
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second.mp4 -c:a copy -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 -hls_playlist_type vod -hls_time 1 /files/v/playlist_keyframe_each_second.m3u8
```
### Playlists for 720p(2628kbs), 480p(480p1128kbs) and 240p(264kbs) streams
```
./s/ffmpeg -i /files/v/small_bunny_1080p_30fps_h264_keyframe_each_second.mp4 \
-c:a copy -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 \
-b:v 2500k -s 1280x720 -profile:v high -hls_time 1 -hls_playlist_type vod /files/v/720p2628kbs.m3u8 \
-c:a copy -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 \
-b:v 1000k -s 854x480 -profile:v high -hls_time 1 -hls_playlist_type vod /files/v/480p1128kbs.m3u8 \
-c:a copy -c:v libx264 -x264-params keyint=30:min-keyint=30:no-scenecut=1 \
-b:v 200k -s 426x240 -profile:v high -hls_time 1 -hls_playlist_type vod /files/v/240p264kbs.m3u8
```
### The variant playlist
```
cat < v/variant.m3u8
#EXTM3U
#EXT-X-VERSION:6
#EXT-X-STREAM-INF:PROGRAM-ID=1,BANDWIDTH=2500000,CODECS="avc1.640028,mp4a.40.2",RESOLUTION=1280x720
720p2628kbs.m3u8
#EXT-X-STREAM-INF:PROGRAM-ID=1,BANDWIDTH=1000000,CODECS="avc1.4d001f,mp4a.40.2",RESOLUTION=854x480
480p1128kbs.m3u8
#EXT-X-STREAM-INF:PROGRAM-ID=1,BANDWIDTH=200000,CODECS="avc1.42001f,mp4a.40.2",RESOLUTION=426x240
240p264kbs.m3u8
EOF
```
## Video quality perception
You can learn more about [vmaf](http://techblog.netflix.com/2016/06/toward-practical-perceptual-video.html) and [general video quality perception](https://leandromoreira.com.br/2016/10/09/how-to-measure-video-quality-perception/).
```
# generating a 2 seconds example video
./s/ffmpeg -y -i /files/v/bunny_1080p_30fps.mp4 -ss 00:01:24 -t 00:00:02 /files/v/smallest_bunny_1080p_30fps.mp4
# generate a transcoded video (600kbps vp9)
./s/ffmpeg -i /files/v/smallest_bunny_1080p_30fps.mp4 -c:v libvpx-vp9 -b:v 600K -c:a libvorbis /files/v/smallest_bunny_1080p_30fps_vp9.webm
# extract the yuv (yuv420p) color space from them
./s/ffmpeg -i /files/v/smallest_bunny_1080p_30fps.mp4 -c:v rawvideo -pix_fmt yuv420p /files/v/smallest_bunny_1080p_30fps.yuv
./s/ffmpeg -i /files/v/smallest_bunny_1080p_30fps_vp9.webm -c:v rawvideo -pix_fmt yuv420p /files/v/smallest_bunny_1080p_30fps_vp9.yuv
# run vmaf original h264 vs transcoded vp9
./s/vmaf run_vmaf yuv420p 1080 720 /files/v/smallest_bunny_1080p_30fps.yuv /files/v/smallest_bunny_1080p_30fps_vp9.yuv --out-fmt json
```
## FFMpeg as a library
There are some documentations, examples and tutorials:
* http://dranger.com/ffmpeg/tutorial01.html
* https://github.com/leandromoreira/player-ffmpeg
* https://github.com/FFmpeg/FFmpeg/tree/master/doc/examples
* https://www.ffmpeg.org/doxygen/3.2/index.html
================================================
FILE: filters_are_easy.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Filters are easy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np\n",
"from scipy.ndimage import convolve"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pojBEZ+F4e0L1pa9y1p9/hN1HDoNRxr5F1WGTDJEBFYFQWs7+9oMc+dTnGZ+9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1f47vIiufHPBzEI5Br5KW7DNwlsnv+bZ4vMvBOAPe/FopGNPU9sTqY4c4pWenzlGvrc4jFuca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\nGyUuMQGCy3NonY12yIkYgiO5UySQztNppdCmGTBbG6O3hj7OICiTZULBw6m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ZeloxVJrd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\nZSpTmcoEy/8HcrO89MWvFXAAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# loading image\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Tl7Qh6payKh2qKoicpJJqo4zIXpjHRqx9fm+wa8RfO+ZrUtbrgh3zWHahpZLmqSMiOynu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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# cutting the image\n",
"eye_brow = img[100:150,150:200,]\n",
"plt.imshow(eye_brow,interpolation='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kernels"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"kernel_edge_detect3 = np.array([[-1.,-1.,-1.],\n",
" [-1.,8.,-1.],\n",
" [-1.,-1.,-1.]])\n",
"\n",
"kernel_sharpen2 = np.array([[-1.,-1.,-1.],\n",
" [-1.,9.,-1.],\n",
" [-1.,-1.,-1.]])\n",
"\n",
"kernel_blur = np.array([[1.,1.,1.],\n",
" [1.,1.,1.],\n",
" [1.,1.,1.]])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img_edge_detect = convolve(eye_brow[:,:,0], kernel_edge_detect3)\n",
"img_sharpen = convolve(eye_brow[:,:,0], kernel_sharpen2)\n",
"img_blur = convolve(eye_brow[:,:,0], kernel_blur)\n",
"\n",
"# creates sub plots of 15x15\n",
"f, (plt1, plt2, plt3, plt4) = plt.subplots(1, 4,figsize=(15,15))\n",
"\n",
"plt1.set_title('Original');plt1.imshow(eye_brow[:,:,0],cmap='gray', interpolation='nearest');\n",
"# showing each channel img[x,y,color_plane] \n",
"plt2.axis('off');plt2.set_title('Edge detect');plt2.imshow(img_edge_detect,cmap='gray', interpolation='nearest');\n",
"plt3.axis('off');plt3.set_title('Sharpen');plt3.imshow(img_sharpen,cmap='gray', interpolation='nearest');\n",
"plt4.axis('off');plt4.set_title('Blur');plt4.imshow(img_blur,cmap='gray', interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolution it's a simple operation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![convolution explanation](i/kernel_convolution.jpg \"convolution explanation\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: https://developer.apple.com/library/content/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# You can apply filters using [convolution even with **ffmpeg**](https://ffmpeg.org/ffmpeg-filters.html#convolution)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: frame_difference_vs_motion_estimation_plus_residual.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Frame difference vs motion estimation + residual"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9gE8CK2u2afd7slAl5H3ZOQ/m/WbM+4LzPqVU6g24Dfh6zf0AHgJOK6j/54G3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\nsvkNMzrpBmYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# loading the frames\n",
"frame_0 = mpimg.imread('i/smw_background_ball_1.png')\n",
"frame_1 = mpimg.imread('i/smw_background_ball_2.png')\n",
"# showing the frames\n",
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"plt1.set_title('frame_0');plt1.imshow(frame_0,interpolation='nearest');\n",
"plt2.set_title('frame_1');plt2.imshow(frame_1,interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frame difference"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(np.subtract(frame_0[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# the block we want to motion estimate\n",
"ball = frame_0[27:37,1:11,]\n",
"\n",
"block_size = 10\n",
"plt.imshow(ball,interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Motion estimation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# let's pretend we had motion estimation (full scan, reduced scan(lfp)...) \n",
"# and that the motion vectors were:\n",
"x = 26 \n",
"y = 10\n",
"predicted_frame = np.array(frame_1)\n",
"\n",
"# applying the motion compensation\n",
"predicted_frame[x:(x+block_size),y:(y+block_size),] = ball\n",
"\n",
"plt.imshow(predicted_frame,interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frame difference vs motion estimation + residual"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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0JPAGYqJnbQmsCSEsy7QvAqZVHp6ZNZpz3qw1VFTwJW1NPF43M4TwYiVPBcJw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\nQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"plt1.set_title('Frame difference');plt1.imshow(np.subtract(frame_0[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);\n",
"plt2.set_title('Motion estimation');plt2.imshow(np.subtract(predicted_frame[:,:,0], frame_1[:,:,0]),interpolation='nearest',cmap=plt.cm.binary);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: image_as_3d_array.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Image as a 3D (RBG) array"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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D/exXB1prtNa84AUv4MYbb+SKK65Y6SFZLBaLJcEKZRbLqceJmiOvWIF/rfXHgI8dzXOC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\ncbKIyta5kQL84i6QFKRCoUCj0cBxHN7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426WAZMLKcJPNYaFrYUm5zE5Ynltzc3Ps3LmT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\nkA/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/m4JlvoFEDhqxuA4PD/Hs2TMcHBycevjTA5Q7W1lGmX+MLHo3GXhKKezu7uLJkyd4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33xi1/kmWee4dOf/nRhUPwixTeYtbOmDTUdftnv93n++eeLCpFlBot+xXHMW2+9xV/8xV9w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\nZsVd0+JYlPPEJXIUE8OPdSHnF92ahHEkzDnp26IrjA8kKKWcm4co9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fb9IfMvrY1JcN7+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\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5bLtt3/ve9/joRz/Kgw8+aFgkr0eUikJe7BUzkaRh4/s+l19+OR/5yEdYuXIlS5cu5eMf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M2B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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# loading image\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"# creates sub plots of 15x15\n",
"f, (plt1, plt2, plt3, plt4) = plt.subplots(1, 4, figsize=(15, 15))\n",
"\n",
"plt1.set_title('Original'); plt1.imshow(img);\n",
"# showing each channel img[x,y,color_plane] \n",
"plt2.axis('off'); plt2.set_title('Red'); plt2.imshow(img[:,:,0], cmap='gray');\n",
"plt3.axis('off'); plt3.set_title('Luigi'); plt3.imshow(img[:,:,1], cmap='gray');\n",
"plt4.axis('off'); plt4.set_title('Blue'); plt4.imshow(img[:,:,2], cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QX4D8n92e0QZkX/3VrPzcz6KHgxSObW3CxtpOFdl0BqadUmls+3WTmvJHlaZc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\ny+Ppjp3lzix4AHZk6u88wK4A9l0NEEK6s4xj13pCvHO7jz5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FMOuHInL5bcfGyT2WpsDi\nELZq0ZRJNoX3oyWfzJjbHS4F0KIWoTlB05BzRsYRmXWkYSCnhFw+IncNzeE+c3G43iNDgKaD1pX5\nQixZYqszCJ7sPWPfQ+OIRngaiCgblKUBtZa1NUTXoCJgDNkIKpAaN4ligMhkBlMUe1vEVJm6TUJx\niGnGIKiYImzpNiC5hNorWgKcpwNkBCNKVrndCDRNHUBVyvUXZApKLhKboGQRsuo017Q2I1PQdbGS\n5ayIAbR0yDRlwUDZVzC3U9azFpeaulLiOuTSyMC1hlVKnOVEKyXvbFCIUvLVPIlDa9ig9GnN1dPA\n3LbszfdpW8vB3py3P/Qw5+PA05sVT69K44M9Y/E5sW8b1prIGeaa2RhDDCNOBNM2DDlhckJdw5gi\njRrUOAIKMdC2HZGMJsXipqYGAaOuZJIZS8oJ8R4379AUiePAcHZOt1zy0H0PEI9vvojfoVceztwZ\nZL5bg9w1uCNgfFeg/s7c/d1C585w9F2h5Xf5mwnZ0ZlAd4Td7wrKv+tl78jO37ndC2i7Od9d2Lqm\nXUH0dx5Pc7hjLIR0x9hv//H1O8Zubu7cTmbNHWN2x8S7QuN39RXY2REB7vqC77q26S6vza5GB2ZH\nM4a7DeTfxa712Rd43ncF+u9ezw52fVV2bGZ2dTX4M8CuZ/pzzTDGF30O4E/U3OSz5X2n8xd9DoCf\nf2Z40efIZ0+96HMcH39+rtetcO3FnySuXvw5AKR90af4q9/3ok9RqVQqL3vuGZHs4KVewIQAR1/6\ndhg2Jax/uSxdLYehiF0pTaH8286Vkwi2/ae1TKH9dupumaeAfDMJQdYAgnqPoOAa0nqNuoaoipyd\nI2IwVw+R1z/KfgRzuixCHQKzeZkqeDg9hhhJ3qOS8cBNHxmAlCKLDOvOsTJCtJYopriwbHmpL/+m\nnNK+phcQlVLCqJT31q0TDJleRnLGWFPErmm/jGA0F+MXoKIXGWIibN+At+84xceUi5imert5gWxV\nMy1CokzdI8v0Jc8M1WmO4iTLzzumMnXO07LurWimk4NtWwIqKCJC0q3A5th2JvUo1hZRsEkJh6Ih\nETSXiDmFxhmGpPSA4DlLnv2Vp7MN7brn0pUj7p/NuG9vjy+4cpUnFuc8fXwLFcN5ilhVZs7R58y+\nMcW5poqLsZSyGgNZaY0hIKQcaG2DWEeMEeuakm+mmRQDtm3JAjkGrICxLQqkmBAbyN4TNj3j2Qn3\nv/Wt+N/89c/V16VSqVQqlUqlUqlUKpV7hntGJHvpyy3LCg5/6Idwj74Gfu934fpzcPNGCexfropY\nlvIklD3PRQbP+3X/JIhpKk6yFEAt6n1xk4VINqY4m1RR78nrgfTwIfbxR2mu3Y9ZDXD9GJ45L8KO\nM0W0i5G0XrEZR1JrOfWJ2Dmey4nzWccaMF2Dtg0ZIU+OL3nenynlIlzJ1uk1iVLbUDDNqF6IXOWM\nlDydnp0cW8YUcQtARElT6aROe6Vt2SRSMtamS6zoZ5RQqubiNJvKMCVnEDOtjdsBZoKgWS/WyZSD\npnqxzm2JZi4h96oZ0Un0+wxnXBH+spHiLptum+aMtfb22mPblKrWTksjA82QE0/HTOcMrjF4zQw+\nMc8elzwprblysiKEyKHMaK3hoaNLPPbo45iDGTdWK54+O+XmYoEBYipuPqOZiEHF4jUxF2EQIadI\nK5bIJMpiyVqaPGhWnGnIwZONYF1TOqVmJWvAhkCyhrzpyeYEPu0Y/tJ30/7hHxKee4ZaclmpVCqV\nSqVSqVQqlVcS94xI9nLAPvYY3ZvfVLLINpMotumLi8z74h6L8cJFNpUJXgRzUQQ0KU4gJE8dJdPt\nTpc5K5pDcU5ZR+oHzNd+ObOHHsCECE/dhFtnEDNED4szAPxyiXQNi5S4YeDYJ9YGfOMYm4bYtaUk\n0ZSyRzWmiEdT2P5WsopQHGCTwGSl5Ijp5LC6Hbg/ucRUc9ln0p+SFlEsX0TtT9tPIlVpS4lMzjko\npZVF6NJJlCu7qkxzMDnJ2JZMlvJK0Wl8K3BNRZRZcylL2jYFuK2wTuWrU8FmOc18+9y5XQ6mkyNN\nMCLFMTcJiUkp4mZOiJZrlSi3T8Ug1pJtEQWtKkOM2AbCmDhqLSkrC81sVAnaoxluHfdcPm556LWP\n8vD+IdcuX+Hps1OeOD7mfLNhj9LwYQCaGOisYU1mHhVjDF6Kg681DYFMymBTxs7mxBxxCiKmNFYI\noXQebdvysw9Y58ghEfqeYT5j9mVfBr/8DFtPX6VSqVQqlUqlUqlUKq8Eqkh2VxQ3UveOd+AeuB9u\n3ixh/etVCcQfxpL9FadQfqUIQM8XyLbC2bbMsmsgRDQFJETUlDJL0VSEH2cJpye4t7+Z5rWPwBPP\nwjO3SuaZFRg2jNefxVOmewbQFFk5y82uY5WUbA2paRFjivhkJsHndvh9yfuSrQNLBGuKsyuLYm47\nuYqDS01xa2FKaaVMOWGiJSzf6u24/YuuldsyyUn7kjy5trJOwfnTHArJbJ1ictslVtZSksey6qQ7\nFhVNJ+Ex69YlVlxhyIVTbSuWfcacQN5mv4lBNJeq1637bJtYJkLSbWZauQ45Z4xO+WhTKem2LNQY\nQVO+7WxDDOqETMYYy9JZog+0OZXOlUAHRMmYTvHPPMu8m3F45QqPHF3m2tWr/Nb7388qlW6Y86Yh\nWcHnzJ5xjKLknGiNxVpHnyOdadCpK6jxA7Zp8ClgNWPaBuMsMUYEcG1X8oNCgBTI48iwWBLf8WWY\n3/h18tT1tFKpVCqVSqVSqVQqlVcCVSS7S8xsRveOtxdFZrOG1WpykfkikHl/kUGWtplkxSFFnpxi\nk1vstmCWMpoT6iyIIfU90nbE1QpzOGf2r349PPQA/PM/gPMloLBao2EgjCOr1nLuE+cObkYYZx2D\nNaS2nRxjFx0msxiS6oUrS8EYweTSdRKKQFWC8UtWWp6ULUUvBLLb2V2QJsfWVtAqYlYpS9w6wGRy\nrm0tXc/zoZXSxq0oJaU61UzKWZqcY2wv4RS+L9v16JSWNu1fOlsWEdBM61G2Fa+CaC7ClhYBzxgh\nZMVxcZ7peZlmmYxRQaZsr+e7qraloEXQAzXFvaZYzBRiLjmXhgcIWWzpLomAsxgsSCJqRpylDbE0\nAghLhrDkaHXO/v4BV+9/gHe89a188tZNbty4yTp4GuuwppRxqgiNNYSciDHTiMWnhBODbVyJw4sR\n1zmyQIyJ/5e9N4u1LDvv+37fWmvvc84dau55njjPpCiJkuxItqXIsY04TuDAQ2DAyEP8kLcATt7i\nhzxmesqDkSABktiIAySA4yBmZFtSSFOiRJk0x+5mN5s9VFd1zXWnc/Za35eHb+19i7q7raLJbrJa\n6w8U6ta+++z5VNX53//3+wdZk5YrsghlGIhh4fcjZ/J6zXDzButHHmH1oQ+jv/e779Tb6Y+VYro7\nqPePwPKfH0efJbozVlb84G5mAexzL77bwfeT+5gD5c+e38zBzEHsZw9v/pQhnPyGzIC8baYhoMzs\nSWf2862LN04s+/1Xbp5Y1u0uTyyLM+UONrMTm7lgEu/2gTj+AckfterdFhiIzDxLM1uUuy6GuLvX\nzj049jYnPVd0MQfzn9/P3V2vnwIeRFNTU1NTU1NT049JzSS7CxnQf/jD9E884QbZ2Gp5dATrtSdx\nsh7zyNyd8lcWddaYmX+CM/zPxT96SUxoHpyTtVxSDg5IH3mG8MHnEInwne/BW1dhs0bXh9w+OESA\nq8DlRWS/jxwsew4NyqKnIBBDTTK5aVSwH2ibi7h/506TM7rGD7Aypak8NTVdAauQfBuXVLi9FjDx\nFJWNJtsxe8xq6otqsE2fS8eRSjNGPr6Z88ZUrWa5hGIQcCZaDafVfdTRzroxrSYdo+k3tQBoXaOm\n0aqBV6q5VrSuI1L5ZNUEq6Okqs5DC3VbWkc7Q03GBQMpSpzSdm4KqvgKaWr6DBCoTDGDZKgG1vXa\ndFLIAhuDyIbh8Brr1494YvksH3rgER46e44X33iDK9ev0VuAlECVpM5OCyYMpqSU/NyGgRQTxEjO\nha4TNHoqTzcb4qLHEEophBwIqqgWNjdvs37kYVYf/jDye7/LaGs2NTU1NTU1NTU1NTU1Nb3Xdc+Y\nZDdwo+JHKVr/1ykar4N3PPbv/EXCYgFvvAZvvglXr8KNW3BwAOtNHVkrxyOW7rTUjdSRy1KNsq5D\nN54os75Dh4webNCPPsLqUx+F77wMv/0V7OYtLG84zAM3zbgWA5cCHO6sOMTY9L2Pb9Y6e8UTThs1\nopgD9Sf0vE0/uZ9MJtUfsEA8KTaOi45gfHwkr6axxu24D1hfLYqq1DFRce5XdZFsbLRE6jZrHKwm\ny0o9ZqmjjWgBrYyvatGE6RLKZNr46GRNkNXrLKMRqOqW2AjtH7lq6sk3Va1mm6/jqTCb9hcBJFTD\n0NNiiHgyrBRCCGSEYJArS00suHE4eZFu8uWxgTNERJz/VjCk89ZKMUM6by5dl4IVZTClbJSFHHDp\nu1+jZDi9PM2F8/fxwWfeh5ryG7//u+yEyDoCJI4oLCSw1kKQQKhRu1ITcVqMLP58hq7HVCnDETEs\nMUvkzYCGQw6uXSW9cZH0M5+Fb36T9Zd/511/z40S4KEf4fVNTU1NTU1NTU1NTU1NTT+M7imT7Meh\nHzYTY8DyscdJDz5Qxyz3Ye+2J8g2Gwf15+IrjxyyCXxVx/TMPGkm3lTIeuPGjQh6cIj1HfHXfp7F\nc0/Bb3wJ3rpKuXWLw2FDNngtCm8J3I6RvVVH6BMaItSmRUEwAc0FEaGL4rsOzhvTGo+yIPgkpbo5\nM/K6qutl4iNIWkZi1nF6TKl8sDEZhmDFIfomdbxwNALlDu6Zjv0FI2Dfj7dUxpeZuWk1HksdTR1H\nI2GMbUll/vt2phIBCSCBWFNzlfVPteOmfcd6/qo2mYSmSpiGg/zeRQnOC9NCkDququJM/2x1/PL4\nHMBTdFqXoYZOI6K+fZ8UVciGBcjBjUvBJg6aBQHz89gUCH0g46UIBUU3N9l//YDbe7c5/+ADfOCZ\np3np1VfRnFkidEHYmLGgnqcZm5yJMZFDQQgkjWgHKorljHQdaj5qGVMCg3x0RN7bY3jgfnj2Ga5+\n+XfG2oUf/s32r/0qxjvS1NTU1NTU1NTU1NTU1PSu6Z4xyX6UNMuPIgN2nn2Wfnsbrl5xWP/+mB67\nA9Y/GmVwzBwLNfumBWJwBlkp1dQS8uEhYbGg+3O/jJw7Ay9+H176Hpv1miPglsBBEr6/6NgLQuki\nIXXkarapGUECpbLEgghS2WNUQ6lGsrwpsugEuzeqkaXqv9cRUK1JL8u+rmAejlOdDDKFyvyqHDKt\nqa/RvAKsqJtFWvz1gKVECUCMWEhYikgXsa0FpOgprlibNmPE6nigWE25qSLFjzeogYKVAvtHMBSk\nFB9jLRlRH4U0gSCRAZnSYp488xlKGdk7BsGsJq9q0g4IOs6CCiI2pdzAr0cMgVwTajZe73p9CIGi\nRkhxKg2Qem9KTcZlFUZkVewCufg2TZ1xlgCzTOgTSZUrm+usX9vw/o99hNOnz/CN7zzP/v4efdfT\nAWuULpu3bIpgmunikqzGYJkoQoh+bqLFE2dm5FJI6oy84dCZd1tPPEUHM5Smd0dtyLOpqampqamp\nqampqanp3dQ9Y5L9pCTAqQ+83w2Y/b0K7D+4g0Xmxownkao5hkysrylKVQqWs6e/VMk3b5Gefpz4\nyz8LqYOvfAte/B4bHThcJN4qhcspcT0It7cWKILFRMVmVV9OCcHh+CNizGqGymo6y0rx5ky9IzFW\nz8sNspq5KiOeWsmlor211NFKQ00p5RjIbyIOq1ebjscErAf6HrtwGl12xEWHdQn6iKx6CIGQAgGZ\nGjUleIJtSixNUGVPRFVEWuWs3QHmn9otjSjix6eKFf+V948oRwO2zshQsMM13LyNDBAKyAB0NaiW\nK0auOkIBCB6Rc1NNC+BG2XisQWBQN9UcXVavhXopwp33QCRUrpoRy4ipc9Mui0O7vfnTmzQtOpst\nqyJBOCrKRqDkzNDt8+L3XuPBhx/m05/4FN968dtcvnQZ6zp6fASUUkhRCBJY54EudTV56G2WIQRU\nC+SBED1Pp6qU9ZqS1wwHB3D//fQPPcLhxdebYfUjqAszFv8cyPxus3N3C1/nJAz+h9HsPZ/b911C\n0GWOdj8LYJ9Za+ZgZiH9b3PKYifvQSkzMP8ZOn1en1xvc+cPRaq++PLlE8vCqjuxLMWT/+zKzDXU\nuwTJz4Hp3/ZJmvtp09y+Z5aFGUj/3L2f24Xqyes1+x6YLZCYOefZ98D8Wc/d07lVdeb5nD3n2Z3M\n7rqpqampqampqekeVDPJ/gilnV22H38MDg89QbZ/4K2Wm8ogKxVmP7LIYJzlw+fsBEr29brOTatc\nCB//IOHnPunb+Z2vod9/nbJ3mzdFeFOVS0FYLxM5BkqMECKqBZE4fTCJKUxGyzjGOALwzY4ZZKYj\nW0vcQMLH/8xq+2LxDzBmnhKzUhjG9BY+9qgYOnhiLddT1NML7Pw2suiQ3SVh1UMXkS4RopBq22Ws\nfLJ4R+MmaoTg5QZhhOGPTZymRAn1Q4ubaMepODs2oKr5YNUA7NLx42ym6GgujmZUUTQr5WhDWWc4\n2lAONtj+Gq7cJmyg0tOIuJElFYpWs2eVVwaI+AjnnY2fBjG6VabjVOrUu5mBQMDHMet0Jyn4aKeJ\nM8oSRgky7UNMPW1XjKHe0GEz8ObFl7l5cIsHHnyE5z7wUcLqJS5/72Vi789ElEA2JYzMMy3EAhI7\nLEAZNoRFV1tFC1EVtKClkA/X5IMD8pkzLJ59hsOLr//Y3k9NTU1NTU1NTU1NTU1NTT+taibZH6Hl\nY4+yOHfujlbLPTg6PGaRjRT7ySzBDbEJiFXbFaNguaDDBg4PSb/4GeT6bfjK19m89ApHm4F94Lt9\n4GYXOVz2WApoiBSpPC4JlOLMLhGpo3tuOBECWspxgkMczB8qhwtj+r6WgkQ3oYppTT8ZpkYZGWG4\nMaWqZIMhCPncCs5swakVYasnrnq6RUeMQhAhhIDYOPbpCYcQ/HujX8hokhlA5ZTVtNR43IEIZsQY\n3NhTI8UKxpfgrxm3YUCUqWRARjC/1NeOCTeFPiqlM3TVTyOgRT05NxwckQ8Hyq1D7MYB5eYBW+vi\n5zONWLqhFwXCCD8zwyQgaoQo5KL13I89UzMlpNoiKoGApyMC4mYahpiPtw4I8Q6eWukSWgohilcw\nBC8Z0E7YP7rOK6+tuXV4yKOPPsZia8XF51/w5KApKXhJQNJxxFNBB0R7JEXfbjUWtWRCdoOtDAPD\n4QHZjP6ZZ5Df/q138B3W1NTU1NTU1NTU1NTU1PTToWaS/Stl3P8rv0wXA1y6BG9dhmvX4cZtOFq7\nSableP5xNK3quNs4TqKbDaREvnWL9Gd+gfDB5yj/y//J/pXr7EW4JsKVReJWH7i9tcSCM8ssBFQh\nqxEq6J4gU3ppgt6HgA4ZiULJhbGhUoG8UYf6V26ZlgIS0LxB1ccsj9Q4MOOGwRvAb4twfZwfMTfh\nPhiNP3dui537TiGLjrToiCmxXHYE3AiTcDz4E8KYvXKnaPyqek2+TEZTKxybefW6G8fGmtUWTRE3\n8zxxdoyT98KB8ZVMCTWpJhlmWPQjGJNqWtNnFiM5FNarBaXvKKsF+9sLvqmF//vo0Fly9awEeMqM\nz6pyzoxdgWWAWNy0jMXNylDZbIGAuN9HzJ6Ki+JjrSoylQswPTZejmBBKAaDQRc8kVdSJKl6SUEU\nJCWCKsUKV956hYtvvMDq7EM8/alP8e1/+VVQYyjZTT2EoUugShQhmpGHDXHRUTRDhihglhjWa+Jm\nzXD7Noc3b7D49GfY/uIX2X/++R/j+6qpqampqampqampqamp6adP94xJdpe0nh/7Plf33QdHRz5u\neXDoX+fhmDcGP5gkc8r9BMKnKMRAuXWL7k9/Dnn2Sfjmd7l15Tp7AjdD4OKi41YKDCmhITjwPoSa\nABMf+6ucMU9HKaj8Ifi+UopHprR446SZelJJjazmbBcD1cygyqHC7ym8FgNXBd6szK8VsFXbKFW9\nkfH5ovzGa7f4WYX7L+wSJFRWV0JimAwvqRGqkZM2OllSxx6lGlo2MsbMiEEqtL+Oi47rRjcEg4TJ\nFJQox2yykamPp9NUK0ut7mQsvzSkmmi+jhAIYqiYp9jwMdR8NHDp2h5ffuUq397bsJVSPfbx3ITX\ngb8nkdNqnAPuV+VxM55RZRk83ReqWRqCElWmMdjRvHSpD1/W4xJxgpQBiiCqpBTZFENDoDNlkHFk\n1QibAY1CTIkSImnZs9m/gsb38eSHPspLv/9l+r7HCKxN6YpSQnCOXM7ErkOLIqEgIXkxQ8kkFC1K\nHjLl6BA9f5706GPY89+hgXeampqampqampqampqa3su6Z0yyn5QWu7sO6j84cINsvb7DJOOYRTZx\nyMQNss3g4HsR9PYe/Pwnkfc/C1/7Fus/+DoHwI1Vz/eDcDMFLAXok6fITCkGVgypKSodWyjvGFGk\n7tKTYp5cU/WUlFa+WK5JKlFlyMYVVV4x+J4EXg7CtS6wEuhFOG3HfDBVHxmN0RsaY4x84yjz2ktX\n+dzhwCcePUuQFSx6QpJjMwlBYjXQcLNsNIZCkCltFxjTYTKx+sekmOFpKkEg2rG3JDJ5TGE0bOTY\nd0qxJsdqGyXBwf8yTXd6Cg0FC+NqPhJbNpnvXrzOb754hasIoUuM6bXRlIsi9Gqs6rjnWyK8HiO/\nY8b71Hi0ZB4blAcFtgUGEUoQB+MbBDW0jp/KOANa2wKCiZtjOKMMcZM0BSObYBLo8fHYGKN7sJvM\npgzEFJGUKNtbvPitr/Po+z7AY5/8FN/9g6+w3feoRDZa6MQNWLOCZSPQe+umFoIkZ9sBphktA7pe\nU0Kge/ihd+bN9cdEMd0dQH98T9+pOQD+7A8M7n7hXa86C0KfKSHQGYL+HEx+7lxmfde5Y5kBtc9B\n+nX+lAkzy+e4/3NlAGXmvnz3rVsnlr301sGJZTvndk4sS/HkgeecZ45mTicv2Bxw/u387DkIfpyB\n0xNmr87MkpPL5u/BzPMwcyxdPHksnczc6Jnzm31G3kZ32SExe7105gSL3P2+m5qampqampqafrp1\nz5hkT/4E9mlAt1zC5ctukh0ewXoDQz5OjI1grBEsD956iWEhuIH1S5+h/+RH4Ld+l71vfJPDAV7f\nXfK6Gft9RGLE+kRWd28cdVbc/KgJKqt8sVJ5Z1kN1FsTc/H8kaknsIbK2RpHQW8W47dN+FIQhphA\nhK0AHcI5JjyYm2+4mRVDQr0FgCBCLoWQIntmfP7iLV69ccifef+DrFYLN1L6NHHFRu78+FE51BSW\nMSbMDDNPd40fbETqmKQwNVrKHfdB7timD2O6IThtexxHnRJrQpnYZNTEmVGKuvGXlSCQs5Jz4Z+/\n8Ca/+cYtUpcIeGJrNMisngP49GWsPDcxI5qxJcIbQXg+1CY7VZ5T48Ol8GEzQs7E5B/+gkJfJ1FF\nmMZop6KFerihGqKGkEQZTMiIG11qbpZ1EanHL6UgIdBvR157+ds88uQHeOpjn+J7X/sKu6stCsq6\nDATpSLFDRQjFWzRRJZdCR/KkWW0tHdZrhvWaMw89zPaP+b3V1NTU1NTU1NTU1NTU1PTTpnvGJHv3\nf05bzZtS4PDAE2TrtRtgpY5a1vHF6eBM/XulODssF8rhIf2nPwpf/hoHX/sm6wD7WwvWvwlbAAAg\nAElEQVReUSUvEnQR6zqyKpIiR0duqKQY3OTBmymn0BpQqoFSEKxkCoLm4swxgyEX9tV4qxjfkMBL\nIfBmDJwKwnZ1jITgxlKIx+cahMA4Pap1zFFQU7oUPQlVI1/PH2b2v/E6f/PCLkEEDYHYRYf340ab\n1CRWGBNSo4moY5Olw+ulwvkFainBceeBwHF6ZRxnraZSiNE5XiFM46fUbVBfN+3TxiKD2ppZeW5Z\nlcOjgS9Ug4wQiFg9fhAJkzkpIjVp5xZeqKk/EDqBc9Wg0xB4VZQXYsd3SuFpU54YlJ0APfi1qkZZ\nFAf1mwmpXivMKLUUIOJpuBQMU8jVvIxW7bsoJHVDFrz5UlLitddf4vGn38+jH/kYV174DhLidLyl\nZCQGBsvEEhCv6STnTCxKKYoOG0yVvD4inz3bBi2bmpqampqampqampqa3vO6Z0yyn4TCuXPeZnn1\nGly/AXt7DuzfDHWmRO8wyGqyrGS0FMq6QM6E/+ivsP/f/U/sbwp7y57vx8CbpZBPbzu4PUWG7Imw\nYSikBIhNzZJSt10wNLtZlouPXm6yejvjoXIA7N+/TXdqwX/58g12Y4QobkKFwFnquJNNkDBiSIzQ\nsDGpNU6SxDvGJFMdFxPcKFJTcgy8kgt/97ef5y9/7FHOXdhltbVCeiGGQBfiHckymfZjZoQk0ziW\nmVa2WW29VOep+Y59rDTE4GUFgMVq6NVjVqupsjAC/Y/vn1qF89c/RxWyKoN6UmqzGfjnz1/i8y+8\nSVgtCATnv0nwNlGF8SKkGI9Tb4gz36rZ6JwwpdTUHQIri6wQ3hDhNfVBxmxwNGT+g/tWbF3fZ2cN\nfYDU+/jlIgpSPFEXrWASUC3EOiIaJIBmfx5ih2kmBkFFEFNSZa7Z0Zq4Mt585Rvkfoch9VA2iBoR\nJYmACVKUEjI6uGkZNJLzgOQNRQvDwQHDrZsc3Xc/qx/xvdTU1NTU1NTU1NTU1NTU9NOuZpL9KxTO\nnHFTbL2GzQZyhpLdqBhHLJFjiP+QPVlk5uv++p8kbgZurQtHqwUvRuEGhm71ZNwUKsWhYlnV8VSI\nA9UrAN/MyMXZZqV4U2VWJatxcJg52Ok5eGCLfHqLnQDfuHSbsyGgdfRwNL/GBNTEBapUfZEwsdR0\nGlk8bo0EJs7XlP6SQBcMSfDmesPnv3uFv7i9pE+JkAIhxeNxxQnm7+2XjMdQfTMh1tbOEVwmd+xb\nmCJXoYL7YUqKmRlh9PzGVFqorCEzRAWJUBy05eOVxeqtKnznjRv84xfepFsuPI2GTEk4H4e0KTVm\npoQQp/IBNXOumBxz4sZjiRI8aIhOqTozWIjRxcjq4TPs3bfDweHA6sYhWzcPWdR7EaMbXmNBg4Qw\njY0G1L3YkT0XAqbFTa8g5GLEUigpYkOmxIgd3eKhT36KV/6/32SxtUQFshYP51khlEAKetyUKj6S\nqjmjWihDJo/35AeGYJuampqampqampqampqa3ltqJtnbyIB09gwc1UbLo2qSqR7D+sf0mNQElBYM\noaw38L4n6Z56hPU/+mccrHpejYFrppRlcki/uIWi6uBlSUxpqbGZ0XCAf6kJoQwUNbIZ61K4+twF\ndGdJCMLpGLh5+4gvvHGbtOh9MyLeAFm5ZiMXbORrjaYXOEdM5Y4mxskSqSbXSAQTT1qNrxtS4qvX\n9nj8lav8/HMP0S/dkEuxmk2VeRVicOOqQuzH3Qh4IyVuolmxiddVva3aZBlqKswm905iZXkhhNF8\nMwf9SwiUaryJGWahQpj9NdcO1vyz716mXy4gxWns02++G1YmRpBYzaPxnI2io083XlM/RgnOWdN6\nLIInwcbnyUxIUbiyN/DYhW0OV4XNqRWbgw2LG/ucv3mIIiRgY84lHw3TYEaW0UA0IoVsESQRLCOI\nt4QWJWXIfYeuN/Q7O5QkXPjox7n6L7/KcmuF1uvmb34fOU2lTMy2pAUtBdNCGTxVNmYBm354pRnY\n/Zym0eA/ar05QPnsS+e3NwfQnwOUz9/xk68tdnfHHWb2MXfOcwUGzK03d3SzRHbQmUMMM/fFZnD+\n63xy2ZdefuvEstWprRPLUupOHstdnl+YO+eZaziH/H+7O5LCSQj+UE5uYe4ZyaXc1TGu0sn/VnRx\n7lxOHl8uMyUQM+ut0sl7t7uY/+/MVndy3WU/UwYwdzwzkP5bR5sTy67sn1zW1NTU1NTU1NR0b6qZ\nZP8KhVNn4KCaZHk45pGp3mGSmf+5lAkWr0D3C59Gv/kiN773Bm/srnhTjNKn4xZLM0RhPWT6PlBx\n9pVDJpRSyGpYEIY6ypfN2AwD+1E4ePgc3YVTBDNSimxhfP6NmyDxGHQ/GlG1U8CZY1LLAO5Mlo3s\nMGrLpI9oSk2yTSwumMY1Y/RxzBwDHYl/+vJbPHV+l2e2l87SujNJlnz0chyyvNNw8bFGOwbzp3oM\n4+hkbaEc413OaGPiklHbOCX6yKNZOD7/arCpCophxQsQhqHwpZcu8dZBQZYdMchxKqy+WOof1bR+\nmLbpQ12srZnTh2yrFQV1BDOKf5963QN+/UpNAP7+azd48v5ddlZ+sutFz9Hukuuby2zvrVl0zkWD\n4Mk6MwJGGpl0xYgSSKFQLLCWyBJDBSQFN8qGTI4BGzZcefnbPPzBT3DrymWGq5eJMfq51Wc2RCOj\nRFVKKcRqRqoZVjK5KLJ7Gm7ffEffb01NTU1NTU1NTU1NTU1NP0ndMybZuzvkVTlUZ8/WFNmRj1sW\n54wB3hw52j0lQ/FEjg0Z+aWfQY7WXP/i73IrwVvBf9ofFgkVT/tgMGRlufARvjxkQgyUYhiKmjcY\nlgHWGBtV9ins3X+GeP8plouOPgZiDPQx8MrFm3zr+hGnFx2bMUFWWVYymT6Vz1UZYWMKQEYufjWK\nBCYeWpjcKze4Yko1WSX0nRtSGwqDGv/0pUs8fN8ZVttLYhBCDJPBJLWFUvDtTykCgZiYlplBGMcc\nazLMxhTbnXfHFCNUwP9kuXkjp9RzDOJJtKCIiV9XhW9dvMHvv3KTbndJDBFTxaoZNzLMrBw3cYqM\nrLPj5JhUoL6MvDXz6x3FxyPHsdJYkxtFvQjBovK9gw3fe2uPDz9+jiErXRdZlY4bT9/HtVuHLN+8\nyc6QSVFYKEQxsjkHbYT5GzAUI0pBpSMbiGbAywoSRlCjDBlR5fql1zj/7DNcvHgR6woxJYxC1Ohs\nNfMkpGAULVhRNBfKZkPJmXBqF20mWVNTU1NTU1NTU1NTU9N7WPeMSTY3UvLD6AcMlrtcX7a34PCw\nGmR11HKss1TA8h1Nl/h4GrB89nH2vvQVbm1gf9WzDhD6zl9ZTZ3NYEjQqSRTBPImo+KGSimGRdgI\n7BfldpfID11gcWrFsu9IUei7RAB6gd984TK7vZtw03ik+Ugl3GGU8YPGGNyRHqsXKYxXS8bsVt1O\nXScEh9UXVVJKbtiY8sK1fb75xjV+8dwOceSSheNU1pgso+5vQupXA2o07wzheBhGpgvko5bVxLpz\nDTlOvPm4lUybJIJY8HbKIGxy4YsvXyasOlJMiFkdM62HUeN0YxIsgI9MiptUWp0856u5mRgkVANL\nUbz1UvU4SWc/YKYJZ1Li62/e4sOPnXNgfhBSgDPbSw5T4mDZsb5ym+1b+0CkwxswS330YvDjiPhj\nGMrAEDsSXnCgIgyq3pqZhbjoWF9/i+X2LovHHmO4cgnVQIgRtQJFCCmiZuiQkYVimtEyoKaUYUDP\nnCa//tpdvnuampqampqampqampqamu493TMm2bv58dyA/tRpzuzswsXX4egADmuibKjw/jz4uqVg\nqhQ82bP89T/JtX/4ea7f3uP7OwtuLHvoxM2vCBSlKHTJyAqlFIo6CcfEOMpKxhgwDjeFQzP4yBNs\n9R1dF+hTJKVIFCFIIInxD7/6Ktc2kBaRjVUGkkGIAVUlxfgDLqFWI0rMefmhtiMGEQf5ixCAlPzr\nQY1BR0tLvAEyCCkkeoFF33G0jmxi5H/46vdYLhf86s88S5c6YhybMr2FMowpL7NpPHGCeqHu+txR\nHsCxleYprztANjKONQJlXG6GibdiOtPM02OEwGZQ/uff/S4X17Da3nb4vfp5DznX8Flg0IwEh+Ov\nUqKL0bsYihIQ7t/p2eqCc23MuTUvXd3j1pEyiNHFQBAjhDj5qV2KlekTKGa8fnPNF1+4xK986BGG\nmszrVpGthXJ2Z8n6zA4Hm8yb336draOBVRfo1E22otAFYVDqWKaQdGCNm3UBq4w5iKV4M2dRDi6/\nyv0ffB+vf2UfXR9VFp7z8ajnX8oS8kDJGc2D/9occePhR7j6jW+869j+R9/l/TU1NTU1NTU1NTU1\nNTX98dU9Y5K9mx/OBYh97y2Dd0L7S/Exy1IqU8xbAcsYBbtwlvzmJW5dvcWN7SU3UyBrIROR4Kyo\n4n4EEt3UUdzgKdUAGlTZGBzkzFEKbB69wIXVgi4FuhTpYiSm4DkqEQ4O1nz1+9dZLhccVcNrZHhZ\nTTyNsHsHsxtRjQurjkfPrPjghR22+kTfJaIIg8Hlw4GrBwOv3zzk6v4RwYzdLnJYjMF8dBE1b0gk\nEETouw41Y5USX3jhdX71Z54jRSEG/36IYyZtvMg1UUaFUQuYhQrtPwboU4H6oxmmFcyvtWUy1O9N\nDLORHRbcjNIQELxsoajy4uV9Fqd2iCE4kwtP7o0qpVSOm3Jm2ZOzsoyBh06vePT0iod2l5zf7ulj\nJAR8dFaE/U3mxsGaF6/s8Z23bvPyzTXLXujq+ZeixBAdgh8C/SLxzVeu8ysfesQbNaf75V8vukQI\ngduPn+fWxZvkG4ds9UIvNVWpNjWWdjGQ1QgUNCYfRVUgutEXjzbQd0gIHFx5k62HHuT2C98m9rsI\nWssB1FN1qsTi4H4tSsnZxy2Xi8qTmwBxTXepLp6EhM+D8k9qDtQ+C6yfWfh2+5jrB5gDsM9pbpsS\n7g7KPnfks+vNAvXv7lj0bUoSZAbIzwyU/c6/C0a9cePgxLIXL++fWLY4tXNiWZw5Hp0B4M9d/blj\n+VHrM8rMvuefp5P7PrU4WUIwV0qRZ4oOlvHkeg+dXp1Y9ujMsod2lyeW7cxA+hczMH+Afub9N/eY\n2ExpwNybZa5cYD332veADtc/aob/j9Zd/lX4I+vFSyef/R+3/s7/ceUd3wcA3dl3fh87597xXcjy\nwju+DwDZfhfOJfbv+D4AiIt3Zz9NTU1Nf8x1z5hk77ZC33vr4mbjqbFSqknm4H5RnRJkiDAAYdFx\ndOkt9heJm11kMIepB/GPaD6qF8kMRKAUTztls8ksOzLlMCv7COXRC2yd2aLvIl2qKbIYCXh7YxeE\n7944YA84XU0iwSH2WGWKhYAZHGVlJwqfuH+HTz18mkfPbHF61bO1XDjQPwYQLxAY1P/TvzcYl/fW\nfOfKbb7+2hUYBlbScZiVoSgmgRA8fRXM6FKiLBZ8+9oe9UgIwVsXZWzO5I4PhCMHDDe+bGwOmDQO\nQQrRjsdCcRqZGwjGNPpoNSFnGFbLCVCdmGY5KyUFuq6jjI1uwVlsVkcszYxViqxSJAbhE4+d49n7\ndrhvZ8FWF+u98HtAbQc1hV1T7j+14qkLu/zMY2f51qVb/MZ3rzJU4y7FSFE3ylQHJAhvYNzYX3Pu\n1GoqRRjHNyX5eZ05tc0thIMghGsHaCcUU5YipMpdG4rW1kuhmNCplweMfLWyHiAlUCOvD9g+/xC3\nQudFB8H/ChiHWFH15VrQkv26DAP0fbPGmpqampqampqampqamt7TaibZjIxqkgnHxlgeaoulTo2W\npYa2SjVoyJm9w31uxsjtGBmGgdgniIGSs7O7hkzXC0Xd0cp1/DLXFNneWlmnwPrxc5w+vcX2omfR\nRfrK+AoihBAIQehEeOnKPsuQyBw3KUqF3Y/mUVbjZx/Y5teee4DHzu14SqlLhBSJXaqjkxGTMRkg\ntRUSnlbjE4+f508+9zD/7T/7Ojdu3ubU9pJNDOwPBYs+ohmDQUqoGRt89DREZ5elGI5ZZKEmppiK\nJDEdYf5j8u2Y5eWXyUc1MTcSZTTCqu1mo0FmMpUOGKGy4gSLgg1w83DDamvL02M1UWFK5Zw542tn\n2QHCOhf+xmef5qHTK7aW3ZS0CwEfSa3m2zgCmq22kubCatlz35ltPvDAKf7T//d5HtjqMLOpBCHG\nSMmeYru6v+aBs9sU9WMIwUdkCREJxlYXCTsrbgThhipnbx5hAfogbEqhDwHFEytSn8VQj4/gTZxZ\nlbgZ0GUPBbo+Ek+dgs2mHldtBa3wflNvATVVtBSsFMLiXfopaVNTU1NTU1NTU1NTU1PTT0jNJHsb\nhS4hucB67b/qmCVWJoOsUP0yAIHhYJ/bquwvegZTQhcYspLNkBgoWdmo0WUjV1MkFyPj7ZWDCYeL\njs0jpzlzZoetVc+qTyyqQRODw9bd/DKGUvj+jQP6PrA2I4VqRtV2S0G4djTw9E7P3/qF95H6jtAl\nYpccGB8DMUYf9zMfiRy5ZFPzpQinDM6fVv6zP/sZ/snzF/nCt1/BSubMsuPmOiMJ+pQIqhRVutWK\nN6/tce7Uqm5fiNGTZdPIpXlySaitjX9o6McqBD8GqU6W22qe+lJEQm3DPIb6WzUehVAbKgULQhm8\nLOGVa3ss+q6aaJ4cc7PMWKbIVhdZrzMPn93ml5+9nw8+cpbVop9Gc9y4q2M1llBTzPyMkgXUjBQD\nqRSKRh65kPhrH32Av//1S5zb6hmzdCIBCbAkcfnWER+q6TEkYqaEEAlFGQy6LgHCuShce/gcV8IN\nzl89YCOeFhswkoQ6rgshKGuF7RTIBUox+pIhRWIuxBQpw5pu0aHDGhBMBBFDxYgCVkYW2Qaohlnq\njssNmpqampqampqampqampreg2om2Qm5DdCttghmsFm7OZYHyNnTNVbYVHPMgo9Sigm3zbjaJQ4W\nHaUUhlKw5ObJkAt5AzEZirO9clHWxY0zMzgwZfGxRzi/tWRrkVh2iS4Gln0PlfsTg1Q4e+DirUPe\nuLZP3F6RRhOtQvIv3jzkV5+5wN/63PvY3lmQVstjwyqEyghz+L9ET6eNjZgphWmM0U0dN94+dmqb\njzx+lv/wT7yfN24c8X9943W+8PXvUgT2hw19iKwWC0II/K9f+Dr/+WO/QhcjXR99JHNKkd1htdg4\nIDkhyNzsslgh/F5R6eWR4+ikTaOEx2OW9es6NqnqqTXJQhbl5uGGf/T1i4RuySZvUHWDLJfC6UVC\nCPzaBx7mo4+c4b7TWyz6RAqVwlVNunGfVsczg8V6JoaqJ9HMjBg9laVq/PrHnuRPffARvvDiJf7H\nP7jIua2OGPz7i1Xi9VtHSHTWnI9aRm+UDIFoSgyBPgT6LrJcdOxtL7l0bg/79mW6zljGwMYKi+D3\nMmqhSOKgFFIdu8wC3Xpg6NaEVY/pwGJ3m4P9/Yrc8WsWVbGcsZTQYUMZBsr6iDwcwc42ATeGm5qa\nmpqampqampqamprei7pnTLJ3OsEy4sjH/UhKSM51xDJD1toKaagIKoZWswQRBjHWQSh94ggj54wk\n8cRYCohpBa47IH2tSqkGGWbsA1cv7PDk9rFB1sdEjDXxFUYe15jwgr2jDRtgWcclQ3Dj6/UbB/z1\nTzzKX/300yx2VsQ+0nc9IpU9Rh3Pq8baCN6Wyheb+GFSU0/cAdMH+oWxs7Xg/lMrvvbKJa5eu8Hu\nsmc/K9G8yfFbr13i4vVbXDj3ACl6ci1KNZnUJnOmEvunr52T5etMnKzx2zFOY5WMJhn1PsC0TEwI\nwbAcEFEQeOP6ATc2hcTGyxRKQbVwdtmxKYW/+XNP84FHzrK7vaALAuIAfQehxWqOCkKsBthxu6YA\nIToTTs0I1N+DgTiA/5eeexCAv/sHb/DwzpJYzbyDwdNx4Y57gwYIvo8oikS/R6EI28ue+87ucHHr\nKhcOC4ayDMLGIJqi4g2XEbAYUAwtMJjS58KQiz8nXfInqRqgUs1XxQjm4H6soLlguWB9j/QLdLP+\nsbLJ/jjUAMyB7ePMX2hzAOu5187w5pn7GzK8zZWdKwOY+wt2juUvc9uc3dxc4cAM4H9uvZldzJ3z\nbNnALOx+ft9zmoPOf/2N6yeWlRlIfNedBNtP7MM/SjMgeZm5EDpzIebu59uVNsytu0onwfZzy4YZ\nOH0383x+4rGTkOxn7ztZanDfzkkA9VZ3cr/9zLI0UwQgc88Ds5d2+qHTnZotyZh5nOae2bst4mhq\nampqampqavrp1z1jku2+w9v/w6NkW9gIrIJcHOrOcRulAuouA4p/IDWMI3FYvZnWtkZvGPRGrEAZ\npw3V2BQfEdwzuH5mxe6F02wtOla1xbJLzvTq6qhikPoRtbLQNtlIyxUxppogAzD+xmee5N//9FNs\nbS/p+o4QIylFN8mCICHUcUyIKTFaFT5qKTUJNX5IluqjjI2T/ltKwoUU+I9/7VP8g997ka8+/yrb\ny44D9fG/G8CLF6/x8eceJsY6NomPgRKOP5DIaHqJt4uFGP1ai93hGtgE6Z8SZzYeSjXMzIFnVvli\nhn8OCvUcXrl2m9AtMCtoNSpPLzrWQ+HDD5/lY09cYHd7QarnDseMtOkem5tIZnU80Yxk8gMjt9Gk\nctNcIQS6CPQdn3vmAW4cDXzx1dt+H9RLEqicOT9Ubw0tqoT6Qa6ot3NaEPoU2V72HN53imtv3eb8\nUSYAq2Qo4gUICkWMOI5gVjN3UCWEwDAUbNhg9b6OZqSo+aBqHVtVLWgeMFOSwK4Ehh/2jdXU1NTU\n1NTU1NTU1NTUdI/onjHJzr/L+1upIqVgw+AmglSDTEAncDyTuybm7YLa1bG5GNgUJaSAqZtquSg5\nCIHCOitZIZtwbZlYndvh7KkVqxRZpETqEjG4yRJDQAzniFUjywTWWbE6YhlDoBTl+uGav/7Z59je\nWTqcPzpfLCVnmYU7RijDaJpJqBwzN8S4M0nGsUE2pUiq8ZRC4OOPnuHU6kP8g60VX/jaC6z6xKEa\ny+0d/uWrb/GXFGIF+Ht4zOrrjy3JKTWWPKUVRSaDzOy4ndL9G62v0Zoc82PWii1zVr+PnGqspQpm\nvHRln0XfcbQeyEPm9KJjs8l8+on7+FMfeIizp7cI1SjUad9uHlm99v4Y+PfVIKh4EUE1lkYDT0ot\nIDCBaqH29dr/mx96hH/y8jdYdRGzjNZnK8bqfCJuEEpESyFVQzPUCxDEkzbnzmxzab0hHGXOmKPy\neowchGiFSC09EPEUWS0EUDVCF8lDRkKoacE6Vlp9VjUjVsabbQa0FDrJbBdt45ZNTU1NTU1NTU1N\nTU1N71ndMybZuz3MoIO3+kkpbshgdcTS00RageeFsVVSWRjk2vS40eJjmMXHLAOgEXQQ1nhaSYPx\nfS0c7C556Nwuu1sLln1HjLW9srZCBtxEiRLouugmlkBMEUOIIVJU2VsPPHV6xZmzO0iMxCg1PRaI\nKTiQf2Sb1abFEHys0MaUl9TvVcOIMdAl3s7ombPqDAbhzM6CD3SBv/7zz3L/qS3+t9/6FywWHbtb\nS37/hdcYzNiNkYinr6zi60cAvpn5bCBhaoqs3/CvQ5zGKM0M0cqAG2n6YwNmrKOvdezRzFNdMXhT\n5cvXjpDlkqP1hlN9YsjKLzz3IL/6oUd45MIOfUyTKTaOcVlNeY0FARZ8f2JCUIU4Gml+j6waWOLO\nWf1emFJnIsKZ3S02m0wXQr3PkTQal0Fq3achZkSJaAgUVQTFKP7sWeDC2R1uH214/toBn1AjaoAY\n6BSMgkX8uVO/4oMWkvTT+eSD9fSsm3gj6mhiSoX1Y+ptlyWDBSxvGri/qampqampqampqamp6T2r\ne8Yke7dlmyN02Pi4JIrTnxxcrggaHcjvIHo3lR49LHSrzOe3OnLXUdZrMjCoUTCi1pSOGTeB38nw\nvQH+xrMP8sDpbVadN1mmCtcXgRQjq2VfmyiFFCMx+ujk7tYKSQkEXr92nf/qL/0Cf/aTTyF3GGLj\nuGCoqaEYpCajxnTSGIVjtK+qOVbjcYjPGRoE0crpkh9IIC23Fpxe9Tx3/zb/3qcf5bdevMZ//Y++\nxItH8C9eucy/9amnp015EsvTVVaNmXFE0i98/fPUIjmOT7pxpaXUkccK7K+GEuO4pcPCnN0jgBjf\nefMmV7OwdXjAbor8hY8/wWefvsBDF3ZYpjTxxKC+rqbdxlFEqdD+cSRX6+hjPTxnlAX/vqodJ9EA\nUXVDVCtrjch//9d+lr//pRf531+8xuOnFnRdIqRIrCakam2ZVOeaRYtoMhZAVmNlhqQ13WP3cWVv\nzf/z2k1+rjfOlszZuuNNEWIN4HUoKXXEZU9aLrj+nVeJImisrB8BYphMPkO9+XPYUEpGh0zIwyyL\np6mpqampqampqampqanpvaJmkr2dhgw5+4iahMogc9OkBJkYZdMoogEoD9xc8+ul8I+3F9wGyuGh\nt0hmpSCowBACrxi8MCiPn1lw4ZQbZClGuhhIQehSJKZIjIF+kUgh0KV4PH4ZAturHgEuH2z4y594\nir/wM89g0/dlaqV0k8wNqjEtJtMopRs9bpiNlGI9hmJLNYkCNW0kIFrPW6uR5COdO31icW6HP/W+\nwN/74i6vvnWNV2/s1zIAT1KZGammmaimEKojTg0wB8ib1IQbOLmsJrOCONC6pvpsGrWsSbIQAOd6\nBXd8uLS3Boyj9YZ/+9NP8gvvv5+Hzu3QRR9BHYsEdCwUEMdyhckQHIsGfHteBCp1tLMmyeooaKjJ\nP53OqRptcYT/G6lL/PlPPMGXX77K6e2evk/VuKwsuOBGW6jpOC0+gmpmpCAYytaixyTwwJltvv3a\nTV4U+EwIrAdlCaR6fcQ8wShnd1lduI9wdc+fj0UHQ02T1WMek2SGYFq8wMHMk2SHmx/fe+uPmcb0\n5p26WwD+HBB8ZnOz23s7U3MOYj+zydkigZMI9beB789A5+f2oTOgfZtBrc8VHWb+zmkAACAASURB\nVBROvnaW+g9YnLk+M4C9mzPP+QuXb59YttraOrFsbs+l3N2A8hwgfvYGzBYinFw2d58AdpYnywXm\njnydTx73Zx67cGLZRx46dWLZQ6dXJ5Ztzey3707+9yPOwPfDycfhbSD98/d+rsxhtkRiDtw/t2xm\nF2X2BjY1NTU1NTU1Nd2LaibZCXleSEueWFkmYEHQAhqqcTGt7f/ZDjVpphS6a0c8Ydu8sr3gdoFw\neMRRgTXejNmp8WX84j96/ylOrXo6EfoYWPaJPkVCcth+iqGOYHp7pfO9IgI8en6Xa4PyjB7yX/zl\nXyT1nY8ZpjSZYKG6PpPnBYAdm1zc2TQ5/l7HIaWC4KchyToKGJi25wUAVk0qIUbh8fPb/O0//1n+\nm88veOXqHmrKMkXGCJuqOsurguUtOvDNqmPn0P3i35cwfYL0scBQP1iPua5AiDZx38A/eMs43mlw\neW/D3vqIJ7YSf/pDD/PA2W1iDNMpG57aojZO1vJMynE7wDRiKiMUrF4HT5/h18SO04UxBE+lBcEs\n1NZO32eMcObUNn/l557i5ZuZZerIqnXbgAhF1dNr4tfYanpOTIHIKhoxJR6+cIrAG7xVjK/2gZ81\nOBRYKaQoLFc93fkznNrZJuzdwvqEWMKq0apjUk78+obgg51BrY52eqdrGBqyv6mpqampqampqamp\nqem9rWaSvZ1yQTU7jJ9qpEA1wjxpVGcPibhRdCDCEcrVVUevxvl1YdhkbhblqPjrt8R4KQi9el7i\ngVMrtrpEArrORyu7LtJ3kRTi1EwZQyBWkyzEiAg8fG6XdHjE3/mrP8/OqW2GXJNM4iyzKeU2/tDc\n9PhrxvFBqyk4X2Y1EeejgjZ+5WOY1WwzdZL8aMK5qXUMq49B+OwTZ/hPfv0TfPXibdZDYbtPU6oq\nhFAZ9W5ugWBxbLAcTcgwmVzmMLDpmGM1KseomVWTByq3LDi4PwgMRbl8+4gHVx1/9XPP8eC5ndpg\nOZphhqpSKnhfTadE2xRluyN1hx2PmY6KyX08s+MRTaPG88wzWj6GKQQ1YohoMD7+5AOcu7pPSoGg\n4ueBp4dCqNkuGw1I32YkOd/MDAnGhdM7bNWjuTQU+vqa2warbKSsbA+ZsN5guZClgAkxRjQlRHXM\n6SFSDbI6qRrG58UM1o1H1tTU1NTU1NTU1NTU1PTe1j1jkr3NIMU7p1JqY6GnmYoEikgF93vLpVs3\n7qCYCIci3AyBwxi5sh5YAzkI+8VIAgsTvi3Cb5mxEB+JeeDUFjt9R+p8vHJ71ZNiIMbozYYx0HWe\nDBtHLUN05tj9Z3f5Kx99kH/jM+/HFProfLKAHI9bjjKdxh3d0itjNswNnTFFxR2jI6MrMmHLahEA\nQkQgBDe9ZKSyHY8intta8LmnznP/zgIR8TGaSrdXG804KmjfE1t6x77QyZ9yn4kJFUb10giOmHcT\nM9bXGNWM8zTZOhdSSvzNX3ofn3vqPH1KU0OmmqHZRyCHUqbiALvDJJPqjIlUTpwaMcZjZpm6eajj\nOKn4qJlVTpqMjDUJPoYp4xgn7Gwt+cDWAjUoWSk1oShWz8yOk4pqU8iMCFgIFIwzalw4u2Bvb80q\nwldE+LQKuxh72QhDZmszsHe4ZrFckKKP4ip1dBXqiFo1MOtF9+e+jsMKhPXR3HBbU1NTU1NTU1NT\nU1NTU9N7RveMSbb/Lu7LgLQ5Ynl4SAhxMmkQwcR+IFWGUccy4ZYIt2PgqsBhClw35fbBEZ3BQuAL\nAb6EshT/c1zCwxdOs7OzRYrio5XLnli5ZN48KaTOWWQSvMnSlwfOdh1/+9/9RVarBabe+Di2WY7A\n+8kGq+aRjzkqos7aKrk2dxoUdaMoDAOYeeGAugkYBJZdwFKihEApgWQQfc6wJq08aSYCsabhPvnY\nOUAIsR6TeOskmKevqhmlCikcA/elcsGqf+e/V/6Y88kq68ujbRQTxmlFqkUWKjvuz37oAT7yyFl2\nl70XMKj/KkXJqgylMOTi6TOMKIaoMdhxEq8LgkRBYkCreSZmzpuyWnYQ/fhCGDE4XvpAvU5a02Vj\nNk/VJmOuG8dRxyAbI5JIJs6Zz3eGCSgXMU7tBE6d2uLw9hoR4XVRboXI5xTOJtgrxt7+IVuLBckM\nK0oUT4mFEEGLX2uOU4FRqIUPClpQMza3b3PkV+Mdec81NTU1NTU1NTU1NTU1Nf2kdc+YZFfe7R3e\nuEm6cYszfY92PSVuyMVR0ZmRD13H4zA2EnirS9wMcE2Ea5vMwWHGDL4A/IHA/cl4JLq5sxng7NlT\nfPjJh7iwvaxJMaHvAiE4CH9ML8UYoI5QhjHNVc2y0w+t6mheNamCTIwpGZlfOPzdTOs4n6fFTJXN\nZk0+WHPxtvJ7h1u80J/j/MNPsb3aIqVUgfFuoO0dHrJebyhHB5wZ9vjk4iYfP7Umpo6+i3QCMVRQ\nuTjzijC6QiMDzEYImJtmtckyhXFE0Z2uos7Cohpppk7PUs9aeQpsTFdZTWnV0clRBXjw9Bb3nVpR\nipFV2awzm1xYD5khuzkmZvzzg9O8oktKTCz6jq3lgtM7u8TUUVTJWtgMmYPDI3b3rvCRfo8ntzOL\nvoMQpsbRVEdBRYRoXkBgwShqRKQeX7Uvxc8rVMh+nCY8DbfrxgKDiISIBOfRhZiQmFDL7KA8+sB5\nLn//OosFrKK3h/6mwpCNP5EC8WggD4XNZsMiJRarBR0Qlx2Wgo+8jsw6kZrBsym6FwJce+0N9nn3\nLbJn3uX9vRNKc6T9eVT+iSVhBkQ/N/ZqM+BwkRniOaP5+oeXnVw4x0afBZ7PHJGPUf/hDc4tOnmM\nc+c3B/ifvTaz1xrKDFJPZ47xlWt7J5a9tX/yxdunl3d1jHPXZq6MYR7wf3K9PAPUX/Un/xnf6ubR\n/et1PrHs4bPbJ5b98rP3n1j2+LmT653eXpw8nkV/YtkcfH9Oc0D+uWf7h5KdvD46s825son599rJ\npd3Mc9zU1NTU1NTU1HRv6p4xyd5duZE1HB5iO0ssRG+2FKFMHCymdJMRGMxYi7FPYC8X1rlg5mbD\nNyM81kMXhT5EYhByGji9Spze2WJrlTyJFSDFQCAQJdRUVh3zk1ANi1jXFTdO8ATVmCKDkYsFIwTf\nvIaRUvxrLYpqIZfClTdu8o8Pz/DKmcd47INP8unz59lerTD1dJezvwwrRh42bPKGzeaIw82a3zzY\nsDz8Dh9a3mKQRTX2AhFzt4wAEv3rcW6vjiFO8HsEKUMd/TNvuhQhZf8gY8GNPg21aXFqE7Up+TR+\n6Iw1xjamyTyl5n6cmifHhqwcrjcMg2JaOMiB3zk8zbWts/z/7L15tC3ZXd/3+e29q+pMd75v7Nev\nW+puNRpaSLQkBiMEUpAsiSkCJwaSAIuseC1hk3jhOCYmjsmKWc4AXvGQLGKCY5tgZ4EdFgJZ2AwW\n2EwCrFkt0eputdStHt587z1DVe39yx971znnvlsNjaWW9Fr7s9bpc+4+dapq76r7+p3v+/6+v1Oj\nIZsbG2xsbTMYjCnKMq5hCHjvqZuaxWLB5WsTfnM6570HV3mVu8zdkxpvBjii03DZJCGZsTSNd+WT\nRmLLhPgc10Y0utdSb85Y6qgxX450/Y21GOMwziHFABs8VmqqahB7IqQDO1EGBVwFfrsNvMkIbVAW\nPmALKBS8V1TjF26Vm76cSmffi5MwCv7oKHvIMplMJpPJZDKZTCbzvCaLZH8Ei6MpurtJMAY1BjWC\nBkOwgRCiGCIa0728EWbWMlWYeo9PGsODAe7arZgUFiPgjMWrslgIW6OKzcmIoUuZXqK4TgSTVQaY\ntasgfknB/ACS3EtddpZKDHvvAuZjSaMnBCFI+tkrQT1N3VBfP+LvD17Ol7z8Jbz+1D6lc1FQ8gp2\n9S/mGpRgwVlLqRVhuMFGaNgcHvAr117KhxdX+EY+jgWMlKhNDhFrwbg1S0oK4A8hvu7qVbugsbCW\nk2UUG4jiUtDUPVTBpK6PJubDqZI6ZuqqIycaXSYpG0yD4ltdCmSLhSd4z4OzEb+82OL05pC7zp1n\ne+8U4/EYVxQUziVNT/He471n6AfUTUvhCrZmRxzOhvzb2S4fvf40b96/gTdd+WlsXqDpHlFVnLXx\nnjGC+phV5kN03XUNC0xXgtq5udJzLLW1USA1ButKTOEQtVhxjIbVUiy0qeTUq7BRCDMLi2mgMELr\nPYumoSiLuD6d4EjXbEBS+WgUXWPdrMGFQHs4fU5/1zKZTCaTyWQymUwmk/l8k0WyP4J6No3CmHME\n5/BtA+JTCSPLwP5WobbCHJiGQB2URuF6UD6xabhtYxjLKcVgjKX2scRvWBaMypKh7cLhY+dG6YSe\nlD1lTCeGmSggJXcRJpZhkoQZudnrox6R1DOxjeJUCJ62bTl6+ga/dLDF1775a5iMBilDSxFrsJWk\ncqlVkH0IgbYx+DagJmCDodwsuaDK1VnJP5uPePPiY5ymxtoBrtB0fpLOLy1YYFWCaZNgZl0q7UuP\nsGwViWhACFEMC1FM0m43Xbh8SJ06U05YXAVNLjCNpZbe07SB+bylbVrec7TJB9nk9p0xeztbnL9w\nkeFojCu6xglRmAwa8D46yYJvcXWDoLiipCwHDMpDrjnH04fXOTWOzixrbbo/omBprSRRNXa3lNR0\nABOFsvQyXuNUtWOW5TvJRSbx2VqHMYaqKlA1WBMYFGXSGdMxpFt2y0iUx6xyR4hibtsGmqbFpqYL\nmgLUJAXBqa7uu+hWNBRNQ9C+crBMJpPJZDKZTCaTyWSeP2SR7BkQoJ3NUWPB2ZiVZcyq498qZx0E\nGmOoVZkFTyuwCMrTm8K53SHblUvlkkKbAuolKKOyZDyoKCSQCu+WYf3xJFJivrEk1WIlkEEsZUz5\nX8fPfK2kEUVCQDXEbC3vqZ+6wv97ZZvi1V/HzuaENkDhOsda2qVGZ1HQ6JrTEHDWUxYWK7BoWg4O\nG8Ybe4xGhxw1Fb86HfHWxXuxdYMbDtP5JKeYSDw3u5bI34119aumE83SfIJfhsKIauzqqD66rqQz\noumq/FRTSlzKlgmA9133ysBi0SBty+/Od3l4uMNdOxM2NjbZ3jnN7v4uG5MB1lp8iOJVLNMMKT7N\n07YeW9SEACLzmB1nHYUr+OXHzvH108fZH8e5duWvkk60ayqgBoyP5yZiMEbxgaUwGqt5Y6mrWImO\nvCQ0GmMxScCLpaDgrFCURewsKjHk3xqTxC6DcRVPTmpO3ZgzTq64uvWUtl2JiykfLXR5Tkaic1IE\ncRY3X6TLkAsuM5lMJpPJZDKZTCbz/OWWEcn6AnSfW4T66JB6USNVQSgd2lp8sEl4CTFyKwllUyNc\nUWWmwnUTqO8ouWMypHIlVTnCq6AItQ80qlz2no3KUW5u4Swk5WVtpp2IxJqYJMcFp6U4JmvbJIJf\nhuOL1NSLltm167zzUeHSq76Vr/mm+zAmdqGMmWcpL2vZDTM6qFRbrBF+8z1P88N/8yEuCWA8RRFL\n/zS08VOqLA4uwacG/Pdv8/z1b6lhUERRzBAFvW4+hlXLSpPm2oWHLbOwTFIh0zxC7LTokBTkHEP6\no7SYPqddsHh0nvk2ULfKdN5wMF3QNJ63/sQ1KAQmggwcDEaIvUSwU/AeGoUjZX/T83/+r/dz4cIu\nR9Mm5pIlkbEoKuq6ZrFoWCzm1Isxxjh+53CXxeUneOvWVUYDwYpgBbAxXyzGsSmO2JggXmKDEIP7\nnU05cAjGQhCJi5U6mxobyy6tcwyrAdVgEIU6V9EAFUpQg1FwIqiUGFMy3DjFI3vK3rUrnL56DRY1\nhTWMu+6gIngklmDaWFpsrMEWBZUYNj75xOe0u+zzDWtPhqj3yY39AeXPNkz8MxQwe8LIe+PS+0L/\n+7brCWDvC6zvDdrvG+rZn/QE7/c2DHiG8/E9Qfsffvz6iTE3OBlOX9iT/+s8amYnxnrn3DfW2/zg\n5PlNquLEWNmzht73r8OXv+DUibH7b989MXZh72RI/6hvHdzJwPreS9p7w/cM9Wyn4dmF4vet6zMe\nXPv22XetTm7V35Mi/wNCJpPJZDKZzPOFW0Yk6+/V9dyigD+aYsoY2q8mPpPcPgqISWVsIixCoAlK\ns1WwOR6wUThK5xAjeAVP7FQY2pbCCAWCWAvOwLKcLYlHQLIVHRfAOquXrglnnUjWbashCWhpP0Hx\nzYIPfnLKw3e/nje87D6q0iaBxS47aXb7V9+irVA4ODhUfuzvPsD/9xsHDM6PGQwn0Laos7FMsmnA\nVOhiznD3FLPJp/jhX7zKX/+WK6BDlgKYgWWHy04LTN0Tl863ThhT4p0ZQnzfy8pdZkJ0uQXFGLN0\nQ8Vqzpj1haYctaD41lMvWprac2MaYOs8brKPjk5DtQmTPQhKUU0gKDEZzHLp4Ap/9u2/yw++/U6+\n6a0v4WjaIG0UMrUoU4OAVJaIMBxNCKFlWu/xnhtTXlfWBImuL6OxA2qU7zrHmCStMJaPGkklmcvL\n1l0TSaW0pFLK6KJz1lJVJcZaljWaKVOsu5ZGYjacG0xww4ophvrgABuiOzCoYlNOnKT7So1J1wpM\n4RgtatzV691QJpPJZDKZTCaTyWQyz1tuGZHs/OfhmAoMDw5ZbI1W0lUnlnWlliq0Ao0RWi8srFJu\nVGyWlspK/Jd2iaWAlXUsfAAc88LhRGO4vXMQhFVXwTUX2bo0kUo2o6rC2vvrD1b/IO47x5Xn8Ikr\n/KvJi3ntq76CzXGVuiXGskAjJpUFxgyyRoWt7QEf+th1/vIPfZBPTFsGd5/BVYrubEQRLQDVAG0C\n6krCrAZbsLE15GBwiauHB+y4BopiNQWxq9fhJiGv+yf7zk0WdJVlZtP7akFakNjVUzVEN5wa1HtC\nGusETO8Di9qzaDxGPb/wEaHcv50w3oGdM2BLZGsfFnNkMIl5Zq5CGrDDknmxwQ//tUd48OM3+Ivf\n/1XMZjW0EJyjSGtvknxULGrGk21224ZPLPb5xOET3LkZELGQ8uWS+XClb3qNhgZNQf26drUlOrtC\nd1WXImZ8bZ3FGGEwKPEhxOGuc6ikEl0NGGNxgyGjrW10PGE6PWD4+JPUw7jeIXXjFKIAvCz5dQ5x\njr2DKXtXr7OZJbJMJpPJZDKZTCaTyTzPuWVEsmdXcPHZRggHB7jS0KiuwvqNEELqCJiysVpVWpRQ\nCqMSShOzx5wYvChOHGjAopTWUhiJWWFiYuZZWBOJllFdsnJY0T2zEspkXRRb+9kDkvajgeADv/aU\n40Xf8DrO7W1jbcofMwbrXCz16cQl79naGvHIYwv+yx/6MFfKIfbMJmZcElyLrSy4Al0E3MDRBsFY\nSz1wKIZiZxc7HPN33jPlr73xwys3WeeAIznebPe6E3bSRHRtzCQhLZCyyixoEcsiU65bJMRMslRi\nJKqpK6VSNy3NouHRq5afemSH4q7TMNpEtrYIahhuD6gPlGJcxlLTcoif1ehkj2K0Rxhv8f/8yieY\n8/v81b/4am5cn2GsTc7G2CwhoJRliTGG4WjCbtvwO08dcMfkMGaDdYFkoulyCuJjkwbfLU1aAmOi\nmBY69xgsSyLN+hixjM8YQ9OEZS5bJ4rG9yvEFjhnGY2HVONdFvMDFo8/SdEGQghRICVqtDYdAxFw\njpEP7F25BgcHn6ffv0wmk8lkMplMJpPJZD535O++fwzt4RR3eISGgKKEaOtBTRTL1AhqDZ6Y62RL\nYVgYbBLJuq6WLolSkrK3SlcSQojlm4VLjrLkKrM2OoGsie+59J5dezgT3zdJZLOy6njZiVHETC/f\nev7wjlfxotsvUlUF1aDClRVFUWCtxUo6L9EoPhUDvu+/+T2ujQeEU9uY3SGyN8RNSrBQDS12YKhG\nlsHAUA0N462CauJgY8Dk7Ab/y2P38NT1bQjNajGNjednzeq1WXvNqlPnsfdFVs0KTJq/mKWjrAvt\n7xoAdJWcTe2pa496z7se3Yf9U7AxQkZDylFJNa4YjgyuNBSlpRhXuFKgsLiNCs5sY/ZPIfe8kH/2\n6zU/9U/fy9b2KDqtTMxks9ZRliVlNcIWQ6rhJsPBGD/a5qHDLnQ/lkhaI9gkZMUsflleLpGuq+nx\n8kpJ8+4qVE3qmNkZxqwRfPBRR0y5Z7ErZwzj1xAIbYuxwtb2Nhu3X8SbmMcUQlwoZVWiqUYQazDW\nsj2vGVy5gW/b5/4XLZPJZDKZTCaTyWQymc8zWST7Y/D1Ap3OsXUdHVcpk6zrMhkkdgEMKQzLFnbZ\nobJ7lNYty+GsMThjsaLYoCsByCVRLKpr6bVdiV42dbK0ZtlpM6kkqw6YZiXKLDPKvCcsZrzw/i9n\ne2PEYFhRlgWFK3GuiMHxRmL3TaIo87f+jw/zqVaR/S3sdgUbBdVIGIxdmrbG03VKMTAMx8LmNlRD\nGIwMxdaAw/0dfuah+yDMYdnhktW527U52iSOdeKXSSH/kpLobDdX2+1kbRtZ5ZGt6joJPtB4pa0b\nnjqq+Lmru7C7iRkMKEYVtjCUleAKwTqNl8CB4LGVYzCyVEOD7k1we6cxF+/kx/7mYzz08GVip0nB\nWoexlqosKYqCohpTDfaoBjtsb27xofkwiqBEh5hJz9akUteUxXZsaTrBjC5XTGNB57L7pSzddp0b\nsGk1GfFiWaczFpfKaBWbto9C3WRvhzCZ4H27Fkotyy6t3b1UiWH/xiFcP3iufrUymUwmk8lkMplM\nJpP5guKWKbf8/CHMbxwxbAvCqGDhLOIcJpXKKeCDEoJB6hYnhsI57JpY1WkR1lpQcGKYN1E480BR\nuviiSz7rtB49fh4rP9FaXpmuvbfM9Ur78S1hPuMjH7vCa974Qja2J4TWxy6JXQOCoKg6vG9xWlAN\nSv7he2a4F10kjA1iA4UNGOspR4aA4FuPrQxBA9W4whrFFIaRsyxay87EMm0m/PnDr+buJ2redPuH\nY8i/MxzrcrnWyDO1dUzPSTzscsu0jfMLIZWmSspwA7wgbYuoQSR2gwuqhACHh3OuHVl+5JEvgXNj\nhptbyHiIG5SIVYrKxnLIqsQHcIVl0XjKkcNulOgRWAyh2sTu7SCb+3zrX3qAP/iZL+X6wZwgHmMt\nIQTGE49dQFVUBG0wznIVQfRjYG3qTKkYrwQDGgRjlr04MQhBTOyGp8RyXolZZRhJLjKzllkXn6yx\nTOd1zC4TSe5FB2LTpi1WQFCcMxRlhdveQm8cxu6gLWCi4BaMQazBlSX3PnmZ/UefpJlO1+63zL8P\n1p78t4i+Fe1rbtnfr6/30ydHnqHbX+9oT1vB/v5/fZ0Ze86n5yCmr4Nm31jPOvQ1KDT25GdNb9fC\nKH7fzOHipEPykSvTE2NlVZ4Y6+uM2dsKsa87Ys9nvT85tlWd/N+z6Vnr0HPt3vLS/hTP+27bPjF2\namt0Yqwqe47dcxH6Oln23Z+9nVt79tfb+dP03bF9n+3/c6pvn33r2Hcfi/TdY33X4Bk6a97iNPXJ\n35vPNjemvX10P+v8xG/Pn/uDbH1u0nPdZP85P4aOTj/nx6DafO6PAbFB03PNM3VW/iwj5ck/rzP9\nGH3u//z6QuUXPvLF+/f2Dx59Dv7s+gKmPH32830Kzxuyk+xZEDQQak+5aKPZxhrEWsQmF5aR5AxK\nQe7auYAMVgQr0T2mGl0+3Zep2N2QVdnkenmhrI+tOcpMV5LYOcw6+xHHX6dSyzA94lF3irIqkoPJ\nLrOsjNjlz84VbG4O+Z1/d4Ce2YfNChlY1EIwnlYVSsEUhtZZ/GCID0Ko59R2gLclrjJIEUW/4XYJ\nw5ZfeOyuKGhpOO5y69xkXVbZurts3RFnblqLpT64WgdJZa2kUsPY2RK0bXjocJM/bAooDHboECex\nqrOsCNUGwThsO8eXQ2YygKLEVA61QhCBUpDKwqjAXrwDLtzOognYdD7R4GZx1uGcwRYO5wqcGzEa\nTWiRY1++xKQOlV3JpU0dKQGTxLJYQtpJn/GzsVFBzDRDVuPd/ekAF2s08aFJXwJtdC0uu4DGdfV1\ni5eu5DLE3Lr0urWWnfmCvasH+BtHz9nvVCaTyWQymUwmk8lkMl9oZCfZs0AQmtZTLsA6oTEFzpao\nkZgtD7hWMSn8XoyJefMaRQ+vYVmiF1QJKIMidh+MmVOdeMSaM2x19P6TWgum6koru3FIHSIDR1eu\nc3jxaygKtyoDJeZOkXK8VKP+tL054G/93/8OU1TYnTEaarRtUK94AlqW1KMRo0c/xubD76Z44r2E\ndobduRP/im/g8O6vRgclRWXQwoH1/PzhaX5oscWZahHPy6Tz7eblBLymDpaQpEPQJJR5YlbastTV\nxE1C1/dxlacFQli6+wLiA799ZQOkBTymELQqWRQjNpsrDD/6Dtwnfg3jZ+jwNNM73srBi9+I3zwF\nWkfHl7XY8RBrhjSXZ8iZi/zB+z7Ga155Hj+rCRgExTpHEVrEOqxzuKKkLCccToW90qIEjDEQfFSm\nrYFOzGOVSQaaxFNZu/Sd6Jqek2AWN1ea2uOI/Q1UlRACXqL7LzYw8KtbKwTaowOGAiaE1BHUI6LU\nwFiVi5evUV69Qav+me+/TCaTyWQymUwmk8lknmfcMiLZ58vytiwv0phBv7Gzw/TUBotLVzDzOoa3\nSwrqB0IbBTHVFmsLrLGEVGIoPuVmrarlYmaVtauSwqQRrZ3AybKU9W6WuiauydpnNTrJrlydsvGa\nl2OKAkhdLSV63iQFuCvKoLS894ED/s2jC7ZfOaG9dsDwfEUbHO1BiwrUree23/qHFI+/n2AKtBqj\n5ZDCHzB8z09Svf/nqf+DtzPdeTWLdgGl5VEz4GNHd3Nm671ruWTd1UzzsroqtTRpXC0Ev3KdBY1i\nWSewHcteSwKThvhjUEJQbiwcH263oIgLXjc1xU7J6Y+8g9En342Kwdsh6sbgGyZ/+NOMP/5zHL3u\nL3D13jcRyppiVOE2KsJhwKviJo5/9I4n+brX3sl03sSOkgLGWIoCQu2xENxqyQAAIABJREFUNjnK\nyglXfcm+aBROVRExGKtoq6lhqSCdUEbMFAtELdGrINplrkmabhLI0goaEcQZPBCCosklaI0DU6II\nPiht08alC4FwOMUbaNoWKwEFmqDocMBdrXLm2gF6NE3HSB1Ds1iWyWQymUwmk8lkMpnnObeMSPbE\nZ/j57iv+nyw1IDrB3Ll9qtvPMLj9DJv33sHOuR1mD3yMGw8+yrVHPk17MMOq4IDWCGJsdOck1cqJ\nQcTQpGwVo0JhHSH46CTrhCBdC7hfncKaa8wcn4R2opqsObGSWpacZJebEXu3XcBZC5hYJiixRFS7\n7VQZDBy/8m8uwdBjdY6vhPpwhtsuMZuO5qjl3Lv+Hu7gcUK1gaSuiaH1tKZAyk20vsHuL/0NHn7d\nD1BvfzlFaGnOnuaXj+7htfxePK9jTrI0UdEogtk1MSzITQKZWYlksnZFUwlm6geJhpjx5X3gycWY\nB6WEwkNomXvPhQ/9LMVjv4cvNuLHvcf7BmMtWmwCgY1/8YNw4xpXXvlt2P0xoYbFbIGUBiYDfufd\ngU9++pDNUUnTtun0YmltmC1wzuC8w5UVVxliZEYIiu3KQpNKGp2FLEP5jYnuviiCyWqFjKDJ8ddd\nfCXlyQHqY3dLk0p5jbEEoBCDRn9ZLLcUCN7TxluDum5RVZqyREdjqtP72AtnuVyOGexdw984orl2\njWY2+xP91nw2OfN5O3Imk8lkMplMJpPJZL7YuGVEss9B5GoiihBuMmb7dfdz+1/+bsqze2AUvI+P\ntmH0mhczmi84e3AIT11i/uAn+OA/+pc86Rs2DNgUvL4UNgRcEi80BBrf5Zul7o5qgbAWVMaagyzJ\nJesllUvnmHR1dnFgGQqtcPkS1+77eu45d4airAiqqLE4u3IGhdYzHhQ8+MiCv/IPHmP3FbcTimu4\nLYufzllcWdCc3uKOP/xViuYGoRzhhpu89pu/he/5tjezOxnw7t/7ID/z0z/FYw+8n0efeBL3M/81\nxX/1G9j9C9ha+OmDe3h7fY4zhiR22ZVQZiSJYN06RVEninxA27nF2iiSmWS18xodaD7tU33qPgqh\nhUUd+KWr58B6zMaEsDFh59PvJzzxQdzGLqKOe77yTfy57/0uXvqC8/zq736In/jxH+fTH/5tmFxk\n/P5/wuHrvpHpPGCmLWZvgE6BcQGvfAM/8qP/ir/7P7+Ww8Maa5S5gYMbBxgrFIUhqMUrXBudZe4f\nYugsIgFjBeejayyEOHUfosNsmW2dOmEKgleDEu8js9b1khDwIUTxUxVPvDXVGHywGIkOMmsl3VIB\nCXBU1zwJtAb03BlGO9uMNzbYmowZjUYs9nb5+L33MhqPmGyM2HAlO09e4uCf/HMOH3pk7X7MPFts\nX7p5n1pv7cmxvo/2hZv3hZb/SYL7ewP5+3Z58jj9jQCe3f6kr2FAT1C7Dye36w/4f4YQ8J7lefzq\nyZD+az1B5ePy5HVZ1IsTY204+dnWnxwLPdvtDIoTY3XPZ89vngxtfvNLToaFf8ltOyfGADbG1Ymx\nou/+lGfn3e5rqNB738nJNdSeC9jXhEDo+WzPMXrPhWe6T3rup7599u2wr4HBn/Cf3zKZTCaTyWQy\nX7jk4P5jxL/ojr7khZz5T9/K2f/8bZQXTkeBLCQXkzVQVjAcw8Ym7O/BnRcZvOo+3vLNX8nXvPwu\nSlvik2gVA/IN3nuCxtJGawxGhNKa1ZcRw+ov3+sB/svgelkL7u+ebwq+X5Ygxt00h4eUF+7EWZec\nSykrLQW4i8TzG1QFv/X+G7A5QMXDqRF+WiODAh0YBpcvsfGBf4sxQmmF+77yNbz9O76Rl962w7mt\nIf/RG17NV3zt1/PU1RugitiCjQd+ASYldTA8KBWPN2vdRjqxrJuDMVEgkNSk4Ob35KY1QW769mKW\n2W5RaBJCUN47jZ/RtkVMYPLE7xN8y8G1a5y+6yV833/xXbz5/ru4uDvku//0q/gz3/7tVNvnosNP\nDFu/+ON4WyI7FaFRzNaQ4swEt7XJux+dMDuaUlYWjLBYLDA2hvLbtK7GOEw5ZBFsnBcSdT6Jwsly\naslJtnydnHImRbEZSTl3XZ7cmi3Stx5XlDjAmhC1QyPJTRYdeD7o8ufOfRbO7DE6tcdka4vRaERZ\nDXBFGXVX39LWNYujOYet5/DiBXbe8np2XvVK7GjEM8ksmUwmk8lkMplMJpPJ3MpkkewmJvfdy6k/\n8/Wc/o63MH7li1JWGMnMJVHssDYKZdUQRhuwuQX7p7hw/0t53atfzM5wAIDtnD8iGDEounQCWWNW\nAfomCT/P1NlxPX/rmBDWM94FkynMDqdUZy8k0STmkXUCnabPuqIAsXzgoSnsFsjYob6mOjekKgJ2\n4Nh74sNxyoXFlJZXful93HlqY7lmBnj1K17GeP80IbQIwvA3/wEHTaCiBWO51pxZVViKiZ04uzla\nu+puuRTHbNzGysp5tl6m2b1cuiC60k2JgqRXHmkc4FFrmFz7FG52GcQwnU45d/EuXnXvHceu/eu/\n8hXsX7gT71sQGH3knVRcg4VQnt6g2B2iR3Oq/QkMT/GpJ2ZUpaFufLo9UtdQu3oWVzLHIRJFLkwU\n9DpXmDGCWMEau7wNotAW3Q6mEzMB0GXXS03dMZWQ7q3OkGdQbDTiiUVtgbXRpRIFW9g4v8Fkf4fx\neEQ1GFBVJda5uJYp7y60nqZuaGczptMj/Je9gp03vo6d+1+BLatVVl8mk8lkMplMJpPJZDLPE7JI\ntkQZv+Qezvwnb+XU295AefE0+OZ4iWMnlIkB66Aok1C2CZu7cPYcgxfeHktaNJZbBmK3QWsMNtUC\n+uBp24bCRiHl2P474SsV3C1LEpedHTuR6Ga32Zp4lhST2cIzPnU2uZosRgxG7FIoCwjGGg4PA3/4\nqWu84GzF1rZlp1KqqwdUE8to5Ljt0+9jc2dMVVmGQ8tkPDpx42yORxTVcHl488QHmDzyHvygggoe\nqE+t5tmJf517bOmGcyvBrHPLmbXtu7UxJq6P2NS4QFadPrvsNg1gBlBUUAwZfvojqC1TiathOBqn\nnLYVG6OKyeYE68BagzM1uw/8Nu6ePaSAcPmIrYsD7FYBmzt84IFLNE1D09RIWl8x0fVlbXKTuYqF\nlFgrS4m0c4Z1AtjytloL5zfd+0kElK7EVjVF18VOqdFppulOsWBKvBhUHM4OopuNWNoUfIDgGY03\nGI0nDIdDBlVBWRbYwlG4FGynihK7YtZNQzOfc31eU7/kXnbe/Hr2v+6r+0sIM5lMJpPJZDKZTCaT\nuYXJItka+9/wWra//sux2yNommjNUY2Cy7G8ki5gPznLnIPhELZ3YHefYVkSQoMPfimE1D5mZhlj\nMXS6TxJB1l1SSxGMfveYrotia9uYm0S2EGikZLCxiTHRydY5iUibGQTjhMvXFvzyey5x6ckjmsvX\nGRaWje0h41nD9uKQTX8J6wTVhsPr1/j9DzzAlcVxJ9F7P/Ywh1efjk0LULAlo8c/QDkAZp4HD0Yn\nz9ek9TNm7XntIZ2rTI67zLr9dNchuchiY8zUACEozA5BHAzHuBsPp1LOKIA9+sjH+cSl68fm8P6P\nP8aVJz5FVZYUVjDVJhtXfg+Z1xRO2bhzQlPD0ZUjEMeDD10hhJDWN15L6QQwEwVIYyy1rTDpmkrn\nDJP0vsTSSEmfN8gyLyfqh2nfpOuVwv+7TB5VoXAlnthmQjEUtsJai7EGaw2msBgXf3YEJpsbTCYj\nisJFgcxZnJWlmU9QVBXVQAietmlYLKYcHM5Y3H6BzT/1Gra+7BXksstMJpPJZDKZTCaTyTyfuGWC\n+58hjvmzQPyiv/Wa+zjzvd+MiEK9SOO6Ese6LpM3Z2KJJvHFwt5ZGG2zeeY88uBHsWJSaLDH2QIf\nAiHE8s2QQpnFWlZlk2mfnZIlytJRproS0NbPJ3RTSE4qk5xUweO3LzDeKDHeR+ePWe0+hBgGX4nw\n3g9eJbQVh4Mxh80Bn/ro0zDwMBpy7sHfoP3IA4TBJpLW5J0/+aP863e9g9d/85/l4oWzvOud7+QP\nf/NfYEKNcQ4UQrXL6N3/Gwdf870wNPx8fY4fJZ1+J4p1ZZWkN0IXwpXG2jW3khIVnLZzkR3veBm7\nhCqqLcFrDIUejGG8z2B2hWL2BIzPYRAGwwkP/ta7eNs3vo/X/Olv48tfdT/vfOc7+chv/CJaTxFj\nQetY0vjzP0bzhr9CW20zvzqjnVr09ATufhl/8JH3MZ02MS/MKAawIWB9ixYwCC03XMnUThCZxl6p\nBkRDFDtTDpmo4kVQVYIFH8CLYFRwCAEIncts2dshoF7xbUtZFBjAFSOsq2JzhqLCliXGOsTa5JpT\nhtaxu7PDxsaE0aDCFQ7nLKUzOGcwRhETEDyqim8DooFmNuXAt7RtzeTOC5z+nu/g+gcfoJ3PVvdk\nphdrT/5bRF/w+LPVHJ91Rv8z7K9/+ORO+0pqe0+7N6j92c259xh9Z9cT5t/2bPjMBseTb3ziysHJ\nzxcng+37dtk3l9CTEC89221VJ0P6F83JkP6Xnj8Zvv/6e8+eGLvn7NaJsb6AfgDXey/2bnpyu97B\nnrD73otwcqzv/+k9lzlmK9582L4PP0Nyv0jPteo5b9fXlKLvfu/9/ct/BmYymUwmk8k8X7hlRLKN\nP36Tfy8UcJsb7H/Vl8YGXG2T/gae3EgaWFPEWGZqGY3CGCEJVgq2gOEIVw6BgA+ewsaOjELACLRB\nMSIUJoa1n/gyuV46eEyMk9Xfzru/uAdd26ZzVqWfg8JoA2fBBLuq4lzbHUTB5ekrc5hdQa85KD1y\negNdHEFQihtPo65YO6zBmJKjT36Mf/63/4eYedXOsc4hpki5a4oYi1z5IOb6IRQbPDi/yUm2bD4g\nqzUQYiuyzmFm0/pLGvM9zrr0eU2liKROkKqALYGC4vAq4qro0BJBVTC2pLn6FP/6H/89fu2fVkgz\nwzmDSCo57GbbgLv8KM0dp3CnC5xC8wcBrl/h8lFD0yjlwKISs8FELGICNA3WKNY4vC2XGXAiirUW\n70MK4Y9FmF2gv6IpMy5+iTQp0t9ol0OmK1OhBuqmJaikSy4UIjhn4/obMM5gncMWBUYEZ5TBYEBV\nFLjC4qyhdKk8tDP2iSLi07GEoC1N0yAGmsYwn1oWu1vs33sXh+/7YBJP85fETCaTyWQymUwmk8nc\n2twyItmp52Sv0cdQ3XsHk1fcC+0i2niCj+JMCPHn5ff/VPYnBmwAE5LAY6PaZBScxZUlIFhjIGWS\nGREkgE0/t77BMEi7XVOw1l8fEx96FK5uk+W4iYJS172yGmLWorzWI8s6Hc0AT99wsHMWNgfAIfrU\nIYwUdoe49iiJgQAGVUExGFdQBBM1rKJC8aAexaOEKIA1EMIV2NmAKp3sMaFsLY8sOaswJp2YgZBK\nLDU64441MWDteVmiKF3Pgujgm2zBeBt3fQ4aSxlVDUENorHjpC1SfJlzce3UAx4I0YHgobDXaDah\neQL8dWBgYHefK8VZWq9xatLNLwby65qAqeJSBlwcE8CIAauoj9cgiMZyy7Am/mmadqebdvNMrjOC\n4tvYpECpsMZSFA7jCpwzFEWBdS49FzhrsSLYqlg6x5yVJIwlc58ESMIdGggaMBhCEHwbaEx0oxzV\nA87cezfjD30UbZtn/RuXyWQymUwmk8lkMpnMFyq3jEj23JVbgnvZXdi7boPWg2/XHgF8VwYjqyyw\nwgFlfN/a6CJTQDxYi63KFGcWoqsJjaYvSEHrgrMFs6ahrRsKiuUhjnOTQGaSWrKunenaJslJ1ZVl\niitZyUg9JD3mYLYAZ2AwgG0PRYB2BkcNHBysyh/VoFrER3B4daga8B4jDSo1IjVoE0+rgbK4gR9B\nfXDTSRxzk3UtG2Xl3OveC53KZ1brvC6QJUFJ0851KZQJ2BGMtnG0iHGpmtMhUhK0QNWh6kCFoOnc\nadKjjYLREOSTT9PcCcUW2E3gcfCXhVotrQ9r00p5YUt3W4hh/cZ0hZUxe6wz/KmmQP7oHgvC0r21\nXiEkgDHRAbc6mOJ9QNUzbxY4Y7DO4AqHLR1ibHxtXVo+g7GOwpoo5BYudTuV+FkjGFGMUayEZZOA\nWHEs+FZj4afAwgj2yGHuvhO5eBv+oUf67q5MJpPJZDKZTCaTyWRuKW4Zkey5wr3gAsXLX4TZ3YjC\nUF3H0P6mgbqBxSLlgXUdLR2UJQw1/kxIpZcBggXrsSJL90+rik2KhxGDdZaF91jnIIBfLCgYc5OC\ntHImrbMsw2RVEdiVWh5zlEURyTi7MiXJql8mrIxbAKFdQD2Fegi1IuMC3SiwAwPl6kCqJRoG+DAk\nhCHBV4RgEGkJzBEzRcwMIzOQOVpAc3REDWvNOpOItwzoX5fxlOjKS7lkITn1lgH+YeU667o9diH3\nsBScFEmCWAV+APNFdL+JJTDEtyWqI4IfoFoSixrnYKYYmcc5mAVKjbbgNhaUZ8DegDADnQOzFuoj\nQthMbkJNl0FWlyiQstLM8n6IJZkxYt8TM8DC0mCnUQxLOmEX1N/JcKvLHyW3tq4RA4c3DqlciwSP\nBI8JAVsWuLJMYlmJJGejMyDOYVIHTmckCmPd5aAF9TE3TYBWURE8Hm0KVFuMQO0MevE85d130j70\nyPrNmMlkMplMJpPJZDKZzC3JF7FIFhUid9sp3MWz0aVU11EUmy9gPoXpHI5mqR7PxC6WVRk7WcpW\ndFwVKTtLbAywUotxMWNLAAv44CldiRjHom3ocrFQpa2b5JBaK0VcsiYGrZ/2iTDhTqBYE9GMIbUE\nOLnbbhdpzJUjmOzBxhgmEksjXRJvlo09haAVPoxp/Qah3cT7cXRiUWPMAVYLRAWsR2hQO0YGJu7P\ncFOZJUkgSwLYcr66sg0eO+ljquBKINO0PzpxKZUiioDbAjdKTrhUahkqNGzgfTz/EIaAQeQIYw/A\n3MBE7x/GNhhXxPLPAFKA8eA2oN50MNhBUn6YiCGWaa4WtvWpwLJzukkM/I6CWXRu+cAyg6yTCY1Z\ni8LrzHJLh2DcKIRA0zR4VQ4PDhGjWDzazEEEsQ4ZKK6wGBNzykwqtzTWYp1gRZfCmTGKtVCKB20J\nwaMhxIBrBQ0taj1CoJnHMHp/+izutrPIYIDO5zfflJmEMc+yiXBfSHhPQrk+SzFSe4Lk006f1T57\ng9p7Dt17mJ7t+sLWtSfgv+/8fM9BrLUnt3uGEPq+oP2HLh2dGKvKk6H6TTgZqh96JtM27YmxvpD+\nuj653f13nAwUeMOXnDsxdufZzRNjG6OTIf3mGe4R0xNu3xdi30dfk4XeFPtn2fDB9AX892wnPXMJ\nPWH8z0ToO3Y4uc/eW7FvLj03meY+4ZlMJpPJZDLPG76IRTKwe9sUX/Zi7B2nYTGDg0M4uAHXD+Dw\nCG5cI0xniBGkLMEV0UlWlLC7A1tbMJlAmcYBUFwRhZtAIKinKEaA0IbAeDCm9Z5Fs6BezJk+dZXJ\nC8/FUs1jUgkkVWX1t/elQLa+3Vox5dKlZaEocc2N5ce6TderM7uPvfCsi/Odl1Ap6gAb0MLhq22c\nKkFKvN+hrs/S1Oep6/M0zT7BDxE5wBVPUxSfoigep9AnsTJnbm8jfOl5ij1oRukkOnHMllBUcU1N\nwbL1JsRvNcO1Ca+V/kXBykPbJtdfDZeeQq5dwYaryLzGty2+bWH/Apw/i3/qHPpwA0VF25yirc+y\naG6jqc8Q/DZBC6x9irJ8kqJ4DOeexLqnwdSY0FDrKeor0NaKrQP+mofrntvkGsPR9toXxXgtNJV7\nxi/2QiUBIxZFsGJiuSW6NMX5NHubXGIBwSZzmhXBp32KleUSNk2DWDg8mnPpU48wKg0lU0paTAtu\nYfDBQ7uB2z2FD4qop7SKGTjKwlIVhsIpVSGUJkBTMz06pG09rY/ZeXEKmrpgxiYEvlgQfMM1fxun\nXnwX5b13sXjfh9bux0wmk8lkMplMJpPJZG49vqhFMtkYY3c3o/jQptLK2QymMzi6gZ/PaSWWqVmj\niEkOJVW4cRgzzEKAzY1kG2vBRudOJ+pYMSluy8YSu+CXBqgATK9eX+kK650tNQlEx7pdwjKXa90K\ntvwM8Q1jwBaYerqqSFzTLnTthRc4vStwdAD+dMwmmzioFEYV7d4ZBp/wIJYQRvh2i6Y5Tb24yKI+\nT2gHiDmk1AFCjTVTvLmBxTEd7zLb2ocWzrXA5jkYbkExWMs5C2kd27Rm63lYevyE1/8B30jMUBsM\nYbID3lPWNfsHh4yfeprRgw/C73uYwZHbZiMEQrBoGNP6PZr6HHV9kbbZAxzWxZJXkSnGHGLCQXQw\n3IBw1xmG+2AOBD+1eLVwdMCZ8hJVeQ8puGu1vt35IxigkgBBsUZiKa52HSqj0Cki0TEkscPkKpYt\nNnswgF/Va6IhCljOwsHhlPmsYTKwWFPixIDOUG8J1iB1TdPUjAsX3XECzgjORBNk5QSrLfV8QV3X\nLJqAT2sf0nEISlAlBCgKQcTgrWM+n+EHBWbrueo9m8lkMplMJpPJZDKZzOeOW0YkW/dXfSb76FBA\nxgPMxjD+5H10JS0aaBaEeoEHimERS+6qAbgyBfWbzs6zCrYXA66F4GIOFRDUEwjUTc2giGWFjW9x\nxqFiURGmNw6O13mYm0SxY+hamNiaa2d9cbq8LmexzZQmxGrQ9Xkv5y/QAucvbMPkFFRDqJrYvbFo\noSho9s/HeRYWDQM0TPB+h6Y5TWjPABM0XKNtapy9jPdDnBvyicPAhRec4juKAd88h1efmcH2bXFu\nwcd9hpDmFNZObM05duyMux+Ph8rH56QCOgO7Owx3dnnBXXfz6xuf5OcWjl+ZneajTz3N5u13I2GA\nDxt4v0fbnAbdA4b4RmlNdMQFjVllmoLcdPcC6sE70JJYQ2srTp/ZZVA55otmeUW60w4ay8F8CAwk\n5nsZWRUOdRlqQhfk370ndMWXoiAmqqnR+SfLWyNmlwWOpvOozxoXu3FisCY2DShE0dAQmhZQnI37\n7sL6q9JgtGGxmNM0DSH4VDAazwBj0BCioOsDgkdEMMbifcuintGWFWY8RJxD25NlZJ+Jr+wz/X3P\nZDKZTCaTyWQymUzmT8ItI5Kd/Pr9meNGQ5gkkSxoLOFrPLQNIhLznEaTKB45F0UYY1fZWpq6X06P\n4nhVgG9jw0YxCIoPIebcrLnDFr4hhJh2Mp1OVye01L6S6qVdIPxNdZJ9OTCStu8CyIzF4Vk0RKGH\nVUHjuvzUAvtbBWXRUB/VEBx226IEgiuYn38hsljA0BJCQdAhIYzxfhPYAkpA8f4KPoyY+yFXHlf+\n9ne/jLd953/IbeNPwsYl4ABaBfWro0vKIPOsqSkphCusT3htjuGm3DJYhSIpEJo0QcNrX7vFa+dP\nc+0lO/xK9X38T+/4AO9/uqTUEcGPQSfATvrghBAmqB9FMRCHYAhn7kDOnGJgwY3BHMFTR8BTlzl3\nV8AVFl3E+6UTl1Rjzo9J+WgDExBNpZYpu6xzm3XbHEui63RSFIPEpp+atpMu2ywKZtPpFAc4a3Bi\nYtMEdbExqA8YBxBo2xpBcZK2dYJoy2Ixp61jtlnQlJVHSN1Zkyuxc5OFgGqIZZi+pakXtJMhxagi\nFAWhRyTLZDKZTCaTyWQymUzmVuGWEcke47PnJNMUU70zHjAaDahiEjr4sOyoKNYiZQWjDShdCui/\nqSOjJnEttDA/Aj8G79HW44PHB0NpY4ll0EBhLIhFNSAC1hgWh9PjZ7d0hclacFgXKCYrYUnkpkTi\ntF20GYErcOLxh1dhawcNEMxJZ08IsLtV8hUXSn69GsJ+hZ8dRTeZg9ltdxHUpRUzqNoU1l+kB4AD\ndcynjnuc8Fe//26+6799KxQC84+nPHtdCXiaJDtJ87JrZaN00zU3ucuSCGjCSvHr5n+sPLXbPsQG\nDBi2z1R863d9G9/0ptfxth/8XX7h9yWd+/rtX4I6FIdiQS00Dc19X0vjLFJDewVoYj+A9uA6L3rh\nZrwVVAjqUdVYCqkBDYoxQPBMnEe8YCSWX0YdVJbzFUmdLFMJbtxGloHVorISR2V176l6FnUNgBWN\nZZRi462Bx4cFhILQNlgrWCc4KziFqjDU8xlt61Pmma7MeWE9725VxRvz1uK2QQO+bmgMLIYDPj2o\naGYzbuYzTSi78Bl+/gsB1xPc3/dnWV+4/LGsvm67niTy3uz8Z9swIB785D6fZSi72JPb9c6ld7tn\ndSrxz7Sbj9sToG77wuCBRXsyfP/hKyebTchgcGKsXZwUf+eL+sTYZnnyf6dNe/LK/Kl7zp4Ye+NL\nbjsxdtv+5MTYsCpPjPXeX88Yxn9yvC/Mv/+jPdevN2n/2V7nk4O9Afg925nec+nH9HWWONnz4RnW\n7ORxxPUc+5k6RmQymUwmk8lkbjluGZEMPnuR4F1El9sYYkcV+DaqRV2HyWIA5TB2stwaQlHEMkuz\n1olxKcaQnDYhdsI0lnY+wxNL1ebtgkoKgioLLRA8itC0LbP5lBvXr5ECqFaimGHti0E62+6kO6Fk\nqWGsO62SkGYtlAXV7i768EcIr/yqaL4K9Hbw2pxYvv0bT/PrP34Vzp2HnUEMzg8t9W13cPBtf4Hx\nz/8sGEWkxppDrL1GG0bABvfvXOYn/8dDXv6Wc7B1IZattrP4zV1IX0jMah6wEgJV155Zq1lcy/kK\nurbWaSPpbHfEwbD+WaJI1l2XRQOLOcXY8Y7//etADR//wJwf+cdX+Mlfr4jtN69i7HWMzDHiQQS5\nccT0z/0limk8nHGwuAHtw8Dl9/MNb7yfw6MoDKmyzPAKPtB6Dx6G0rJftDAPlNZiNEq0imJUlgKZ\nB8QoRonB/ely+hCdZCbdIyqS9EPBe2U6n1OV3W3QYCTKa6oBS4H3M5SGsgD1HifCZOTQULNofdR4\nA8lBFsUxEUNQjw/dbWUxRlFM3DZEcbhZzKlDoBoPsKWjOX43ZjIuimEWAAAgAElEQVSZTCaTyWQy\nmUwmc0vxRd24XKxF7PoSaFSRrI3CWFVAWcVult2jKqEs4/tFkcowXXzd1NA0LOZzghja0CIEWt8Q\n1ONEUW0htLShwYfFMvtpKZItQ6vWhDMxK4HOrItm3eeS66oTOoyNc9jYxDzx+FJj6qbImuakQKPw\ntV+xB49fgRseFgajFtkawHbBta94M3I0R6TGmCOsu05RPAU8zd/5zz7Je/7lU7z8Oz2MAyzm0Vln\nZDWHzn3XOfHEgnXpPNPz8rE21+5hzNr7a/tdNjPo1q1bmzXBUdZEM9L5tXPuevmC/+uHn+Bnf+Ax\n7tr7FFJcpnDXMO4AZI5Q07zwywj3vYzJ+XgrmAX4A+Dhp/jOr/a4wuJ9dO9F81oMu299SA/PSGqG\nVnCiMbQ/OQINgjGyPD2j8Zexc5tJ91ridkZSppkIkkotg9cY6l+DaFg6JFcNABY4ie6aojS0PlCW\nDkJLUzcE1eQMiy7BVTOI9LMIRgzWWMTYtZLS+DsTHWUeVBHTY83IZDKZTCaTyWQymUzmFuKWcpJ9\nthHnokjWOZdIgoy1sbyyKMAVURQzshKzgJXSpCvHUuNhUbM4mmKNQ9VTtw1VUWCloG0XICYKK75m\nsThaE7rWPTg3+XE6B9l6WP+xckziOXdCmSEKSpu7FI88hPchhbnftPs07TrAxTMjvuy+AX9wtcFs\nbMCeYIwSrjZM738Z0699A/LeJ7HmAGsvM5uW3LfT8Of/xj6MGpj7KETdLFYdj6pPL+X4FLupQcwc\nW8Xgpymvl1KaqCiFzk0WWJWgstrRsiRTQNZcghLAayytLVq+9Vsf5/6XDXjB93waGV3C2gOsmSHS\nIv/dD+CmsfHpcDua1fzj0Dz5BF/3H9/GfFbHzpTEsqPAykmmqizqBWdkjjPRARaFLgUjhJD0ztSo\nQUVwqngFNYJR8CEsr6lJQpxBCJoC/43grKUFmuAprVmWDCkeIwZPbB4RgufoRo3dsbSLhtaHZbVw\nXJgQXWoay2BFutK1eM8ZE69FJ+KBLEP9TVeKnMlkMplMJpPJZDKZzC3MF/c3207k6jCpVLFzhhUu\nimQujTm79lj72aaHEfANi9kUZwzelLShZdHMmS2OaJo5R/Mj5s2Mo8WUUM+pNsZrLrHOPdU5xTqh\nqRuza86s5MhaloCufY4k9A3HlAePU88WsSlkN901fa+rUDSF8O1v2oVPH6BVATMhTMHuVTjrufLd\nb8eEKcYdcXl+mdfd/STv+vsBhg00LasWjLByc8maGyydt02PdYeYrM2hc5xJ5x5b2wdrr9fXiLXn\nP/J6d//xoB7q2Mn0znvnfP9brnJ0/QnEXEfMDDm4RnjFV7N3ezQPzh4DjqA1wJWP8aUvO09dt8vd\nalC8jy4yn4SyEAL7doGE2FHSaHSBdWljgizdYnG54js2OcaMsVgTu1UaDEYMRiTpUXHbqsso0kDj\nPVGuU4KCT+LXcOhom5b5wuM10DRNEtPSuaTrJNjUWMAgxmBs/Nma2HBA0r2oJOdcuol0vYw2k8lk\nMplMJpPJZDKZW5QvaicZwaO+C7NKAoxNAkwnlpVFLAlcD53uBBmVldLkUwBX8CxmM5xx1FSE+gho\ncWJYNC0hxEyy0NbMFzDe3k3C11rpoHbHWHdhdc9m9VrWPrBqiRg3sUA5oCw8i6cvMbx4e9SxTJzK\n/8/em8fbdpZ1nt/nfdfaw5nunHkgISEBwhAgIKKoaIsWlkNV25+ybLvUVvGjH9TSttF2aKvKspqy\ntJqqpktL7ZIqpMsBbCdUFBAEgUAgkJCEjDfDzc3Nnc89095rvc/Tf7zvWnuds9fVS0ESbvL+Pp+d\nffa71/iutXPu/p7f83t2ll9WAV73mov48V+5G3soIFc5ygtLKCGsVWw85xrcg49xaASvf/mIX/qZ\na1h8xUWxs2ebESazh2vAXedcGtDVPSez7TljDb1rEu6bjp1NNHlbRimdIOlmDtI1aYGd7Zi+riMt\nXa+6hlr5P3/oMvbvOszP/v4JLhivUiwuMTnkEAcr+2AyhSO3wdYh+IaXB/btGvDoka20G0tmQiXU\ngRBi4wYBDpQVVVUzcjGPzMXEMBCHSXRyOXGghkk875AcWxa0hVgiMazaRAgIIg7DWBiP2tLMWpVa\nHCIhlkQm6Lu8e5FTJ86wtDJkUhcMTAGf3G9Cp51A3K8YZg7TgMO3cxbBXYR1DQTVOmAh0AaYZc2p\nNxi9N9x8Pvy7b0zd/FjPEG0t7/xG54d6FnN96/d21j2nXfTuJPQsKH3h+z3r9jUM8GcJrD+5MR+0\nf7qnGetSXc2NVdX82LjnGH1PyfHXXH/h3Nirrp0fu3T/8vw+huXcWP+9ND/kzvL3L+0Jse/9+0Lv\npe+9yeaX61ms75L2Npvo3e+57eNszQrEz/8zx2x+773/BztrA4Qd657ts3aey9l8w4svtP74zifn\nLyy3r1/whO9jcMF8U44nQrq454nfyZ75/099weXnG5E8EZJd+5/4nUzmG8E8EXILi0/KfrKysrKe\n6XraQLJuIeK5SicVOpmmEkUHvoQy/aNwUMbw/nKQwBnbs8MQCMmNNK1haws2t2Bzg8NHjjP0BaNi\nxMPhAmz9cUpnmBkhTJnUNZixOoVLX3pD3F/zBWtbGWIXLqU3uyWW2lm4BU2p9NADEhjeeBMrH/k9\n7LJ/ikqqQCSV2dnsu9C0hssvW+J9b7mCr3p7je5bovaGlsbKZUOmZwLPue3D3LPvUfzCYZhswMaZ\nzvF0M8SYOcWa8U7OVTI4za6ckoL2uyWsgbaLp1lszYnNSifbyWrgWnMXWPvUbr9dJsGztrxWZ287\nz898/yv4qe/7Sv7T+iv44QtXePGlcPooHLkrrr5xCOxvDvPP3nQjh4+sAkIwQ4MxmU6ZTKZsTSsm\n04o6BESUPd7QrQkDJzgF8Q6V+LNqLLNUBPPg1FCxVFIJXjwoeHOx1NOEkBxluAItB1x6wYUs7F9k\nY7JOWTQGwxHmCmQwZLyywEOHTrJ1Gp79wgvYsAFFqFBR4p58crAVrZutsR0qLjruJII907gGzuNS\nRz1nYNMJ1tM9MCsrKysrKysrKysrKyvrfNJ5A8kOPAHbXNqa4qf1zADlU5kiRGhVFLPSRtcFZcQM\nsqCx1HAyhfUNOHaC+swZzmysY8QOhReOF7lrtWQQAmKxW6GzAeuTCeWuPVxw+SUz51hrHmvKLLuu\nLGgD+pv3PDM3m4MZIErkpxBYWGHX6BBrLjEokomqI01AaauCl79kP/zMfXDRIroRWLphgGzAFo7f\nWl5LgGw9ASebHWtbWpnOw3tmgKxxlHWcZE0HT0tQz5KbrnE6NB08d7rBus65tqVlz7k3Y22XzGY7\nHWjWtUtoDPV3Q/juPce5J6zwzjvgoqvgsQkcfQB4FPjwXwEvTbuyVGaphKBMq+giU1O8GI6KejJh\n7D2FKSKKcy4CtHQO1gTxQ8onI3W1jM+lCFVTpmkgOFQc5gtUPOOFJa68/AoO3n0no1HJ1AznPIPh\nECsHjPfu4sF7TnHgwgPU05rNynBr6+BKBqMhzguCj00ExKfySYjoLjrpGoeGNNfQwCy62RzKaKti\nTwhkTJaVlZWVlZWVlZWVlZV1Puu8gWQLf8f7TZXi56JyMkG2JsxgVAGuihtqOkT6IuVi0ems6CDU\nMKngzBk4eRqOn0CPH+P0xhaTqmLghjiBEqj8mDNVTRGmKXPeOLMBL37eNQwv2h/bEzrfKU0kQa+O\nO6tbYtg6yzoOM+gEjCWAJwoqlJdcwRqwbIntNTyqw400vTcNwAMPwp4F5BX70amwdsa4brewb9fd\nsLXGDD7BtoywZp7Ex0ebIdbjiGu9f+kgEiCKy6RSyNYhltQ4yIwZTGvGG6DWbtK2s7LmhXa2se2H\nEOscNzeRwcf4ueGYX926iM2/iW9xUOGTH+M7vwPWN+I9Eyxmj9Whpqpr6lRqKbHVJVcVZyjqwNBJ\nLFssBhCUohBUNZZPAkqcO6exXCyWU0YQ5UiXkeRAM0FxOOcRV6DlmOuuvprbP3knbsFTFp7CD7By\nyGBpkUOPnsS7USyprStqV7K+tgV+SlBlMFTKAYh5vNiseyYO5xMLbUuJ4nURP8soc3WgnEwYT6v+\ncqWsrKysrKysrKysrKysrPNE501wv/0dDz2HZWaPBCc2puhGyhEQSUHxqVOf89tdZN1wfYj1ievr\ncPwUHDlCOHqUSo2tOnY2NCJwKJxwwXAIqpyuYT041oPj1Cq87FU3xVT4xsXmZBZcj4NBydrWNIK6\nbrliFz5JE+BfxIcfxEc5gHIYO3MurWD33YEWtFFdraus8zPEqtH/559fAh++D3twwsbNNWwKb1+Z\nwvQMkRjpzPXWgsME6rZBMz97z+0EZjvWo4Fkf9sNkFxz3cyx9gQ6D3qeu3eRadxG97kp8dQaJsqo\nvpmv2oRHHoFjnwAOPQCnb+b7vuvGFEGnmFrKIdMIyDQG54sYAx94brHKQjnAlUMoF+L1KIcwGOMG\nI/xgQDEYUBae0nkKXzAoyliqWxYMyoLVzSmLwyHDomDgCwbOU7iCsigZlgWj4YAXPu8G/IpnUk2Z\nmrChxvG1Te6552GOPHis7VLpnUNdSWVQVTVbm1tsbW4xnUwIoUr3bAzsj+WU8fqICE4SGHMxVQ0E\nV3gKM3Rzgtb15/D5O/dHVlZWVlZWVlZWVlZWVtaTpfPGSfZEyLYm2MYkQpYmrN/FkrNZx0o3A0Ld\nMP3NCZw6DcePo6dOEdSQ0lNjBFXEx5Bk50sOLIwwU+45dowjG5ts1AUH9i7w3FfeGKFMs482c8xB\n6Tl2bJU3/tD/zW+87SdSDV4123/rzOq4ysTPSkLN0wbhDwIX3PkXHL/0KhZGY5xFo1kDIoQZJwL4\nx996Pe/+qw/wXz90Lzz3Sn7uwt28ZN8j0WbWwim2g65uF8r20T1OZsfdPu/AIMLM6dXuY+dYwqFd\nIAYdl9nO9xJU613eOusl95ql7LPphB/Rit+/q4SD98BDH+Gtb3oJpROm2uTLRedYFWpMA2JKKUol\nyvV2lKWwRTEcz0CgGfgO1EvPsfJTKRO1jC8NNxxw3U//Fr/+nV/DV99wFWsTxQxqhMpB4R3BGdc9\n+2ouu/q53H7r7djCJlqv4QKMBiXjoU8xbEaoa6raGLiCMN1M191ivwfxeF/gfdmWzIo0SLmZtqbt\nQDwfJ4KvamxzEqFxVq98T2q59dHgnoBy7QkYdz2Wvb7gcOsL2Yfe7P0+Iul6jlH7E9PnhsTN//3F\nekLji2J+OdWeffQcc99y/ix/9nl8dXNurO7Z6LQnpN96wvyLYv5X5+tecPnc2Muvng+Mvnj/0tzY\nqGd7rqcxQd91+pxg8jk2jOj/Y8X8YH+Wf+/gOS3W10Sgb2u9of89Y3H9nvuzZ0d9zS/OdW7Ptu+s\nrKysrKysrKzzT+eNk+yJkK5toqtrUAdAoGicZH7mKut08mshmRqcWYMTJ+H0KhoMc8JgVCKlx9QQ\nF7sXihilL9g1LLlu316uXFlksai59mU3se/Zl8fw/xmFo/nndsB4w+vfzGcOr/ObP/mWmD+mIYKy\nqoJqGrvpTDbiY2sNNldhcy3+PFmH6RbYFJwx2rvMyod+m6lqrCZlxp/aGDCLY8dOG//hl17FP7jh\nBPs/cSdvfMmDII+SIv/TwmyHX3NArHGWdaFe9y2b/wbSsrAuFOtCrAS2lHjQtvPRvJ9ea3rsdJjt\nXHfudYAq8OoLP84/rW+F97+L//Czl/KiGy5ifWMSu0jWgToEqqqmqurUCVIJznOVHeU5doyF4SA6\n+YpiBl27P5dlcvwNYDCE4RiGY2Q4wu3ZzUc++xCX7l/hX73rE9z28DF2L0TX2bD0DLwwdDDyxr7d\ni7zmy1/N3cBkGlCFQeEYOGPgR3gxfAKMirTd3kIw6mlNtTVhMplQ1RXiknPMzRxkzX3ppOm06REn\nFEWBn0zR9U0sZEiWlZWVlZWVlZWVlZWVdX7rGe0k0+OnqR84jJ3ZRPYuxY6WlUZ3V5EypHxyFgFt\np8XJFB4/Cqtr4D3F0gLFcAhXXoG76z7q6QaMFhBTNqcTCj9k9+IyS6MF9ozHXDA8wk+/6cci+KqT\ni82I+xkPeN+ff4zv+9G3ccWz9rO4NOK3bj3MH77yDbz+276M1/7g/wDrmwn8dABQ+0jbMZsdL8DK\ngOVxzfKH/j1VNeLUTd8KS3uReubpMoOQ3E2n1o1f+6VXs3f8CJy6HzbrTk5ag9nSnLRdLHvAWNel\nYDqzsTQ7bNxhuuPZwsxxFToAK4QZ/KILwnYAsZ2ArJmbdtn0c6Dj6krHFTQd2kF++Ucu4g0//tXc\ne6bm2PFVVGMOWeMiq+uKUFWY1lyzdT83ukOMlnZDuat1ZEWwComaJqdfc/5p9jUtNyi59+Cj/Ohb\n/oDjvsT7EhXP//aOv+E1z72M/+Xvv5KgUOAJgBQe1S1e/42vYX1zlT95x+8wLh2joqBAcVIxcAEX\nAlZXKAblCFdvEaoQ2eM0oLYBeBbHK9EFpBIPU6PrwrrXMpVejsqC8tBRpo8dm13vrKysrKysrKys\nrKysrKzzVM9oJ5ltTNBTa2gDnZyHopNHtq2csVkJqEJ0czkPCwuwtAzLu2DXbigHhLABxNIkL8Kg\nHFAHw4nDOYdUa7i9K8lFxjZH1fHHTvIv3/ROLr1yH84ViC8IeNZWlnjzW/+KP/+3b4eFUTy2YFBr\nBHu1xtdqnXD6zrMTKD3s2UO54tj/0d9kdNeHCCVowz+syd43HLAs67B2GOpqRxmjdCaj+zOz/fVl\ng20De90ssA7U0pAeXUDWAWOaXreArIFrzbb63GM73GZNrtnOZ9XYkEGjMyxMt+DkcQ5snY6QSRVR\n4nULgRAqKlW2TPjqzY/zysXjjPZeBMNRun+SMzCVKrZ5dl2XnTCDZ+MR9973MD/4y7/HiWKAOB/h\nlAgBx19/9jAfuPMhBmVJ4YTCe5wYXmPK3rd85asYlZ5hUeAxCnGI1Vg1wcIENHbeFD+InSvFIyk3\nTjWWj6qmcH6JcExSNpkQu1mSAvtFHAMR3NpG/PxkZWVlZWVlZWVlZWVlZZ3nOm+cZE8MzVPs2Cns\n+CpcfiCVWhbR1ePcLExfO5lSLdABhmUsmRsMYPcKLC+jzqcyRo2gwjmqEFK+veCdZ2W0F+rpjrLC\n+NMv/Iu3cUqFPUUMTzcVal9SK+j+Pbzptz/MkcMn+Pb/9TvwdZg5x9oAfZh5wxr6lY7f+zi8UCIL\niyxv3om/O7C571qqhX2EYpDOVRgXUHIUpk03SzrHaslNJh3oRMpA03S1LLq0hO3HsM1B1ri+wg4X\nGQmU7SibbABZA8NCD3DrBWE73XYdJ5kxe51AmQbFxFGHGtvYYGl0hn14HqkdzozNqoa6YqFe58r6\nOFfpY+w+sCeCy+Z6NPcL6Xp0I9iaa9SdO4S/+KuP8y9++6+xlWVEBBWHBYtlnUTn1ts+eBfXXLSf\nA7uWcIWgZjhnVJubXHvFpejmJrKyzMCXmE4ZOsdU67ZsUs3w3mOuwJdA0JSJBhpsljklyTFoTX6P\nxG6bjgh6xTGopsjJVTiz/sym7VlZWVlZWVlZWVlZWVlPC503kOzUF3yLkVoMj69SnlxlqdZYXlkU\nqZTPgRSzoC6YQQ5NUGowiiWaS2NYXIbhKHqwDDTUOFdiptT1FOcLMGOyucENN70c6h0ZTmYwKnnv\nLQ+y78BufMqDMi9IUVJXSmHg9q7wto/ez+E3/gpv/PnXw3SagF485BaWdWFN42ZqWJWT6F5aWGah\nfpiFww+jboFqsJtq4QAb44tYHgjY8eisako36cBCZi+3QSGBCKfScShp3RkInCt/3JYJBm0u2DZA\n1oFh7euOA6x1kXVA2LZult39NW67HU63dh9G0ECgxoKn3Fzj8lHJ6YkyDptcWJ9gr6wzLmqKAeAW\nOxyxU0Ipbrb9FpLJLASu2af3GPDzv/8RbNcyiEPjxUQJCEJQZWNSc8JNeMdH7uT7X3sTFgzxggUF\nH+dr4wwsL8dtu3RQzoFWE6rpFs41sMtjJhgz2KgaO7NKun+aLpbxsCNkM4nX0wkU05r1k6ucnE7/\n1k/a56MLn7AtP3nyMo8QrWe5vgD13oz9nkYA0pfG3xug3umD0d3mOQa1t/futsXOMQy+J8y/byb6\nFus/v3PaHABH1ibntPDWZP5eHvdcv9e96Iq5sVddd8Hc2MV750P6S+/nxqSvuUPfhepRX9h9b/OD\nsyzbe5l7Vu9dtfcanNu92Ltu3156jqW3p8HZpqvv/uy5je0cGw70fQR6+k9kZWVlZWVlZWWdpzpv\nINnJz3P9bSaeztjg9BoLj59kYVrhBqPoJCtCJ7hfE+xowEaCCq6I/zJeGMF4ARYXoSiRoqAOqQuc\nxTI4M8WZUqtBNeWG176GWX+u7W6ipT1LeCeIK6IByZTCe8xK6joeTrG4wJ8/eJx/9LE7uPKl18fj\nCc0mum6y9LwtF4wZzDLAl1CAo2KoRxmuHmZp9XbYvz9+E1HtBO0nd1ELvRrA1OxL0zLNzx332M7n\nbeWXHfdXaIDWTnjW2J265ZTa2S+d7XUdYztcZE23y9ZdxrZnC4qpoSYoAaViurnJvtLzFfooUMPQ\nx3mVcgcE637J6sy7dc6ZzrMaDEtOn1jlLb/9PmycIKs4zFxaRVEzpq7EqbFVBT528Bhv8J5p0Fge\nmaBWvbHJJc+5ks2ThxnIKDJKC2AB1RqswiykxT3eRQgHkr6QJyBGBGWKYCLpVonloiKz92V9k1On\n1jjR89nKysrKysrKysrKysrKyjrfdN5Ass/3C/jZ/oBenzzN6sfuYumFz2bhhmUYjSIAU43AzAWw\negY6SJll4yGMhjGTbHEZRgtQFKzs35ciq6ZMzKhkirmCaSWsnTrO1/5P38WFX/Fy2FidgRuBCuFH\nv+/fsDgo8b6IpZaWSttQnCupBIKVTEONDEu+61f+FJn+AS/aNeYH/8nX8+yXXJ+OPUEbYeaEsw6U\ngw6wCWm55DYTieWj3sN0I67n0mw1bq1mu5D+rJ7AmTggJIjW1Fp2ZrsvK21bjlgHftX1POjqc5WZ\nxYywxiXWBWLbHGbMIFxbNtu4y4BQY1WFBqU2IQSoxDBv1AasOQajXbBxtENcpT21WfZYB4y180PH\nuQYUnmOPn+A//8lH+J1PP4wbD1MJ5CABsghWIyQzDEFNWa+VkADew0dPc8m+Xagq4j2hmgDCv3nT\nz/FDP/xjTLZOMCgG6bAqjCmIUboKQo0vBpgJ3hWxM6UoIkXszNqenmCaQvulBAxx4JwxGI/Quw6y\ndt8jqdQyI7KsrKysrKysrKysrKys81vP8CKB6JyZPH6C6eMnExASKMpYdimd8P7GoSWpjNAXcRlf\nNGn3gLByYB+FgypUqAWCVlTVBMKUodRcedONKRzeZvCnKDh078N88FOHANqSN+9jSLovPN4JZVFQ\n+FQqJw6TAh0OuWWt4sfe/A7e+ivv5PgDh8CVpNpLIgf1EZ6Jj48mRL4pzaQLexyUA9A6lTy2NqgO\neGIGhLYF8OsMRLXZbek8Q9jh9Oo8N2H97euzlFW27q/uzza/Xve4uq6znQH+NKemLeyLp6EohmpI\nnSxrqnoCwW0zosVcs+bRcaa1lZ7RnTWrXRIoPH/w7pv5wbf8Eb91x2H84hjni+Qgk5atmRDdX+l+\nCFqhGqhNmarx0XsP4zBMo/stlksGLr/kYl7wohtZX4u5eJUmt5kDE0U3NxCNAFMS2Izh/XH/ksBf\ne6u7uIzRhPhHeDYYltTHTxNWz3y+H8KsrKysrKysrKysrKysrC8KPcMhWYQB06OnmBw6SlsCWZSx\nDFFchEqpw1+bKVWUUPjotmo7YQIGbv9edu+5iK16EgFZmBDChK3NU1zynJewdNXlMJ103FEGpefd\n7/kEtjBsOwgCOHF4cQiCd47CO0pfUBQeXxYE5wl4KApOD0f82kfu4Xt++jc4dv+jUI5gNO6AMU/M\nWCsSMOvAM1fQwjSR2IygrrYf47aSzZ3AqgOfWtjVdX51SlW73SjbcP4GwnUBGcxonHbAU/phW8ll\n53lbBtnZHu2JdLLPAhYCagEzJWjs9BhCINSBaTVNOT++Mydsf3TBZwsHNTaAGJasn1nnXe/5OD/7\nZ5/iwcoYDQeIxLk3iZ0szWJ4viQHWTxUpfF31abUdc0nHjrO+uY0nUKABPWcE77sy17JxkZcVs2o\nic9l6fDVJqIagZdIYqSS7vE4pt0pajpbJqjmXLwXB4OS6bGTbdFwVlZWVlZWVlZWVlZWVtb5rvOm\n3PKJVH38FFsPH00AJUAxTPBLIgjTrpOMOFYUM4AmQkzOB5aXuPq65/E3H30v3g1xCBomnH58k9d+\n75fDqICNCW3pncDmiVXe9zd3smtlHLOeBEw1ZaKBb3YsgnmhRBAJ1GoRUoin1kAx9Jy0wHf85K/y\n6msv5utfcxPXvfwGxvtXUvnijpLLtgzT4rk274mDqtN9swVBnXw2ZVaGiQOabCyJwWnNrpqSxG4S\n9M7g/p1ONE1grBmDjvOsB4KFLiDb4S5roZjNjqEFWkrTwdEsoGjkWkbKJbNYNCqOra0pI7/J2PnZ\n9lvEbJ1w6E4WmfPU1RYHH3icP/7g7bzrrkd5rFaWFkZI6+iTdBsIZtpGxWkztWKYKiIeTfNei+fh\n46e57/HTXH/ZAaahmQOF9Q1ecdOL2HvpbqrpKbwrKIqCYlyysDBkQUICbhaPwSyVdyYI1i27bRsQ\nJMdZyqPz3jFysPX4qfbYs86unkz2XllvAP65hf5rX5r4Wa9L3xZ69tOThO58z3I9Ke/nCk/7jrvv\nftKeU5GeoPWzhbc/vjYfyL822Zobu3Jh/lfif/+yq+fGXnH1/rmxC/cszo35nvk6W3OBc1qs5wR7\nz/ksE6F9638uIfg7t9d3lOe4bv//N+ZX7mtq0LuP/k4AvYmXNSsAACAASURBVCfT04uBvs9Lb0j/\nfN+Fcz3lrKysrKysrKys80AZkhG/0G09cmRW9ieWnGRFZF+1B6mJoAooGzdWcuCImz0XBde97EY+\n8NEPMakVL45QV4QtuPKlL0oOrQ6sco5HHj7Cg4+tsrBnBcXwZjjvUiPK2FHQWXSUYYo4AxPMuxTO\nHr+8qsbxzbHwrgeP8Z7f+BOe94cf5EteeC3/8H98LcOVxXj8jYvLOnCnZWTpn/uNw6rJ22qD6Rs6\nliDTti8wLn3jalBPAmrWvN7pvuq4wLqAbGfQfrfMkx2wTDvAjB1Ajc5+mkyy1gGmneNJTjKNwEjV\nEnsTlBqrwRcF02rKeCBQJyDYwsYExhpgKsZ0WvH4Y4/zi+/4ILc+dpozZgxKz1JRxOyv7g1ohqVS\nT9BZJlkzTyKoRrgVVKnEmNaBux87yfWX7k/lmIpzSr21xcrePbz4FS/nI+9+N0u7h0hR4IcDFkYF\nZV3FUt0EX2KHy3jNnC8jpHUdH5lILAWNNw4OoxyVjKYVa6fPZDyWlZWVlZWVlZWVlZWV9bRRhmSx\n/ySTR49jWxXiywhNvEsgzKKjyzy4VC5Y+AjRGitYU47pPJhw0Ytv4OJLrubgw3cyLpepqppLr7mY\n4ZWXwdaZ7dBnccDdt93PugmLkpLERHDORdeGxqI7L4JKcnklOKVEp5lYdH1YE7RughOjNuXWk+vc\n8p5b+Mit9/LKG5/NC15wDdc+5wqGF++NsG9axbywBjQ1EK0powQwF39MnQ23/d1cLc6LpbB+ScvH\nN2npm7H9L/pdALYtp4wdYzYDYQ0go/s+nfcAa0L8O860dps98C2BM8NSDlnM8IqrCyrRZUYQKlWY\npDlpTtE3+W4Qzqzz2UPHuPWeR/joPY9yy6GTbHgYeMfQlTQZeBEfSgSMKX/O0tybSQJk1pZeNrcZ\nQVNJZo0Cj5xYYzKtMO8hGOoUq2sKgVfc9FL+7J3vZmFfSTEYMl5ZYs+wgMkkusJwM7Zo8f4VX0Qo\na7LNvSHmwDWh/Y7BsGRwapVwZv1z/rRlZWVlZWVlZWVlZWVlZX2x6ryBZBc8wduXhx9jeuu9DF/x\nXJiux3LKwSiWVg4UpsTyyrqKUGTvfjhzmjac3ZepTBO48EK+59//Are/4095y6/+RxYFXvemnwCq\nWdlj48JaX+fNv/6X7FpZaI1NEcMIFhTnJJXmJVdZyifToIgGVB3alsxFZ1hVR8ARzVVKUcCtZzb5\n1Ps/jXzgNgxil8SgjBF2j0sO7Frk4v0rXH7hHr77B//+zFFnxOc23D9RuaYcT2xWr2ON42zWyGCH\nZWoHY0sOr51dKY0OuGtcYAncNU0BtuWb9XS3bOsm2ZGtprO8MCxur66pQ00ISqVGUKMK0TBmBEwc\n6pVJXXDHsZO89d0f4aGTZzi6tsWJzSlrQSlLz7Bw+AaYIsiwpEzXDucxC22JUWR7iiAJhBkhgb/m\nemoCpO3r5PmqVVnfnPDZx09y92MnueaifagFvHeEEPCrZ3jdN3wt//Ud72T1zCoXXX0Fz9mzwnAy\nRVVAfGugiw/BlyXD4TA6yZpLZbFxhCJ4B15gcXnIBaaMP3k3B9J9mpWVlZWVlZWVlZWVlZX1dNB5\nA8nGT+jWY+lcuP9R7IVXI0UB9RS0TmH3KaS/yaFShaUlqCYxu0s7oMwnR1Ux5oZv+lq+68w67/yz\n93Pg2VfAZDqDRCkw/fTxUxxZn7B7cSF1NDScWHQvpdyqGIcWHUZgMTfKO0qgJoIXU8VwqAYK72cB\n8LEHIiJFLNmzFLXmYl5Opcpj04pDR05yy6PHOXz6Dr77+19HC6iaskKTjkOsmbYGQjXjDVST+VCi\nbXCsA8u6TrHGFdbtQGk7AFoDxNpssmaTO0FbF4zR04Qg/ZxKbE2VoDH/K0KpmN9jRAjpRBAL3Pb4\nUd526/2MS09ReErnWBgWuNRgwYjNFjTOOpIm3LSOTRlIUfgCTVA/aVkwwrbSy45ZLi2hpogIm1XN\nVq0cXdvkGlNUHVIHVGusrkAcX3LTS/nLj36UZ+/dw2BSpTtFWqhpJmg6rqIYMCgdTW/LplFAPPyY\nM+e8MByUjI+fQB55/An+TGZlZWVlZWVlZWVlZWVlPbk6byCZ/d2LfN6q7jzI8JXPxy+OoJ7Ex2AU\nyyj9IB2EgAQYDyNkWT0Z3WGhBl9EmEYAq2FlmZe//tuY1spgzwpsbcwAUHKSHXr0BOtq7HPNSSYk\n0uZSGa4YQYiZVCKCpFJNEyh8gWpNEFLwt0udER1ODRXFLJYPOinTzwZiSHJ8+QTNKIx9y0BVQZFg\nkktEr3FqicweSCq17DrN6KTO77h4zWK6gwB188FaYKY7Avx74BddN5l2YBizn7HtYGxnGacGzALB\nDLUQHXZK694KEBsmWAALbGxuslwWOCcRjImgJohziLhUthnPX0jjGLPqSkvbm82Zpmsd0vWO8xXS\nlMXzjFwwJGNdhckCdTXl2JnN2IVTYlmkqhLqGj+Z8KVf8hIeOHyIxTq0gMw6zxpqAHxRMhqVlD66\n4JplmgtqGB6jHBQsopSPHmfy6LEWpGX97ZLeJsJ9/0ebH5OeMPK+8HXfE27eF7yfNnpuh9PXSKBn\nsb7g9z71HY+4vmD7nuV6dtyX516F/pYBj5+ZD+m/aFzOjX37l147N/ayZ+2dG9u/sjA3VvQ1NTjn\noPz55fqbMcwr9DROONvvS7WebfYufI6/cc/5Xjqnof7s/XP7qPQH/J91T+cm3/MvpL6PVd/9mZWV\nlZWVlZWVdX7qvIFkT4aq+w5RP3AYf+n+CLvqLbDl+KZzKczfR4eZeVheie9trMJkM7qSigJcGdux\naQXO82Xf/20dF1QH9CAcOnSUYlBGtxLxS4xaQE1iDrz3mIbkBiMyuqKI5YgWOx6K9zhXEDTgnCOk\n7auA80V0lQVFrcm0AiyW+ZlKg3MiDPEBqjo2LGi+pLVQDNpw/rb7YUP3ZPbcBNl3oF8K3Io/tuPN\ncmfLJEv731aOudNh1n2ffqA25zJLcC1EuBlCIGhIWWRGMKhNUZOEvFwClsrpjWnseOmKxOEkAQpJ\nX/hjxhipbLHtXSmCWmPMa0Bo9Jt1v3VZgoYxzS02amhzyjpzHELNxrRmfTKNXU4l5qkFTaBtc4Or\nnnUV167sIdR1e63iFqSdprIsGY2SiyzlkWFEB5y4dDkN54TRqGRpfQN55Ajh2ElyqWVWVlZWVlZW\nVlZWVlbW00kZkrUSwsOPU332IcrnXYkbldFJplUM8MelVP0U4h9iGR1LKxEmTDZgazPCtMEwLodG\nEDMeRfDUOJtaMKJMNyd4N4NUMXescZIZIdQIReo66FrnlSQHmKi01Z7iYmkgqq1bQYTU+VBi6aWC\nSQQuojETq2FVguCcj2Whg9TAQInnqckK1Ybxp0ewDjCDbcDsbH9db8PoO66wFnDtgGBdN1gzb80y\ndNe3lG/WdYl1t8MMvKlFyBgqVEMEY6ncsg3tb66BNH6pmL+2ur4JroggEodzpHJMMA2Ii6Wu0T3m\nwCw197RUbtmUMTZllfE8giY3iWmaNmmXac5T07lZgmFqxvq0ig44BKmjC08ECIGl5UWWxgusnVlL\n24wQT9UIQSm8YzgcMBgUM6+T+HRPuLZyFhG8h4WBZ3jwOPXDj39uH62srKysrKysrKysrKysrPNA\nGZJ1pNMpG3/4QSg9i9/yauSAwNY6DMex3NKVM1dVmaDQAFjaBaGCtdOwuQrVAIaDCJWECNWmW0Ro\nltxkIbqmVk+t4nyESqaGEmYld6Z4X+BM8eJal1Fbyuc9SIgVkdCW8gXVmK3l41hRFGhdYeIThCkw\nC6g5QqgxXAtklkYlB+95mGe97NrUCZF4Hg7wtt1Vtg2ONS4nZvVEzZDteNHAs22usTTQuL3CWTpT\nagr0b0hWE9CvOoNn3VB+mD2rxsYLocZCICgEU+pg1FWgUmWqRo0RVAjpuE2Mwjk2pxu859N3Mw0h\nRs95D5ZyvFQxJ4hpCx3VIpxsXIMNdNM2oJ/kVIuOMVVL61o63FmAv6XzN4ygNYoynU7YmmyxWVex\nwSqeuq6pqilua5Ni/x6uu+G53HLzrbFqVaNDUcQzHDrG4wHep7JR50Acai7lpcVSv8ZFtnvvAhc/\ncAje/ym27n7ov/ETlpWVlZWVlZWVlZWVlZX1xasMyXYonDhNddeD1Pcfpty3AmECtY8OMSmjO6gp\nKUTiDJrF90ajCHwkOZUMsEDMztKOy2nmDhqNBoQ6pJD9OOZSRpWaR1QxF/NpXHL1uG6Qv4ZY+qea\nqhxj2WYENqkaUUjdFSPk0gRtHIalcSOOOyc8+NARnvWSayJccrCtrM41mWSuhT9R3Qwy7YylZVr3\nGLR1h205ZeMWo52XbS4w62yz7XbZQLLU3bIN6N9RmrnNTWYQKiyE6KZSo24fgdqaTpLRfaepTNJJ\nLH09urrOnY+cZDAaIb7AXMwAc07aU2tC9yNXNIK55BGUVFqZsoakKaGM5zzLKtN0ytZsrXWbNXDQ\nCKA1wQrKwmFBUQLqfHvspgpVzf4L9pMi0UB8atrgcM7FQH4iIIugtGko4HDJkSbpfluua4qDh9l8\n7Nj89c7KysrKysrKysrKysrKehrovIFkfdHXX3hFzBHuPUR9+32U118Bo3Est/Sx0yTiZqCoW1Yo\nQFkmp5NGOKY6g2TdEsLWcaXsu2APVVVH4NU5hm5ocwg1OIcJOBeD4oFYGplcXeIdphZjpCy6kMRF\nh5g0ACZlhTVOIVNNYfCxG6NDUODT9x/hK6oQHWFtN8nkJgsNJEvn0E1aburzWqdYmpgmh8voLGvb\nl2vhIbNyyW5JZeM065ZUNutsC+3vrNtkwHXHQ42qUVvsJBk05rUFjGAa88i0CcqPrishhkIfOn6K\no6cnXDwYxIrboBE1JvceeJxLJbPtuWo779a4zBLsUjrnK+l6tPOjqTdBhJ/RKJfAFamk0xRvIbGz\nBNQaQIaB1izvWaYps3QeIhQTnPcJhkly/s0ekYFKy0LLQth19CR6/6PYidUn6bP49JE7a6D4dnWi\n6TqaX1fmWsf2r3u2QH3Xs03rq43uWb8nIx7zPTvpy4fvO7+eg7SelftORXrWndShZ0koivlfdf/z\nlz9nbuxLr9o3N7Z7aTQ35nsS3XtD8XvU13hB6/l1+5arey5A337P1rSht5FA76Lzg/0Z/T335zmm\n7/ct53saOUjPOffdw2cF9z3Dffu2c9yPnuNn5emgg8f7PtxfWL3t4J4nfB8AXH7BE74LW36SzmVh\n5Qnfheza9YTvQ3d2TH+CNN49///wL7SmZ/r/n/uF1njliT+XrKysrKzzCJKdejJ3dvgYk1s+y74b\nn8No91JsceUKEJ8gWfOL3TogpwFiqdMlCVS45CpD2Q6PIrS55MAuwtak/aaiyVXVBLyLKcECaNqt\nRZePIXE/qSTOmmwwM8TFXCnTQOF97H5m1hxRWkYiJAkBix0DUgdM4cP3HeUNaxsw8jM3mRGfGzho\nMANgnW8InarLs0KytvIyDbTdJ+mAr8680izP9pJK1ZmbrNdF1nWQ1dF9pdq6x4Jaeh07S4Y01lRw\n4uPcNt+nPvPgY4C2bi1xaXqcwwF1qClk9qVCRJJrrvWLRSdY+63U2i+ZM0Bm6fZI1yw526w7Dwhi\nNYNixLiQ2OxBFa8O0Hg/WHSSjZeXcOLjfWiAc3jvEgBzs/LZFNLfmCQFwTvDCwzHBXr7Ixy97zCx\nBcCT943wwidtT1lZWVlZWVlZWVlZWVnPdJ03kOzEk7anSHDW7noI/8m7uejF10A1iaCsToBMXUzN\nb2CGxvI3tJrBMiEuMxjAxs7uion4BOXiC3axZEodlMLHIkjVgJPoHFMFddHjZUYsrRMXS/zEpa6J\nllhUDIdvSi9h9pd+cQ5vYAmgaKx7xDmfgucdJnG9O49vcP89j3D1C6+Kx9oAOBc6UKULxjrZZNv+\nGp9oWOsQS8MdSNTCrx1lqLP3bOYSg/my1SaLTHfOcUhZZSFek1CjGue5TpArBKMOSqVNcL9Gs1mC\ne5JcVUXhOL2xyfs+fR/FIEKwpuOoeYeGGnVlDO83SwBNEhubZZIhndJJi24zkRkcE2aQtHGHzWBi\nau1gFq+bGAOv7B+XVKrx+pm2TQjUDKqKwcKY4WCQYFu8TyIbk9QMQtqGAoK15yyJn5WFY2FYcOLu\nRzixufnf9pHKysrKysrKysrKysrKyjoPdN5Asie3xEuozqzz6Nv/kn2vfQXlxXsjhBlWEYYNhsBC\npAgaZpCMOrqGCgfeRbDmHdQbCeIkmNO4ykLN4v49/No/+0d8zy/8Hrv270ox7g5zENRwPkKf2kUw\n5lIHSgmpNCWxI1/4WOangaIoYzaZ85gq3ntCAOej68jMISFgziNE9xSECNwU9q+MeONb389v/tgK\nixcsx6YEYmBudiHaKLKeK9O8ZzueoeMY2/m6cZoloJQ6NW6DZ0p05W0rt+w6xjqgLCjUW7EZgsbS\nSlVjEmJAfp26WVapxHJSpyB/UqWYCKXzOIkltv/pL2/mfZ95kL3LK5gFMI+JI9QhAkhV1EU25wQI\nCU+KbTPUxfB9mr20LrxZgL/RBPTTONaw1i3mRChEKL3jhRcuc/0lFzKZThkMS4I6NJWPaghYXVEM\nltm35xKmkzUqrRNIC7E7qmsy1yI084WjKMA7o/AwKB27d4/ZddcDPHL7fWcpccrKysrKysrKysrK\nysrKenrovIFkT7YEqFbXOPPJe9i79yWpFE1mUMiXpCApEr1JK8qsHDMEmE5oYU9y8mxzSm1NefHL\nruPFlyxx93pNOZCYeaLRFabBCAJOI6QyYv6VE0HN4VyEYyGE1lkW6jqZu2KdpJninUSHWXILaSQ4\niEWOF/CIgvcGKjx0esLHbjvIV371C1PtIVCknLK2uyUgfTk8yUEm0oFfzXmn97tBOE13y06ZYVtC\n2XVTmaXSVdsRyN8pq7TUQTTUsSEC1ob0B4MQAsGSg8ya8eQkazbhmrB+x2BQ8tDRU7z31nsp3CCW\nMaqm0P6KwhfJ8RdweExmbjtJ7rDYmTLBMLEOQ0x5cc2rVJppqcRSUrlpSKWWTgQvUHph90LJ8y+9\niGCp4QOGoYQQUPOo1sndqAzGQ3DgqknsjGnRXSdiEZC5mJsV75nYvKHwMFoasHhqlelnDp7DJyYr\nKysrKysrKysrKysr6/xWhmRnVQQ9qx+/k4VrL2X07EtBanDTSBXqSYJhsWwxQqG0agN2Qg31FCi3\ng6IGCAFoYLi8wD/5plfyw29+F7sv3o9iMSdfDOdcbJQpDnVNOZxgzqXg/WjwwgQh4L1Phi/BpaB4\n0UTCYFZa5yyVAzZZZID3WAgIsDgs+IOP3cfLX3AlC3sWo5sspAUbYGj0O8l6LWTNyx2llEDbhbI7\nP01ZZeMgayFYmGWRNZlloQPLQgVBMdNUQhlzvQLMO8iabLKU/aUNy7ImuD7Crvfceje3P/I4K+Nl\naCLO08SbSYRbzrXn1bxvaMsJwXDOOhFrTb9L6WSRdaHaLIwfYiMH76KXqxDj+Rfu5pK9u6k1UKgk\nsJayyBoYm9yLxoRi6IESp0IIDrOApPwySeDOueguK7xRjjxLYhT3PcKJew/xtE2mfhLk+4L7rWeo\nLyi/J0y8N3S8Z792luT+vpD+/m3OL+d6xkLPfs7xlHtHtS8Mviecvq8hQl/YPcDfe958ut0Nl86H\nbC+PBvNH2DM3veH7faH6PWMhzJ9LrfNjVZhvQhB6xvqaKZwljb/3+nnpuR/6zqXnuoT+3cxvr+d+\nKHuun/i+sfnfMerm/+kiPXMY9913P/Xc2703bd98ndvnLysrKysrKysr6/xUblT3t0pY/fR9nLnl\ns+jaRoRe1SQCsnoKdR2hjAa2l/qF+F5dzbpDAtvoWJtNBmxOeM3XvYKve96F0eGTgEmbL5XyskII\n8edQU4eaEAJVqKnqEF+bUtXxPVWNLiqLX3ZNtan3I37dkTagvcnfckZ0QDnHeFBwy6FV3n/z3eB8\n/CZWh+TS0lgO2ZY6nu3ROOia12meQlOi2tmeJthVN+M6A2DdktYQZvvUOr4fQrwO9RSrK1RDzB5T\npTalCimLLIQ0FlJgf2jnV01jSakIzjucCGVZ8NDRU7z1PbfgGSQ+6GbnBsReZxFOWQJbagHVEK9h\nAn1mscwzhvHHfUauF4GepS94TWZZUxYZt5vcZ6oMCqEs4EuvvjIG9CdA2yzXwLZm+zhHHTbwPuAH\nDl8UlGVBURa4wuN8Ol8n0UFWQFEIC+OC8bGTTO44yMajR5/gz1lWVlZWVlZWVlZWVlZW1lOvDMn+\nDm0+8jgnP3gb67c9AJMaqgqm0wjLwiS5xeqZ8ymEGbQJ9Qz2bAMrjYym9NAVBW/8kX+IhlQKh6Ia\nCKFGTQlaE4JS1zV1HQh1BGR1qOOYhgjINBA0UIUqvheq6C7aBsRoSwITFyOhtBjmTvxL+7j0/MYH\n72H10NHY3TNYhGOhA7LqugPPOsCrC7VakKUzoBi6j862NIGyZrstTKs72+2sE+qYPVZN0LpC1ahC\noNYQoVjQNH8zQFbrDJi1gf02qyQVJ+CEYML/+/5P8tCJ0yyOG+eCgpMIpWjKIgUl5r2RSiU1QasI\nr0ILroJqdJ41sCyBtPh+hGsaNEG2pplAdH0NPZRijAeOvStLBI2AbOY4a4x4rTctloaGCaYTvAv4\nwigKoSgc3gu+8BRe8EUssSydMVoqWd7YQj5zkNN3PfKEf8aysrKysrKysrKysrKysr4YdN6UWz6V\n5Qyrn7mf8Udup9y/i9GzL4mgrJpGIuF1e3dHmu6LCRrReavNIrPZz42mFRdcczlbJ08z2rNCsnVh\nItR1iN0GUQSHS6H+YrEkMACisRTP+9h90adcNO88qoZzEQQ5F4PoY2lKKp3UCMfQkA43BlUNvHJ8\ns+KdH7yD7/zGL4mQzFL5Y5vRRmqF2DnPJpOske0YasookRlc7Ib0t+6ztLISXyfoE81wM+hmoY58\nMoX0x/LK5MKDNndMLYbaByMF+Tc5ZAbOxxB75xDncOK486Ej/Je/+ATjchR7P1os3XGWSoBUU5ll\nW0MZp8Zm1ahmlmLbtM2ja0oopYGUzUinHLUBcFigdI7SwdALo9LxwksOMK0ryqKMs53mKrrPZo62\nZs5MK6ACC7H7qRO8pcrZdBlcathajBzLYhT3H2L1MweZrK3/nZ+PrKysrKysrKysrKysrKyng84b\nSPasp2zPApsT+N33sfnAY7hv/nIGr7ohEo0ida/0PoEimcEv7UCdwYiWELWAaOdrYGOTT77zp/il\nf/12/q8PP8BFe5cxHBUBUUnwSiA0FsBIpcRJKpgEcY7hYEDhC5rsMdUaSVWfaobzKdBfY8qMSczV\ncuLb0kHxHjNheSi8/eaH+PC9R/jVH/4GmG4lCpSO2SU61uS5NDBsW25Lt8SUDixMyzeZYnTeC51l\ntoXyV62rTM0IIeaKRZdWxEuqhmJUCZjVCZCZwTSkUsuUQabpoJ1zuMIjzuNdgXMF3/wL/5nl0UIM\n8U9dLp0ksmSGK3wbsk8Cc9ImjdHmplnTrKEthww0jj5L5x2hmiYGmMbMGBfCsBDGpeOilUW+9SXX\nsXd5seWyZg1gi4BMQ01dV0y2YFh6xtUEdIKFCP6cuPZ+cenaCLGLalHA5d6x9Oc3s/XhO9h7eo29\nOYssKysrKysrKysrKysr6xmi8waSPbXBuJH8TG67D7drEXdgF8Vzr0z5Yz66yVynq2XbrTGVHg6Y\nd451uhpue0+EH/vxb2P85t/l333gHnbvXsKLiy6p5mhEUqpYKhGsI7hx4hBTCi3wHoIFJEjqhKnR\nRaQhQZgUGC+Chdih00wR58G5mHeVOmwuDEs+e3SDamODclCCVrSgz9L8NO6yZr5aWNY97S4IazbR\nQMWuy66Zu877IYAFrKowLFZ8ppyvKsT8NTNrV1OLYdixu2Vctjaj0pByw5rQ/JjFJS6CMBFHUZT8\n5S13MyqGHUBmsfdBsoiJpM6UAtHL50lWs/b8mpcNOItALZZRisRjbFhiZGhN2aWl9YxRUTAeOC7d\ntcw/uPFaVsZDgmn64Frr4NPOPdRsRxpLG6lk1UXQKM61xyViOC94D8OFkpVP3cPWrfcTTq+d86cj\n629XX3B/Tw597//j+vpi9C3XH/DffzxnyXTvWa7nuPs22tPhti9MXvpOpieIvu/wnJtft+4JwN+1\nMOxZG2666sDc2Kic//XXd9x9ofp1PX/c9TmG9E/rem6s6tle37r0HEtfZsJ66E9SOFn7ubFH6/lm\nBSd61l+znmvQ1xyi7x7puekW3Py5LDA/D5eV1dzYpaPJ3NieUf8N33ud/fw8OO0J+D/Hz19O7s/K\nysrKysrKevrovIFkT70Em0zZuvlO/MV7WbrmsviFpTDaMCvXcZM1Xwq0Q49aaNQBZO2/rhNNSg0A\nfuAHvoXS/wG/+v7P4leWZqYqiJV76YtIzMNKW3OxG6aqoSHgXdk6jYIZzjS+3z0+JzRFf+ILxAx1\nkvYRj1HEWBkP+P8+dCff+lUvBHMxb0w622nKL7HtX5I60Cge5E5Q1oFi1hkPoQPQYumqmaaMsQiS\nmhLK0JQYauMOs1RSGZ1ZIeV/BYvrxF3Gs47OMIdzDhOH9wUPPnaCX3znBxgUvuV+TeczkdTwQGaO\nMcO1Af20X/I67ydoBaljZermKSIzuCGNqywCsoEThoVnVBZcd+Fevuray1gaDVCJ0EWa/DRJ5ZtC\nB8Rt/3IXYVlATfHiMFOcSCovjdsrhwW7vFB9/B6qx4517smsrKysrKysrKysrKysrGeGcnD/5yQh\nnFln6/2fQo+djiWB0wqqEAPt6xTi3wbbN7lkO6BYA8u2QYgOLEs/fu/3fiO//O2vYmNa4VEKDDFF\nJIa8B41dLc3qFPoeg96bjKugIYXDa1uOF1JHxzYZzIJrZgAAIABJREFUS7U9HEk1fE66zirBLIK0\nf/fez/LBT97TAYC2vTNl0Dgn3Z/rHUH920L3QyeMv9lWco3VUwhTrJpg9ZRQB+o6daxUpbLYuTJ2\n/TTqECFYbZoC+UPKIktlmRadce1MiyDiMYkllohjWJQcObHGv/7d93Hw+Gl8gmFIcv40IEscTZMD\naOCWMvOmER1bCWBpBwgOiwJNXUpnQf0xrB9TShGGDsaFZ1QIN122n7/3vCvZvThCsQj12lsnlWWm\n+yq+oyCxaYBrSJkZWLwPjYCT6Bx0YhQeioFjaXHE3kceZ+v2++nsICsrKysrKysrKysrKyvrGaPs\nJPscJUD18BG2/uJmRl9zE273ErgAzkPh5rO56iraeiyhqG1lJ7bdWSadnSBQ17z661/JN/31bfzR\nvSewpRGjoiAIOIRpcqnVAXzKFStjTSB1ULx3seMijloVsVS2ZIY2jihi6Z1Jck05iaV6MCsfDfEc\nFocFP/G7H+entyq+7mVXRyAoLpVbySyrrKVu3ZOCOUdZ1zXWuOhS98oGLjW5Y0YqmdROWWVyaNUp\nDL8pSW2ccsE0jiU3XVO96VxCWZ2MrmE55KHHT/Ovfud9vPeOg+weL2DJbRU7fgpx1mOZpYhPZZMp\n363JBmtfK4JvAZaapi14SODOzCh8bHpQOKF0joEXBmXJwDsuWVrka55/BcFiuax3sUTIuXRMrtk6\nILYNlQGoBWy6ifNgaCTiJu3lKQqhGBYsjwYceOBR5AO3dW/ArKysrKysrKysrPNOB4/Pl9U/U/S2\ng3ue6kN46nT5BU/1ETylsuVn8LX/AitDss9ZESCc/vU/ZuM9t7DwVTcyfPn1+Iv3pQB/ZqAMZiCp\nW0rYZm91yi2bnxuwVAjgoQr8/E/+Y37eDD21xp2fuZ93f+wePnrwOPdMQVNOlqRcraIoYlC/eKZV\nhC8R9Di8c6kToiHO4V1yjZngiI6ylDIPzmOhbssoTZWy8KwsDPmXf3Qrb/rjW/jfv/kmvvL6i9C6\nRpy2zqrZNHXgnxHLDJugrpRDZk3XyTQHTfVlUE2lkbNgfDXackltSyhn2WRxsQTL2mWabad58h4j\nhdeLwztPUQz4qbe+m9/76B3sGo3YszCOl9FF4NjMn6TSTHHRWmYqiAuYCeYjQKtDwDmJ0NLCtnJT\n7x3DsmClHLG2WYGk8HyDAuHZexa47tI9XHPJbnYtjuO8i+B9Ct1vstOcxHLKBnQ6iZ5QMRImRLSm\nLBxba2fSdGvKVXMUDorSs2dhwN4HDlN+/F62brkHnU7JgCwrKysrKysrKysrKyvrmaoMyT4PVQ8c\nZu30OvXDRxl/+Q2UL7iKuUwurTvp7O1/ZllcMMvz6obfx8CsWffMusYtjHj+S67n+c97FtNjx/nT\nj9zNg8fOcGR1k1NbNXUdOO2Mhw0goCrU5hBRCi8EDWl3Ec6YgvOxlNJH6pLyqhzOtA2xbvK0LAGn\n4aBgszJ+4L98gJt/9ltZGJaEatqWGLbn1CkHjEa6lNJlqdwzcUIllkJqZ19BtYVhDfwyS+N0AJjZ\nzF3WWa4tJ03nK05QExCPT7Cr8B7vB3zyvkf5vY/ewd7xGFKZaSxXjI4zcT4CMrPo5mrC7+O7CQLG\nELeGV+mstQJqKQHNDEG5bDzidBUYD0uWRyV7l0Yc2DXmuisOUA58PC+J7sDGMdbAV9cFsCm/DLG2\n2WjbedMJflCwtXoa5wwnUHiH9zF/bLQw4sCH7oBP3M/GvY90bsSsL7SczM+rtResM9Y3/z3ral94\ne0+wfVvmu3O8Z6xvyd7g/55tut6q/fnl+o67byf9u53fXt/xjcv+vxwPivljDKHnGHs6KtR9y/WE\n9PeF+U+qcwvp7xuTnnM+FeZD9u+cjOfGHtTR3BhA8PO/8oeDcm5sYTTfAGFlOL/NQTG/ru8ZC33N\nD3T+nKc983X7ZGtu7NbJ5tzYnpPrc2MANwzmm5A8a3E++L9vHuhpVuB6Qv/P1iQjKysrKysrKyvr\n/NN5A8n6v+49lYoApDqxSnjfJ6mPnmT3gV2x/LIBRGoRngRNSephOyjb5iIjAY5O+L048AZl6pw4\nMJgalCWD3Xt47Zddz9bmhI3NKVsbE7Sa8sH7jvMfH5rGzYYac7FUsOuMci59YRWBUONECK75UiYE\nAkosu2xsWEbTKTJlZ3nH7oUR/8cf3szrX/MCLt27RFVXsVyyyeeyBlU1p2lY6oTXlPxpcn7NYrsa\nSJb2arPg+6aUMjRGPFKEWbNM07HTmvS3puazgV2Ccx4ngvclQeFdH7uLf/uHH2LvwpjC+3b/zntU\nwftZYwPnXXKH+VnTziZs37n2rCwdXARlaRmJR1E44TU3XIwvHEVRUCZw5cSBj0H+4iS6/pAIMRun\nYMpHa91jDaBL946Itc0WnBMoB2xsrOOdUBSeclBQDgcsO8+uQ8ep3vtpqmOn0ibyt7ysrKysrKys\nrKysrKysZ7bOG0h25Kk+gF4lsFDXcOu9TP7sYyw/70rGl+3DD31yhwXY2oKywSq2ffWd3R6bNwRw\nBrjoJiv9LO8rqdwsqcfCwHusKNB6xAuuBH3wkVmGlsaOlyEkKGaK6qx0DwRVEA3JHSWx86LzWPpL\nf0I/1KoxcD7BtNI7/uSOR3n01Dpv+O9u5LqL9+DFUYUKmvLNRqaAxWafDTpLpCykaWlgWOsUU5uB\nshSa36yvMMsbM8OsCdFvnF0diNQAR4TCFTjvOba6xZ99/LP84u+/H/EFi+MhsbQyLm8WnVdNFL+I\nS5co+cPS9ZJUHmtqaTlry0ulCfpP1K6QeBkHwxKT6BJUMcTiek5id1TnfModc21JpRNBvOBSY4Vm\n39YcQyrNlHQMRarjPHXyOOWoZDQsGTnPwtqE8f2PsXXHw5w6dmr7ffxFqEue6gPIysrKysrKysrK\nysrKesbovIFk88URX0yKVOTQn/z/7L15tC3ZXd/3+e29q85wxzf3PLeGFlJrALUkkFBsM1hgEAQw\n2IbglRWDE+zEiZXYATsLExJPZIUFthdemNECG1AQCUhIQkJIQiNqzWOrpdbr6b1+453vOVV7//LH\n3lV17qtqViP1dKX9Wevp3rtPnZrOuU/vfPv7/f7ex9In7mP1WdezdM1RRieWkbUJo61N5MQxqNNV\nHDCSyaMIZd1+aeIdV2hsdnmZpdkehbUUhaOqPDeOS6rqi5SFTVKSxMmJVqD2US+S6NQyaghpCIAY\nwXgPxqAhYJILTJNQFXv141RGTc4oawxHJmM++vAWP/bad/D9X3sL3/nCWzm5vsTeYkQmCUFdJHLx\nFjRF/NoKYdp0j6UrgFTUTxLKksIW45aChqiJSbu/JPpIFLla8UkMYHnXJ07za2+7mz/57GlWx2NK\nF91lsRzfRFeY6oFpkkJI+9EkkgWMNa24J+k64+kuOtri9Y2tMLaG5VLARJHSWsFJdI0ZiU41kdQT\ntxC1lNYN2NjISI8locx0byVMwJroUEMCs/kea/OaySObjM9vUd/3COc/8yD7FzeG432ZTCaTyWQy\nmUwmk8l8lXJoRLKn/8d5wdeezc89xObnHmzjhMY6ll/0QZ758/84TZaMjioWHEZddo+DnWQQe8lc\nUkOMAWfjNMjSgTNIPWFU14zqANUcrWtedcLxxrMzirKgqdOJklkSWFIZPalTqxFkYg1aFIhSe1gb\nKVSU0HaaxeimcQ5nLcfLkrqueN2HT/Or7/sc86rip77zJdxx3XFWJyVKQFsH2mI/Wddz1lx421UG\nnTuM1F2mIZbkt86xOBmy0cRCIyKReteMxVhLVQdOn93gXZ+6j3/+/76T1dGYwhmOLy8BsUdMjMGh\nsZhf451xxhA0vgTx/igGIajHGEPwIbnxutdMk/kvRktBg2fsLKtjx9UrI77pjmsYl+ZAGb+ReN+d\nten1aEQwEx8zBmMOCmjGpsu3dPsgFvOXpWNydIWzv/8u5J//EjPgYKuP0DjiMplMJpPJZDKZTCaT\nyUQOjUh2uJBW8gm+ZvP9n2T20HkmNx4HX9F1kaXN206yhe+hawOW6ArCNIqQSYVeDoyPLjGnUDik\n9vytV97Be177PrYLEyN7SCrNlnaX0RVFKpRv1oVAJ4S1AlnrAIsCnyoYDVhrk4MqTtUUY1hSGDnH\na37nnTz/miN863Nv4utuu5ZjK0spRhlQfNNzf6Bkn0aaW+gca7vGkvoUfzYpZpgUqeaE0503aZLn\nXqV85otn+eOP3csb7v4M91/e4shkkp67IBGl3GK7NyOp/14pnKX2moTB1BkmJpXx04pa7T5V0RBi\n0jbEDrKRFUpreOZVaxxdHUXnmIndcE05vxHTuvykeW2E1l3WXKIISbVLQ1STuCkpXWpM0lTHIy68\n42722zuUJbGnCjNw68PA4lBR+1DJ/oH37p/D0P7i+fSfP7TlUFn+Y8UOneNjPO7g6kBRvg5t9yi3\nZqCPf3Cwgff9DcNAcX9V97fbm83721X97XSgsH637hfEv29vrbd2H9Pe2uq0X7J/Ytov8wdYXVnp\nra2srffWxuOl3lpRDBTbD73OA/dVB4r7vR8q7u/fw9ms7yOfDZT5b+/1y/wB3r07UPy/dam39rXu\nQm/ttuX++Xj6wxNkYHBGJpPJZDKZTOZwkkWyJ5T4ASIQuHz3J5nc/leg/cd9+tDQlnM1T2nil41M\n1apaKVentDk7mwYCmADWQ4gKyTPvvJ0f+uxD/Ov33c/S0iQW3rclWVHYUQJNfVbX4dWdtS6cW3SR\nKdpOvAwUxImPjXATiK6nwhowwtrShI+cuczb7nkPN66O+KavuYG//rLnsb40xrmCoD66ywgHJspp\nE/NcEMa6DrPuPqVEJKrS9neJMWiA3VnFQ5cu83+9/u28/TP3A7A2HnNseSkV6XevjaT+MTHgTJws\nqSFgjKBqCEkgCxqw2pTzN0KDQTWkSGfXPxa3jwLkxDkmznDtyoQ7rlkDKzjbOMJM5wxLscs2Tolp\nFLEueplUNLPwvYhiTPOWkFjSPyrQy9tsvfvDuZQ/k8lkMplMJpPJZDKZx0gWyZ4EBLjw+rdz9fd/\nc1QzgudgxrJB22mS3TOTgtOIao06RIhdZUah2Z3RWPKvwn/5qhfzns88zPv3agrn4oDNpjNLJM4E\nwEQnmTQTHLvzaE1uoXOReY2xSQEK62I/mXOg0WUVO8AKzHzGyBWsT2CpLNmdz3nt++7hXZ99gDuu\nPsbzb7qK264+zvHVZSajgtK5eEyNExrjZExIdq7uzGIlWNKRopgUFHbnno3dHe4/f5mP33eGu7/w\nEJ958DyPbO5xdDptt9cQRcVmYEFr1EsR0yYO2ghn0dSmrUtMm8W2O823Apc29rf2+/jcsRMmheMl\nt51gaSkW9kcxDGxyj5mF64mvurQxzsVC/zaKSbw3TSwzvaRxKqYVRuMRlz7yWWrAZYEsk8lkMplM\nJpPJZDKZx0QWyZ4EBNj66GeYnz5LeXw5RS5T3rAVVxY2VmkFol5bv0nKkKSuspCeG5rsooIPLJ08\nwmv+5sv5gX/5BvTYKoJpE0sigkcQaaKVcuAU0pzG1hkVy/ID3nuCBkpXYK1FEXwIUShKMSURoSgL\ntKrTz/EYpTU8uLHLZ89+gd/90L2cXC645cQRrjqyyotvuZZT68scXV6iLC3OGAoXe7isxON7r/ig\nzGvPvPZs7c94ZGObhy9v8f57HuD0hct85oHzbFVzCgzj0rG2NFoQtxRnXXTUadQaWRCmEGkjlEE9\ngsHa5A5LWcY2cqq26w2TuH0cCkD7mhmB0grTwvH8645y41XLeAVrJHWINVMrTRt1bYr6G6+fiLSx\nTBbOoxHMooiXhi6IYEVwzmKd5cJ7PkYOAGUymUwmk8lkMplMJvPYOTQi2VA3z+FB8CgX3v0Rrv7u\nb4Q5CwJYEj4alaqddNk9lyYq2aybZKlq1o0kkYzU3eMhKLe86Jn8i79xnv/5P/0Z9uhKfChNhIzi\nUSPydMcUUim+hGRsC7E0P3h8XUVBxtp4qODxPvWbLTjcFMFakzq5FOMcc4GV6ZTJqCCEwMwHPnj6\nPNv3PMQv/vEHWSoKrj2yxJHlKZOy4NjylOVRwbgsqOqanXnN/mzGua1dtvfnPHBhg4v7XVfNUlFQ\nOMPRctI2wjW3zKXpoKoHXVrGCD5N77TGpL6xRvyKwmCzrxB8O/myERGbfTbDDgB8CJTWMHKGsTU8\n48QqX//sUyiNQGZwzkKIfWNN75MYkiOtcabFCzCNgJkEs6arTGx6jkn7TX1k43FJvbXHpTe8O573\nl/W+zWQymUwmk8lkMplM5quHQyOS3fxUn8DjwT/7Bapn30Rx+zWwX9NzibVFYAvCWFvqn9QRDZ3T\nrNlONfaRNe3zwcSvdeCVr3op73/VS/j2v/1znBtPkMJSo9ShiRd2nV9CLJsX4mPRmOZjH1nwzOua\nyXiCIZYuNw4zayzU8VqsmJQYDVE3w2BQComxTOcs3nuc94yKgtWpp1pdQoOyr56Ht/ZR3ePe85to\nEqniwIAYY9Q0SGA8HnPVaNQW5Hf3ULvifcAmIUoDGBudb9YYQhpAYI2kFjGYlgWBgA8Bn+5Duiut\nI00IUcRqzkkV40yKoBomrsSJMBLhzmvX+Za7bgSa3rTUP2ZMK4oJgqavzQTL2POWjp6cZcakuKU1\nbZ+ZGINz8TFro/i2tLTEpf/z17hhPid3kT09sNL39JmB0vkwsDb0CqoZKIMfKEs/MHl18TgDffdD\nHf1Dxfg6UFAehkr1h5ryB0reZeAYRvvbhaFLGVh8tGEDOnCOvu6vDRXyz+Z1b22/6q/NZv0i+jBQ\nTv+5vX75/h/N+iX9J1f75fs3LvXXjq4f6a2tHzvRWwNYWuoX8ruBQv7CDfzTYHBgxNBrP3CvB+7D\n0NrEj3tr84F7XVVVb215oOAfYG1vp7e2vde/j3+6d7S39pmNc721v7R8sbc2Gh+af0r9hfjD08ef\n+IMc79/3J4Jyvf879nij0/7v9hOBjIYHczyeFNP+gIrHG8U+4ccAmEyfeF99mD053v3Rk3AtmUwm\nk+HwJLL00P8RArDzB+8C67qLCs0Gi4KZLqwtPB70oMOsad1v3GSm+d7QWovSB+V/8z/+VZ7HnPml\nHaz3lCiFCCHUhOAJvqKuK4Kv269xrSb4mnldM1bLdTjKuo4CWRKTqrqm9j5+H3zcX1B86KZhAp1I\nlHq5jBicczhjsM5SuiJGLK2NX43FWIuxDpcinsZYnHVYZ3HWYq3FOUfhXNyXc1FssgZnLUInLjUC\nVdMZZk2c/IlGwWpaOq4ZTThSLLNcTqJzrO390uiqS3/i66I4a3BGmDrH2FhsgIkXXnHrcb71rpvT\ndaSJmykSadN5GCNIug/GSOcIu+IeGdNNvjTNfWw6/kUwoogxWCfU951h9rYPPA3e74/Pn0wmk8lk\nMplMJpPJZJ4svjL/8+fTFAH2X/dW6u96Je7kEl3EMm3QFoM1i1eW++uCu6xRntJ2SqeihKbkP+1L\nDDc+/xn8+395ij/4/Q/wq2/6JJ/HYMYFIxvjlj45tWK8Mk2YVLAiiApOHLeamldcb7kcLG+/UDO3\nFvUanU1GkNSJJul8FEBj95qmaws+tI4ZFY2DAUTSZQjOCSGdfwghOtM0oMQC++j8SsKb0XQc7Zwi\nIjhI19AV3WuIR7XJCaOpqD8N/GRlXHLb+pQ7jpV8+oEt7rnskNIQiOX8qKISBwoEoDDRoWaN4MRi\nFIp54I5rlnnlC67nmqvWmNe+m0wJ3VTKFKmMpxvVL9sKXt2aqiBxwkJb3m+MoI2QZuL1WbE4ZxiV\nBVu//sb0zskuskwmk8lkMplMJpPJZP4iZJHsSSbs7bP7B+9i9W9/C1E1SoKWwAFhTJqC/uaJGl1h\nB+w1Cz+3mkh6bqOvqYn9YlWNTJf49u95OS978TN4w9s+xp988gwf3JihycUU5S2LIbrENBiMKqMA\nJwv4zhdfw3R5xPH9igd2NvnkLBCM6RxuSeySNIIypEmYGgJBo+jVRHFUUj29BKwRQtAY6ySKbkHj\nV03X0YhEzfNt872JsR0xJk6vJMbWmk609p5KJxvpwsTMdDasjwqee8M6p45Nueb649x0+hJv+dQm\n86hCIZLil0YpTdO3lu6/V+68ZpUX3HqcZ9x8HGMsPijW2iSwxf4wS5pSueCoa/rGBGlFNEmF/KaJ\nyyXRrPkxCmmKtcmRZg1FYZlMx+y89QNZHstkMplMJpPJZDKZTOZLIItkTypxauHur7+B1f/6VVCn\nXpVmKuWiUJYcWAdEsANl/836Qm9ZG8HsRKu4X9NNxVQ4ev1J/tYPvIJvP3Oe//jGj/Oph3Z4aGvG\n1jww94FSYFJa1kbCiWXL9cdHXHtiQjEtqb1Sjkq+9obAFz6xwU7pOidYmg7ZCD9xEmTS6sKCQ42m\nxygAhhB8Eo8abU9TN1raZ+rjisJRvE4NSehSEDGoBpw17eBPQfD4A0JUQ+NK0yTKOWO47eiU606u\nghGcNTz/jqs5cWyF02d3ePjyjMs7FTuVEtSjXhgZYWVScNVqyU2nlnnpc6+hKB3ea+xSMyZpms0U\ny67SxxjTapimFcxih1Rz7s3FNa4yY7qhA5IGANDEN4skki1N2T747shkMplMJpPJZDKZTCbzGMki\n2ZOOoFVFfXYDd2oN9mfdpEpI35NcY12E8ICDrNmmzTResY10IgvSWLFM92oHD9ayfvI4P/rdz2dW\nByofqFM5/15VU9WB2sc/86rCB2Vee6oQ8D6wvjrhx68/xv/2ps8TnKXSONWyLblrBTpawStogNRl\nBkmzCz6eWwiQ4pdBNbrP0gU5awlE91Rb0yZdZDHu16TDSnSUSXqehs5BlsQ30jTR0hmmRcm6NXzL\nXTehqWOssBZrDc97xhIveJZNcVJaZ1fUAzV9TS9XmkJpjKDY1ilnGgdbU8hPE6k0bTm/gbbYXxtH\nXiuaSes+Q0wSyJIwZizlqGA6Ljl29Ajb//b1OWb5NKR1BC4w2C8/WIDf30wHXuOBrvtHnWw6NDRA\nB0rZVQdK+ocGBAwcY2h/8hifO9RGN1jIP7A2VNAP4H1/vR4ojh8q7t+f90vi9wdK+uuBgvkP7Kz2\n1j5Of+36I/1C/WNH+kXjx44e662tH+kXn0+m/f0BuKJfxu1s/3W2A2sy8JoOEQaGNng/tDY06KB/\nD93A/a8GBguUxfA/Z4auuSz7AwLG5XZv7fLAcd6wNeqtfUP9yOCxM5lMJpPJZDKHj0Mjkn1lffQX\ndn7zzaz9D9+XYpVNez+kvCJ47frGOuWJdmzjgd3JwQ+MutBdJiluqWnipdH4vVFw4EYlgSrqRnOl\nsI4ggnOK95555SF1go3GBd5r/CyvilsV7liyfGLb45zgVZOwlZxwSYxCYwQyxi/TPLs0pRKAENqe\nsUaA8qHGGYcxltI6qlATiLHH5jY1BfxGDCEJbBLLv1pjXjzeQVeeM5ImURaMArzy2SdTAb/BWkPh\nLEZipNM0kcZkc2sdbok2Advqmd2j0vTLme5cTPoO4cCQ0uaetHa69GMzW0Ag7icJZ1ZMO9FyeX0Z\n/dRpZr/79iySZTKZTCaTyWQymUwm8yVyaESyraf6BB5nNn/7j3Df+AKWXng77Ox0Souhi1U2DpRG\ncFrMXnb2qC6a2fwsZiGuqd0EzcZZZgRC+upKCu9jkX2hUIEnpBigMvcV9bzCe+Xo0RWCCLqg0v13\n3/E1/PzrPspHdmqKsaMGKu8XzjcKWd0wgNAKYe3ppxJ91fh4HUJ0SRnLKBSIFFgnzHzd1rSlWn5U\nGudCdGhFx1mMX0aNLqpQcd9QOsPYORwGO1Ne9aJref6zTlEUDtNMoTTROXbx0g7OGVamI4pxmWKP\nSu1D7A5LbjKDtKX+jfBljLQil6TIJSqte6wRC2OUlLaLDLqXkiZ6KRKdZ6RophGssThnKcuCaTHi\ngV9+AzO+0sRkuOqpPoFMJpPJZDKZTCaTyXzVcGhEsvNP9Qk8ziiw+3//J+789/8IEQuhPthN1hT3\nX1nm33wfFkS0RSeZ0A0DYGGt2edinBMB6xBXUug8lutbxYToBPOVZ7Y/496zWzyysct3roxhOkWc\naac0nlhb4f/4O9/Af/j9j/OGT11AJ1HImSWhLApYUdAKQZN41YhW8Xp9mjwZQozfOBEKdVw3snzd\ntVPObCsfuxTAWqpQ4VOHmaYCMmlEsHSvUhAyHU/bW1BYw7QowAvXTApe9Q3X8cybj6MKzrnUjx/j\nmmFW85aP3M91x1Z45rVrnHSGonSgxEEDyfVlSC8FJr5k6WBx8qXp7j9ReGt+1PQSCLTr0JT2N6LZ\nQpmZCGJjN5mxcZplWRasHz/CI3/wPh746D30A1KHGwFuf6pPIpPJZDKZTCaTyWQyXzUcGpHsK00A\nANj73P188VffwE1/59WwvQ2ETuxqonew4BpbEL1IIlpPIGue3yw0GcbmqJ3oEkcuGnAFRj0FAVWL\nrwO1UZyFnVnFvY9s8elzO3zXzVuU1qJmDM5gTIwlrqxO+Hvf+0Ke82en+Y133se9u3PK0lIFj2iM\nZwaNAphP0cruHOPJicbpj4VxEJTnHhvzkmceY32l5KZKWX9gmw+e3uYiFmsMnkBIrUu1NnFNJXnJ\nIE3JFJEYo5QUp6wMd92yziuffw0njy6hmDglUgTvA2FWobM5Zy5u8dEHLmIUTq6OWF8dYWzMPoZW\neIsvjGmERxFM81gq54+utu41bMSv1heYHGak3jNajbRzDkp6rH2uCM5ZpqtT9MGLnP7Z/4xdOJ9M\nJpPJZDKZTCaTyWQyf3EOjUj2lYdgUB761T/g6IuexerzboXdXVo1y6SuMpP6yZo+MpPKrNoJlo2Y\npgvt3Vf0kzUuNG36zQQwoHWKXBrQEqcBKcCrxdSCGQlXH1vlpTdV3Fgqb/vUQ3zL7Z7VY2tUkyl+\nJAgFqFCUBd/+itu58/aT/NqbPsPrP/sIhTNvsMWQAAAgAElEQVR4Y5JbTPAhlujbVNLVOMCaOKR4\nYVQFrlstedWLr6cYORRhPIEXP2fKLVct85/ffYZNr4gNiBECAbMgXKl24pigmE7KIsyUH37Fjbzg\n2adwzqGAlegAm+/s43f32Nve5bNnL/OJMxt8753XsLI84shK0TrhmhIxSeJkFLPMQto1Tp/shEmN\n4lpSxBpTX/MqNHqnNC9bWlBSmb9ppmMajDFYE8W+0WTEFMu9P/e6NCM0C2SZTCaTyWQymUwmk8l8\nOWSR7ClFsMAn/v7PsP6KF/Ks//W/Qqp9QFNxP7E7rClzF7o8XxPFa4SybpcLLHSYNUJZLO2KS9Z1\n+zPRUWbrmmW7RwgBHwLL0xEnj67in30j+7tz9vYrHtne5/zD57i8M+O2JcuxScHSdIRORpwalfzD\n7/4aXlMWXNzc53P3X+KehzZ44MI2Xzy7Q+0DVmDkDEuTgqNLjmfdvMbKyojROIpiQbWd4Bcr12IH\n2NGVMT9563GcMWhQtrZnnLu8z0c+f4nLuzW7M8+8DqgoZWG56dSU646vcMOpZU4dW6ZwhtorEgJ+\nVrOzu8+FS1vcf26DBy9ucXRtwvrShCNHV/ima4+xujbFFS4V99u2S0yQpJV14ljsKIsdY9rc4vZ7\nabdpryzFVRvHmFlQzFrHmBHERuecbYYK2IJyVOA+/gAf/ulfSXJnFsie1piBSYEDkxntwFMX+/8a\nhiY1Do23HBiqCTzK1MvBcZt9hnYpAweSgd3JwLUMjuV8jMcduo6hKYowPJWzGth2NhuaZNmfuCh1\nfzLj+/f7Uya/MDnSW7v1yHJvbWWlP/Fy/cjJ/tr6en9tbdrf33J/eiOAtf132dAtG1obfIsMrA1N\ntxxYQnVgQujAfbXFvLfm5kVvbT7w2gGI7PfWzMDvpLH9fw4Vrn+cSwOTP/9oo39fXz14NplMJpPJ\nZDKZpztZJHsaYIDL77ib+04d5eYf+hZQn0YbcrCjDLoiK1iIW9L1jYW0zWAE84ri/7bXrHE6BcAh\nOsLW87Z8XlXRAsrRiCUPa0dqrprN2drd47MPb/DJ8zOKaptjBZycOI4ujSiPrrI6HvPC245z563H\nmdWeSzt7VLUnhNBqdQjUCrWPayG5qZrLbhx0JkUUY7l+FJGWlkZcfWqF59x2gqr21HUzXTN+CJqM\ny7Y/LNSB+e6cjY0dzm3s8MjGLo9s7OCDsjoteO5t1zAelbjC4qxQlI6iiM/XplQ/nZtZsH9JbO5P\nBfvt6XZDKtN5S7qXLKwh2k7AbAYAYBqRzICR5CaLPWjOWorSsrS6xOd/+le61zKTyWQymUwmk8lk\nMpnMl00WyZ4WpOjlb/8RN//Iq2F3u5tKKSTBLIkhoRHLzEK3GAdjlX5xe10QypKCoyyqUOmrSe6y\nAOrAWCT4FAOMMUMHqA8EbwlLJcsrY9bWpuzPai7vzDizucfdl3bYPneZGx+8zFVrE44sTyhGDikK\nVscldeEI6RQ9iveKaMClSZFXuhUaJ5Zp3FhpGqSma1IFK4q1Fh0BIeC94r2n3tlnrw5s7864sLnH\n2c0dHri0w6QwrC+PufXaoyxPR5RlQVHYGL20gk3l+MaYpDk2TrDF81JMI06qHDALtXHLRjdrBhUg\n0cHQlpGlC0kvYyNuSmryl7S9MwZnHYVzTFeXmVzaa11tmUwmk8lkMplMJpPJZB4fDo1I9tjCQIcZ\nwaKceeN7uOqbXwx7uzGjoosiF13s0nuwNrrOWOwaa4SykKrHTNyPSNpfOpwxqfPMJEFO0/fE3Jco\nOLtQrp+EokKxwWNDoCgLRpOSEJTjqtw4r5nN5mxu7/PpM5f52OaM3Qe3kHnNioUbVsdMSkvhDKZw\niLMYG6OEKoJKPI2YZFxUnZoeMMDH7TRo/KOBUAfqylNXnnlVszObs71f8flzm2zPKuoQKJ1lbWnM\ni247xXhcUjiLczY5tWLfVxORXOwJMzZGVCVNmWzkK0OKUUojomnqQescY3JAXFuYXGk6l1n7kraO\nMxvdZMZgRHDG4qzBOst4dYnJ5j73/NSv8ijhtUwmk8lkMplMJpPJZDJfIodGJBvq6/nKQ7j/Z15L\nMRlz7BueC3s7UVUxulAvtpDl8z45ykjusSu7UmR4AqY2eUZZiGs2j5lU8N9M2lzIGTbDAcSAVVDF\nqGIAp4HRKDBdGrOyOmVtfYmd3TkbuzM2d2Zc3pnx0bOXmc9qwnzORJSRM0ycYaUssNbgnE2OK41R\nTwDpxCARIYgQFHxQ5rWnDsrF3Tl7Vc1e5ZmFKA4aY1hZKjl5ZInJuGA8KiicpSwdkkrwkS7O2NxT\n04hxdJcsKQLZplqlc5e1fq6FfrLWeYa04l/7imhylrXtZiw4+rq4pZEolDlrKZxjtDRmpVJO/+zr\nmH/xYWx2kWUymUwmk8lkMplMJvO4cmhEsuuf6hN40hCq//0/8BCw9mPfy9K33hXjl4tOMUMUqkzj\nGJNuGmbz/ZUCGKQI5qJYlvZj6eKa3oOTLtYJcX2x/6xZu/JbAYNSKhydeo6Gmut8jfdK5QO7s4qq\n9swrz7yqqKua2bxmd3ePeVVTp/ikqrLvPSEoPoRukKeAc7YVkZacxVrhpCs64Sm257fl/4v602KJ\nftcL1tyuOAyA9mHpStMXdhIHjLY5yih0SbeZNvttTpgko0k6n9Rf1iQu294yEYyY6CizFmcMhbUs\nra2wfn6L/X/yC2yfOc8xhGNZIDtcmIG/ZkO/oJx+Hzgy0HhuB4rDdaBVXR+tFH+glV2lv+2QUzEM\n1OUPDSEYOvJjXRta1YG14Pv3MAydNFANFMJXVX9tqKR/aLjA+y5Nemu/+1C/ID5cfqC3duFC/7nn\nq35JP/V2f03K/lrZfz/ccPXAdsAL71jprb34Bf1j3/m8q3prJ4/3BwTs7Q/c13rg/TXwPh4asmDd\nQHF/1f/9sa7/Ohk7663Fbfv/iW1o26EyfztQ5m9Mf3+bjzYlI5PJZDKZTCZz6Dg0ItlXV7Qsyigb\nP//bhO09Vr79JeDrZEMCPCmCmIQrNK2ZuN64vQwHjWDNxMxW7GpUmoXJl9ZGsc1KM1qSVknS0H1+\n1cZZtnCMtmurcbU5BMHho4anMWpZOktdWnxQvA8sL4/xIeBV0RAIGrvKlH5PWVunJnrgg7NqFNhC\nEvNUtfvAnK633U0rakVRrRXObLwfTcK13X8rIMSut66jbNEiJouhVNrut04xaw19bfxyYR9R94xC\nmUFwYnDGsn76HPs/97tUZ8534lomk8lkMplMJpPJZDKZx51DI5J99RElka1f+X1Wvv1lYAuoq65f\nzCwU8ZuUlZTQFfarpy3jh85ZBp3jbOFYrVCmgLFJHCOKbW0vmekcZ4vP04VhAA1hYRsxiARKa/ES\nsCJYKwQf8NZgrBBCnEwZNKBBoxNM9YAopKqE9FXpBDHvQyuQtc8Jik+nGlTjdMnkLVNZuC2pNX9R\nB9N0O7tplM0zTXSqQdpfJ4opjVEvCW0Lopg2BwyNQ627e+05pPClEcGKYIylnEzY+7s/G/XP7B7L\nZDKZTCaTyWQymUzmCSWLZE9rolB2/id+gSN/7/uwVx+F/f1u8qWlFV+iWEbqE0uCWCOGNW6ztlOs\nUYBkIYK5mBmEVmBr3GO6+GdRHFsUyNL3IcTnhRBPLj0mqd/LKqhJ0cIQEBF8iL6xEJRgo2DWaW5x\nHcCHkA6nqIlimmAIIfaYBZ8ENmswPqAoNk2gbLQqaXvChHYQZatBxWtTjfVsrQjWOuZkQSCTGDVL\nEUpUCZL8XotxywXjXjTvdbIYSaRDBIvBiuBcyfJoxPQN72fWnlMmk8lkMplMJpPJZDKZJ5Iskj3t\nEeaf/iLn/9G/Ye1Hv4vxS74G9mfRKZY0lvgnCWBNrUpb8G+Sw8wsRCPTY6b5nsUcIwdinJLcY0Zo\nrVmSRLp0flEQS71l6mPfkq9jXHJhoKZXbQWu5rQNBhUPJnaKxVim4oMmB5eiKuk0NM0cSFFKbVKn\nUYALjTAV0mNtPFHbWQdNwX4zyRIaTTG62SS58qK+2GVJZcH+pbLg7Grdd0QBrbnJ0pnpNN3XLtmq\ncXqnxuOKpEmW1lJMxqztzHG/+HpmH/osWSDLZDKZTCaTyWQymUzmySGLZIcAQQhbe1z6V68lAKNn\n3cTaD76K4vrjxAxfEroaV5g1aepl6gZrBLE2psmCuGO6Un44KJqJgQXRB6vdNkYXtjVRGAsKNeAD\nOq+o6yh21UHxGt1hTTSx0ZxCiiY2YlZIQlqdFCZNscuARv1NY2dZCCHFK4Xap+8lCnaNA03RNO8g\nimzGxGinSRMkpe1kS5qf7Yxx1hD3F0I8P20ilDHyeSAHKjFSqSnQ2SVQFwOZ3aAAIU4QNcZiBaxY\npCw5+YYPEP7wvQRg3sqImUOPHZjNO1CUP1jmH/rbDZX5M1SoLzqwHYMDAsLAey0MFPLbgfMZOGuG\nD91/rg6c9+AQgoFG/oHNqP3wNc+r/nH2Bkr6h/b5ubP9K/xnrz/f37A40l9bPtpbkvHx/trSwHZ2\noHzfjnpLYWD28+m9oVcFTr+3X1j/+ree7q0dX/1Cb+2bv7F/jt/z127urV13XX+7nd2qt2ZkoMw/\nDE2v6K9J3V8zQ79TDBfyD+1z6HdSHuN2+e/qTCaTyWQyma8cDo1INvBP1a9CBAPUn76P8z/+b7n6\nl/8pjMrYVRZ8ildCKuLqJl42PWVG4ofuVgRrirLMYqN8JwA1ItiVHwqatcWcoiaxrQgwckhhKeYV\nrppjK09de/brkGKVyXSWduYhOcRIzjFSJ1lyjYWAJodZqLXdxidxLAUw0wdc7TQ9l8QwiRMBjRWM\ntW3kEaIQFoJi0qfjZpBd7DbTdupfI7g1Mcu2jo2FxGnjQEvPiL6y6PCT5pgBxBqMGKwK1jnKsmDl\nC2fxf/je5K7LH7gymUwmk8lkMplMJpN5sjk0ItmDX+bzG9nhUbwVTziGIa/Hl0pUaC7969dy3atf\nyeozb0gxw5q2OEx8FMSMpHKtRtTSZpRisk0kx9linnDhS/uc1nW1IKQJ3X+RV9O51EKI7pnJGPGe\novYUVYXd3cVXnqr2cbJlULyGNJkzVa2lfGZb0g+EOnRl/amkH4mOmmiQS3JbcoghUSBzhenijDad\np5HOANdENE10gtG6vzrxbDHBqk2xWOtU6/6E0DxT2jipkiZnNhFQQzvB0olgyxHF5h7hXR/kkTe/\nH5/FsR6nnuoTyGQymUwmk8lkMpnMVw2HRiSbP9Un8LRD2P/EvWx+4l5OfstLuenVr8SsjKGaQ11H\nZcdIZ9ESSdHL+Nw2oqmBtsy/FcsWxJq2db6JaaavTeN9K6otPM8kz1VQcBZKQMeU4xH4mvG8Qn2N\nr+roMKuiI8z7QMDE1KZPky4BdRKdZ9pEZzQZ2CwYMGKS8Bev0zRDAYxpXV+CaR1oapooJ7F4X5sa\ns4V4ZKsLLgwQaNxjV7rI2q412geiyS4698Sw0EFmsc7hjMV+7D62/593MLu02e4+k8lkMplMJpPJ\nZDKZzFPDoRHJsoDQxybV5syb3sOFN72Hm/7u93Hirjtiwb6G5F7SNGWSKGwZ0znLBNoplkPIgsCm\nutBRRhfn7Damc5tBE3uMNq2Fn7VExgHxivGeInjG+3uE4PE+4H3sL6tq30YyQ5p8GTvLuCIuCmqk\nPQNt1xfsXkjbh5bmbbYOOUnTJdWH9jpb7U9jRLLrUkudY60KFg/YDPmMT2v61eJPphXHopvNiDB6\nZIv5H7yHzY9/Pr4s+d2dyWQymUwmk8lkMpnMU86hEckyV6Kp9UqwSaw5/e9+i60P3MEt//3fgGof\nfA0hamKoB5scTcZGQcgmgUxSL1lb8J8UoNAIXU2J/ILw1AhnjTC2WPxvYrNWzJimeKdXsC5lHJNr\nzRrwFrEW6wNWPXiPhjqW8aumcn5N34NqoBWsmtMMJHFLCG3UUaKJLsUgTRODlEaUSrJbug45IAiS\nthV8CF3nWFpvNLIQ6KZ3xpGXbSRT2v+NrjaDMHIl6pX5v3gtHigWRbvMVy4yUNzfxH8fr0M8xrJ7\nGC4yH9pWBsTbRQm4Wxs8oceyNJh/Hy7p76+Fge18PVxYPx8o6a/m/W03d/u/jb/43v3+Dteu6S25\n5X4hv05P9p87Wu2vLR/rrw29JuW0t2aHyuUf5bWXgXZPHSj+P791sbf2G2/Z6639zhvf31v7x//t\nTb217/i2O3prQ2X+Ug/8bThYqN/f7NGK+4c21qF37eBSf3Ho1sp0efjYh5zXX+oPYXjcOTnw+/BE\nsPTEv0ZmNH7CjwFgJ/0BHo/7MYonvgnYmEf5nX2cccUTfxzrnpwyF5c/tWUymcyTQv7r9hDQtV1B\nMZkymk4ZG8PSpQvgPZVCJbAPbHzwk7z9B3+Cyc3Xcut3fyvH77wZme8RfOz+8qIgVZrwqEhZRNHM\n0BWnNZMxW2eYjyKaaVUg2hNqxbKFSGfq+YIkuilgUy8XAiZ0hWAmQDDxnagBQvz4UtRVp0IF3065\nrJtOsuQqC0HxJsUyNWYhNbm7fJp4qSlOKWn6ZYxWNvX63celA/FJVXwy4zVRz8alFlA0gE/Ha3xs\nolG0jDVkFlVYHo3Y3qn4vbd8gI+968OMgOlozMRZVoDjIhwFjqtyQ11j6uioq5J/LXsoM5lMJpPJ\nZDKZTCaTeXLIItnTmiiPlcvLTNbWmSwvszSdMAqK7O8y29xg7j0h6U+iYIkVYHtfeJA//Zn3cu93\n/CW+46Wb3HbsDJUGkJAGUQaMEWxVIzYJXM51UUWzECs0ksSqxjVmFuKGCy60RixrOskWp2NqF2ds\n/8u+0Whza56nJgloRNeZhGQTE0QDogGr0U2mGt1k1grqpTWoIXR5yvZ4qXdsoTus7ScDfOgcZ9BM\n3hTq1Im26HZRn8Q5TdMrtTGRGRrHnQYBMYxLx+9+sOZP3v45Vh/+MEtAYQw2ePCwZQ3bxnDaGEpr\nuUEmnAyeU7VndT7H1TWzuibdgSfkHZbJZDKZTCaTyWQymUwmkkWypyVRlbFlSbm6xqnbbmM6mTBS\nxezvE7a3CfszPBAwqAY88cX0RPOWBey1z+M3Lh/lZ3/f8dG/Hlib7rIfKoJWiASCepCAoFhJMRcR\nMC5NnEw9ZCEJTpIUoUb8alxnpN4zSUJRCAudZ025lzkgWKWD0YlZLIhmoXtO4/hva9UCIUUnTYpd\nGpEkEi6W7HfxUNUuUhlSfLLxaYUrBLVGSPMhXSvabqNJPAuNMtbEUAOoWEQMiqEoLGC4+7TwE2+/\nzH8xOsq0ubUCBWA0YBBcaIRA5YGi5KxzfL4oODodc2peccvOHqGuqb1vu9EymUwmk8lkMplMJpPJ\nPP4cGpHsiU77P5XyQ6vrEB1OFsP0qlOsXH01y0ePcXR5Cdndhc1NdGMD2dqKP3vFJc2qaXeJ8kzs\n3dpYvYX7xmPmu5t84+uO85Nfu8+3PmMPsXvMwhzxHq97OKuoDRgNMYJpTNdZ1rrJaHu3urslXedY\nc/e6Bnuacvx4gaG72MY5tngDWk2rEeRM94BIjISG2KlmggcjjbaEpMikiKTuIkFDimAuHEJD5wJr\nzkhTbNOn7eNkzYBK4yiLgcwmHeqDpiSqSZdim2Y4EMfSeMTnH5nz2j+9yO/co6yMApvFMr48Rjm/\ngAbFuhjJtCluaozBqGLqCjGWbWfZQzgznTAblVw9n7O2u4/MKmahjvrl4/BuXXzfZTKZTCaTyWQy\nmUwm89XOoRHJlp7qE3hC6QSjpZtvYenqq1k6dYrJ8jK2ruHhM7C9BZtbyOYGdmcXmc9x3lMTBbEC\nmKNttZgb38iDa8/gAsIpV/Lp2vPqP3bcdfeEn3zZlJffMqPSfeZq8L7ChBpnA1YCxbwCZ0BtNxEz\nLIhfizFGka6Iv4lSyoKbbFHdbLvLaF1aQDzG4tjKRdVmcX+k/YlJReUL7rckfMVVbfW2eMQFUazp\nHBPBJ9GsEcqCNj9D8E0XmRDSdk0zv4ol+sAEkXjHJ5Mlzm9W/Nybz/Cb79qFtYK1JUfpAluuxK9f\nT/XIBUaiBF9jjUWNYlTQ2qMSMIUDBes9UhQQaj49LrmvLDk6mXBLVXHV3j6jvRn71Zwsbx0Shkr6\nB4ro0YGC/6FSdp0PbNd/7qMJqXH4xUEGuvzjxNcrsEOl/75fgB8Gjj103KEC9aH/IDJUlu4Hzm82\nUMYPMKv660b7a7//qf75fHynX75fnryqf45LR/oHPnKqv2bL3pKs9Uv/mfUHBpjpwP8ThoFi+6H3\nFyCuX/gt/f587KR/jv5o/z0239rsrf3kP72vt/a5e/vb/YO//7Le2t7ewHu7P3Nh8J3tH+X9Xgyu\n9rc1A0MNhrYburWzRxsakMlkMplMJpM5dBwakWzgI8Qhp/uXdnnTTZQ33sTo2mspp8uwsw07O3D/\ng7C9DefOwf4+zOdQ11DXmBCYKswBi2BRCuIUxUDggeu+k987ficr++cpixWWTcXUOj7jK77/Tyou\nv9mzpBP+7IeOcmxpD+93MfUMZ2pKt4+tPM7WcQCmAXEWrO26yoxtzWTJ4pV+XhDKWsfYFS6zJp7Z\nrDXlYM00zcVPxM3j7S409YnF/QZCErFCqiLTGH9MopdvJmOm51YhJEEstNHKEJRa49c4TTO585Iu\nF1RiVFMlxkEpMGIpipKA5X2f2+J/eu1nYGXE1MLkVIEXy/Y88IJrV/mzh/a5dvUZXHXpXqpqiyLE\nmZYigphOSPS1x4jEiU+zilA4XPDUYjjvHBfLEUyXcMArZjOu2dlhvLtPXc1zyX8mk8lkMplMJpPJ\nZDJfJodGJPvKIkoa7uQpRrffzvi22ynW1pAQ4MGHo0i2tRUFst1d2NmFqqKLPjooDKb2lE0ZPYJH\nGRGjl+ePPJv77IiJCOLGCGBQQnIdrI3AB8+zf2WLf3XXlFffuczaaIf9+TaqAWsr6uBxNuAs2FBj\nSlK5volt90Zij5g0PWJJNWtFLT1wvZ29q/l5Yb0Vy654bHG0ZBLWNDRPaXrCUiQyBEJI4hiCT0JY\nIEYtvSq+EcMWBDKvUC/ELWMsM4Y4Q4il/F6TR08shRszKkbcfd82v/bOM7z7YzNGJ4s4SRPBiAMC\ntRjG5QjRfXZG68j4GKbaanvjatUUmY2iotYVYh0qNkZKBfABda4dLGB8IBjDe9bXuGp5yu3be1y3\ntYXs7FGHmiyUZTKZTCaTyWQymUwm86WRRbInnSj+TF78Esa33cbo1KnYAba5FYWxC+dheyc6yXZ3\no3ss+PZ57WRIsWAsRgUbmqCJsItSrDyPL6zdzkMi3GhL1IwQqWK8TwTFpLBgzWQ04jXv2eXnP7TD\na+5a5lvvOIEGKJhhpcJrEstMYKQVxkpylJlUsi9xIuViBBPpHGWLzVeNe4wrRbIrBLJGSGsFshDv\ngQ8EHx1jISgq0fnlQze1MhCdX7oYnUzuMR+UEAI+pH2kQ4TQxCs1xitVYnk/IMRopcZRCDhTcPqC\n8kt/fC9v/fgOLBeUJwvExMmggXgPLJblQri8K6yZwIVQcMPSNdit+4B4ni59rTUW+RfGoEGhnqPW\nRCtbUULw6DwQggdnMdah85qHjOHc6io3LS/zjN0d1i9t4He2yUJZJpPJZDKZTCaTyWQyf3GySPak\noogrcDfexPJLXopbmsLuHly80DnHLlyEvT2YzaCaJwVHFyZAEp1bPsTesCA4oAoeA5QIl44+j48u\nXcNEA9aO2DcFmAIrhhBizXwQAakYqaecCg/WFT/2ti3+8qcMv/i9JxA2mde7FMbitMKHGqHGasD6\nCrGCGAuFS+6yhVhlO5FysV8sLBjLrnSLhW47XRDIGnHQezT4KHzRJDM1RS+jhuZbx1j6PrnIGjeZ\nD9F5Vvs4HbPtHwvxMR/AL5jXBIOIJWDjKAUpKMsxZzcqfviX7qEKhmK9xNgojnkMAjix0Zkm8bUQ\nNaxNCs5Vyv7yVYwPvB86McsAdVAK6oUEqxDmM4xzGOswddOXpvi9faSweFfwhfGIS8UqN4/HPPN0\nTT3f7+0/k8lkMplMJpPJZDKZzJ9PFsmeFKKYY1dWGD33uUye81zc8jJc3oCNy7CxCVubUSTb2oKq\nhroiKTup2J6opDRmLGugHMF8hsFQotQh4IAzx17MJ8s1prOLWDdBxEIoCX6OmBIkCmWxA79E1LBc\nWLy1vPVizY/81iX+4SuXed7VU6p6i6reo5AZEHCiOCvYELAmdqNROPCp4F+IRdKGhYJ+onq1aCxr\nesjadn2N1yskW1cADXFKpYYYgSS6wTR1jTW9Y0FjUXhIcUvfTKxM4lgzvTIExQPeN71j2jrMvAoB\nk85JCFiCFog4lsZjNvbg9977AD/7houwPsYZSa4zg0URI9j0vMLEuGeFoQrK8mTM6UsVF5eOsT45\nge6dTwMXFEvjWou9cjWCQdEQcAFsWaAh4OtZirlaxDqCsxh1aYqnsGHg40tTTl19FUcuXsRvbUZX\nWxbKnnqGWvFbV+UCdqC4f2g7N1BFPlCeH/sD+8hQC/4AQyX9fuB8ZOA44h/bMYYK5nVgLQzszw+s\nzauBlnegmvXb6U9f6t/v/3jfQPn+9f3ifl0Z2G662luStbXeWtD+fZ2sj3tr863+9U1W+9vVA/fB\nPNrvfTnpLfmBsnxdPtZbk9D/50Kxvd5bC0v9a37tW7/YW9vng721H/8HX9db29zY65/LwK/UUO0+\nxInPfQbeswNrYWCMxND787G92zOZTCaTyWQyh4Eskj0JCFDcejvTu+5ifMutmNEI7vtiFMgub8Dm\nBmxtx3L+vb1UXH9FAb5J/wxv/sWvGmOO4wns76MhMDaGKtR86vjzud8WrIgQiine1xhTpPL6AMHE\nSV5iCIT44UcrjFjWTMWbL1S8+d99gb9253X86F3LPP/aNWbVBjq/iLMV1nucNVijlMFj6xqxpiv2\nNzaKY80kzOYmNB/mFj9kaNuyH6/Z181y9G8AABbzSURBVHGCpI8CGTSiVxS2vGpbsN9Mq6xVk9AV\nxcgmTln7xk2m+DrggdrTTquMYpt0XWQaO9UUh2IYjVbY2ff8xp+e45c/uMX2w5fg+CpOwIhBJUqN\n3jicRNFMDGnyZZyqdmm35rZTy+j5C1w269RL11LuncMR+8vqFOQsEUL6mGaJHXO1gMwrLPF4iCBV\nDbVHXRQk6/0ZxZIQTBT53n38GLesLnPLxiblmTPU8zwFM5PJZDKZTCaTyWQymcfCoRHJBqbUP81J\n5fzHjnP8v/kRimPHo1Ps0iW49EW4517Y3oq9Y7N5jFY2xffCwvdy0IXV6B3egyuj08Q5jLVoXVHM\na37j1NdSzrdYdhP27RTHnCBCoRUaaupQgq8QAsY7CDVBfXJwVayqwtXwjsslb379BWYf/h1uCR/k\nN3/6pzh14gTMN7C6h6WidB5jwInHmBpjBCOKFTAm+dUkTofUBb0s9vunyGSKZapqG6MMoSvWb+KV\nwXePLzrJKg2xb0zB+9A+XjfOsqCx1kyVOjTPa7rHorikyTVm3QjF8p6P38M/+e3fguVnYcbHuWZ9\nGU6W7GrqYxOHE40ONkx0oonBpdfIYzAibOxVlOWIrzkx5syucvmGu5ic/zBpDEArjFUoLr3Iml5k\nkxxyimJVsVUVb56x6MYmwTrECKqBIAb299mdz/nUeMw9p05w9MbruXVrl/UPf4g4ViGLZZlMJpPJ\nZDKZTCaTyTwah0Ykeyh9/VJjDY+HPPDYjx1ljulNN3Py619OceJkFMcunIcLF+DiJbh8ORXzV6l3\njNQx5g9mSVRjtLL5PoT0s43imbOgBagifo4FPmrGrOoGtZsSxCEmUEig8oKKwVqPl3QsDSAWUY+T\nCi8GZ0pqB8aOKYxlZX3EykXHr//4a3jZN/1VnvMNd3Hk1BqV30KrCiOeShQjiknDL50VGl+WtIJZ\nMpEdmHzZpS1Dcpgp0XQWfHSSLfaP1UkEixFLxYdAFTphrY1WKlR1QIMcEN28xm63gKTEpwU1TCbL\n7OzVfOgT9/I7f/gOPr+5yYniWs4VJp6RWKxzWG8QY9I0UcGIUGDQVLqvIhixWA2IscyDcP/5PdYn\nUy5e3GVjeZXr0jXWQJnuQSNgGSQNDIjbFOnRJm2Lgvg6iWs1wQuytY0ZjVDvUWsIhSVc2uDyEeET\nays879Zb2T39AFU1+5KEsi8nSvTl/t7d8GU+P5PJZDKZTCaTyWQymcfKoRHJ5IqvT+U5/HkoigFW\n73wBx1/yEo4869lw7hycPx+/XrgQJ1lub8feMZWuuD71YbUxRdWuz6j5XpJoQ/q5mQBpbXw13S14\nAk4EceNYaWSFulaMEYJYUI8TwYsFAj4YjIbofqKiMiUT46jdGMwY0RplxhrwyFveyLm3vJETL3wh\nz3nlS7jqpiPUlSeYENOhJrrIvICIIiJtarS5NAUkxTCjQKYLzrIUgwxNG0wXjQxAHUIniBGdZrGM\nX1MJf4phel2YWEk76VIV0CSQqVA4C2bEW9/1Id73nru5uLGFAlcDF9QDBjWOygNYKoSS+BqIWAwx\nDorGVz6KV4KzllmAwggXduasT1codYtHvOOZ6X1SpGa4xjmmdIJUrHTrrIOm/S6uxXJ/xRCFU1El\nmCiwGXFUKKbcZb+qeOTmmzixsoz7/BfY39z8Cwtl2X+WyWQymUwmk8lkMpmvBg6NSHY4iALZ+ou+\njpMvfzlrN9+CzOfwyDl45JEolG1swN4+1HWaUOloY5XeR2cYxJ+bkuymuB9AUleZsbFgyxowZfye\nGfMTL2GigUIsagtMmiop1qEBjHpQSYa1OD2yEEMdPCIWEYMYRzAOMQViCxxCASwBk3i2bN59N++8\n+26+/oe/h+ufdRNSOCrx+MrHOjKb5B6TqtW0+b4dY9n9rzQ9/p1jrOkSi1VmzbpE8Sz1jwXVLk7Z\nuMWUha9xn82QTU2jMUWEUhwqlrPnt/mzj97Ne//0bkpgFdglxntHZhTPUQQfwFoDdRKpUma0VsUa\nG2dSigH1GGOpgrTOuEt7NTWOpULYmPu2d4xU1F+yKIItilLSioqx3J8kwZFiorH43zdCWQCt5uju\nDmod9fYuriw4eyQg113HcVci997L3qWL6RhZ/npSkIH7PFTmP1S+P/jc4Sry/v4e7Xz6zxcd2rjv\nIRws6a+HvIYDa49t6UBlYUMYGDZQzfvnPB9YA9CBe/uHp/uF/Bw/2lsq1/tF9Dqd9tZk1C/FL6Zl\nb00HquQn0/77Icz6a6OB7WTWW8IUA8MdiH+XXYmv++fjxqP++Zj+WrXUPx/j+tccBoZSvO4d9/XW\nbrz6w721H/yBF/TWLl/a6a0N/k6R6i6vQEx/0dr+P4fKcmCIxMAOVR/j72Qmk8lkMplM5mlPFske\nRwRYf+GLuPov/xWWb7gB2dmFixfg4YfgwiW4fCkKZFUVPwk2sUMlil1BOweZavdhWENX4p8EkXhA\nic8xBmyMET588htZE4+3Y5wZMZKKuY9+JScCapl7T+GEoDYKLMbiQh0FKDEYU+DciBqLmAIXKgww\nApaS96mJBL7zV36HpeUlbn35C7nlGTdw/NoTzA3szGbR0ZXUHyWZ4KTxTkma+ZlEINGFQZepe4wk\ndqHtLYn9Yl1pf1Pir9q4ueLtCsQOMwSk0RoVpqMxlzd2+eR99/OhT36eez57HwWwkl7DGfGXYgxs\nImAKGhFTMRgTxTCLxEmUJgpmRgUIiC2iqCeWidG4rY3vjmnpuH9vjnUrhHoLuyCCBQ7+MgrdMNOm\nv0wOPN59UGtimAAymxNmFTKdgEBgTHV5k/NFgV5/LadGI/SjH2G2tfUXe3NnMplMJpPJZDKZTCbz\nFc6hEcm20EuryJEvpx/piSM6np71mv+FlWuvQ7a24J574PwFOHs2TrKcV7GcXyS6yARwRXSPlEXq\nGrNJDOOKXrKUVYT41aTHJXS2C9lGj7+K33jm92GCUIymGGvwIUotRgxQ4EPAWY9RT/Ce0jiqEMAG\nTKjREJD5BmZ8DPE11hQEXzGm4ijCOuAXgoATlLC9w+k3vpP73whzohNr5aarOXXdCW64+VqOnjhC\nOR0zLyw10Rnmk0jogSCdo2TRSRaSnBYaASz1ljXyWhPNjC676LcyqSdMgP39ORcub3P6wXPcc/8j\nnD17kb1LW1jiGz8Ax9J11GkPjnhO0eFmcCi1QBUEZxygBOLrFPvWDF5BJfaUaRCMtUmwEyo1WCOc\n3djj5PoxHnrgLDvHX8bKmTcRaDrHOpGsua/JeHcggklaS+8CJLnJXLojDkFS9NRtbxN2d5HJhEqF\nsHGZ+tgxLh05wolv+zaOPnSGzXe8/WnrKBNgE730VJ9HJpPJZDKZTCaTyWS+ejg0Itk/gb/5M+j/\n57og39OEKJCtPf8FrFx9DbK5GaOV587FPxcvx+mVvk6OMJOK9yU6wDS5x8Qk8Su5w7SZaikxHqWN\newzAdB1lwScV5TJb6y/iYytrhM2akTPUAhZh5CxV7QjqEROjk1UQrDN4r5TG40M8NzEBbxzWTpgz\nx4hB/JwSy4jACDAo81QmHwUmYQTsooyIws/+fQ/z6fse5mPv+igGuOoZ13Pbc27m6LFVVtb///bu\n5keyq7zj+Pc5595bVd3T3Tb2jGcMHjuAwQQiDJHC2yYiL4ryJ0AUISGxYIHEy4YFAgkJskqkILGw\nxC5iFYmwAZJFFgQJgWEgmokUJrFQwoDtdtrdMz3d9XbPOSzOOV01rkIJUjzjUf0+0qjLV7f6nnun\nZjz90/M8Z5d22JG8Zw5M+kBIebZXniGWd2ysc8qWq85caXXxJUlyBt4clqDve24dj9l/+RYvHB7z\ngys/4+jw9tkA/AHQYTn4YvE4l4OnGp45oE09iUhrua3SWUuOAfM8slg2EEjm8MAct5ggZok+5XUG\njJdOZ7z+oT3ObzWMu8vs7Le4OC9BV15fKHfqy1pCOb5ouVw8iYiV6rJFwBXLZ9FK6Z4LEW7fJnYd\nbmub/uYtmM057AbYpYtsbe8wPTl+zUVk+TOVwufgw398rxcjIiIiIiIiG+O+Ccm+n9K3/87s2tvg\nnf3/fvpdUgKyd72bS3/4QezwMM8de/6F3GZ5eASnYyDkMMv5xde61WMNylIqM62W2jBrVVlcfl2i\nKedyFZpz+VgP/33x/VxrjWHXEpu8c+KUcilraJ0npUAfG1o/Zx4SrYdZjLQ+YLHPoZTrsGaL1qA3\nR5Ny+LVLDppSmVEGMMOYldlaD2CMKQP1qcEPTICXrv+C/7j+CybkVsZ2Z8CFR8+zvbfDo48+zGg4\nZLTV0bQt5o2Bz9tkmuVKsj5GQoxMZrkt9HQ643g84/hkzM+fP+DgxQNefOGI03Ltrvw6x2IXyR5o\nS0BW2xdrpVat2vLldQ6qciA1x9Hh6LqWOJ3RuoaUIg5HDzSlHbM1K1VwOcQCo3UOhycmOJ0GvB/w\nfL/DxcEjML5BX56HYTjSHdVknkUVWV2Pf0Vk5srXOqvMSLRQVu5yLd7Nm+Ab4nxOSInxL3/J4Rte\nz/nffzf2rz9levMmr6VqMg9ch2vfT+nb93otIiIiIiIisjnum5AM4Bn4/JdIf99g93zddRfLvaff\nxSPvez87ly7Bf5YWy/0X4fYJnJzm1srlHSjTUltl3+fB/SmBLyFZnqi/qBxLpb0yT6EvbZZWwrYa\nqiVoH+Mnr3sHYw+jFppSHjWsFVcYIRp9NMxFIkbnAvM+MfCRefSY85B6nGuJriWEFp8iXYqMCDQY\n25Z3UOzrUH3yMP9AHna/i3FKDmumGF0JErfIH7ZpOTccT7nxsxvMgB+WYx7oBw3RO6xpabzhnGMS\n8xy2SYzEeWDW9/R9fj41jKtB2C6rrYqDsjbPneFdwvBlvpor3wMWcZEj4UtF27QP2MzTWChBYkOM\nka60WUYcJPDO4UvlWcLlKjdzpGjMo/HwA3vcmrRM9h5nOL5BR61cy62isQRlrqykW5rglhtnS0tp\n+e9au9aUT+WiMTffW8JwocednpCch25ACoF513HzrU+yEyLp364yOTrit9318tUyJvXPwOf/4l4v\n5P+LXzekP645b83w73XD/NeW0q65xvo55ou/W5at2yBgzTFbN1Xf1gz4/z9/llbPi2uezdrB/WH1\nWD+br73K/snq0Pl/OFwd0s+F3dVj2+dWDrnBcOWYH61ew7drBtuv2fygaVeP+Wb1/po1/+fr19zy\nuvMA+jWfJz9YPXmwtfpZjGs2jHB+dd2Th1afV25Vv9O6PwJ//Verg/s/8N7LK8cevbj6+3Q6ma1+\nQ8CtGdLPmiH963a6GKz5vVq/2cT6z52IiIiI3H/uedj02/huSt/8rNlXPgafvNdrScDgrU/xwHve\ny9bly7nN8oV9ODiAm7dgPIZ5n8MsUv5Hea0cq2o1WQj5p5oYc6DmS+UYpeUyUQKxuCgzMrd4HSaw\n80dc2XsEYvn3v8HAwcm8FKAZtA0QjJA8Ljn66Ggb6EOkbSIxGH1y4AY4P8CHvMGATwEPnDPwKdE6\nRyQHZa0Zk5TOKrcm5CH4tzAacqXZoNQ11V0cIzm0qgHRrLyvB9y0ZwIkZmfHarhWn9yIxbB7T/7R\nZsDiR5xXVmHVtkXIVV/zEia5sybO/L2buoby2pW6Mg9EM7xviMnhXa4Ma3xDTLneLOJonMsVZAat\n82XeWouZ0XrH8SSwt32O544Sv+sf4xyLOWh1ve3Sa38WjNXqtkUFXx36X59JU+7cLR2rYaHhsJPb\nuOEIu32LONymP7rJ4emU7onHOW8Qfvwjwnj8mgjKnoGvfDelb97rdYiIiIiIiMhmua9CMoAvp/Sp\nL5ilj8InRlhTB67fPflqD/zlRxg99RQcH8P163kO2c//C05L9VjfL3aqNMvJVezBNZBCCcP8osIs\nRkopUg7E6vvr3dVJ9bUlE/JArhShP+DK2z/DP51rGU1yN6c3mKa8u2QtUug8uFAuacZ47iF6sFQq\nqQJDF4mDHZp2i5ACXZjQzk64AOyY45wZjXPMY8QczFJkZB4STEmMyjB7FyPe4CTlXTWnwLmU55Yl\nchjVk8OynlxpNieHOrXabL64+7Pz61D9GpKlcmx5l8gqV6cth2J57lkOw8rg/aX3zakD8ctMMnP4\nZkjfDPG0tK5l0G0RrM1zyvBlfUZjPg/6N8cMz8wMw+ObIQFPsobDaAzHQ257x78/9Adc+p9n6abP\nAY4G6Ei0pQLMLVWP1TCvhop1Zlltwax7ZLZLc+JquNaQg7eIw0+mMJniRlOYjBlfu8avLjzM0eNP\ncPHJJ3no6lXmP3qWu916WWfBjUn91+Bvv5zSp+/qAkRERERERES4D0MygC+k9Ok/N/veJ0mfexv8\nXoOtaUx5NeSrNI9dZvimN+WWyhf3Yf8lOHg5V4/FWGaFlQH8zpf5YfO8m2Ud3n9WDdbnAC30eUB/\n7Z0xSluULa5tjjsiwRhL2dMbePbBt5AMulIx1peyqoGHecg53DhAtzQCbbvLl+iCcdpD03j66Oia\nIWaeZC19mGI4hsBW0zAMec/JgXfMYsKZI6XEzOws6ArAlkGfjD1njGNkgNGTGJWbm5DOKs9qqFOb\neepuk3WIfm2RrN+b8rWGY7WVsoZotSIsvydfs36fGop15TnGpV/d0usRcDsFaHfBdSQanGugGQEd\nEVeCMsPMAx7nGgKexjzJtWCOaG3ZWdThreHW3Bi1DeNui3D+zXDjOQZljtgISoskNEvhV65qy6/d\nWQXZWUkhkMPAWN7TsgjJaktsfbYesPEJyRxMJ4SDQ8Y49p94nPbt72DryhVSDNytoKwM6e+vwtW/\ngS9+K6Vv3JULi4iIiIiIiLzCfRmSAZQfpr/xQbM/+yrp63vYg3fjuv7CI2y/7wPYdAq/eh5e2oeX\nD3NF2Wx5JorlWT1mOShLMX8NaTF7KJTmvtLWmIfzWz7u/KISDRaD+mv1WSohSTyF3T/lX3b3iKWs\nbhZh6OGkz6cN2xyaDZocmLUuz4Pp6yV9nl02D0bjjLEb0nmPc54mzhkxZZt8OwPviTHSJ9jyntPQ\n47zHQsDMchtgyjtOzsgBzY7zTFJkgOckRaYkdjFOSAxZ7NhYq8eGQNnugB7uqI6qlWZ1btesnD9n\n0VJZh+HPyG2YtdrQsLyDJotQzbEI1iKLqrYe2MFy22k7IiXPNBhNs0WILc45Ag2dcwSM6EYk8zTO\nE/G5s9YMsw7MkeqD9gAtB8lzuP0WHuQfacpA/tpmOaBGVOnsD2iteqt30pan5rCzDQeWg638PPI5\nOTTL9w65qqyZTUnHx8SQCESmbcv+G3+HJ59+mtmVH59d59V2RDr8OHzon1P6zp+86lcTERERERER\n+c0srRvELCIiIiIiIiIiskF+0/5nIiIiIiIiIiIiG0MhmYiIiIiIiIiIbDyFZCIiIiIiIiIisvEU\nkomIiIiIiIiIyMZTSCYiIiIiIiIiIhtPIZmIiIiIiIiIiGw8hWQiIiIiIiIiIrLxFJKJiIiIiIiI\niMjGU0gmIiIiIiIiIiIbTyGZiIiIiIiIiIhsPIVkIiIiIiIiIiKy8RSSiYiIiIiIiIjIxlNIJiIi\nIiIiIiIiG08hmYiIiIiIiIiIbDyFZCIiIiIiIiIisvEUkomIiIiIiIiIyMZTSCYiIiIiIiIiIhtP\nIZmIiIiIiIiIiGw8hWQiIiIiIiIiIrLxFJKJiIiIiIiIiMjGU0gmIiIiIiIiIiIbTyGZiIiIiIiI\niIhsPIVkIiIiIiIiIiKy8RSSiYiIiIiIiIjIxlNIJiIiIiIiIiIiG08hmYiIiIiIiIiIbDyFZCIi\nIiIiIiIisvEUkomIiIiIiIiIyMZTSCYiIiIiIiIiIhvv114w/WH4m2tGAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# creates sub plots of 15x15\n",
"f, (plt1, plt2, plt3, plt4) = plt.subplots(1, 4, figsize=(15, 15))\n",
"\n",
"# cutting the image\n",
"eye_brow = img[100:150, 150:200,]\n",
"eye_split = eye_brow[40:50, 20:30]\n",
"zoomed_eye_split = eye_split[4:8, 0:4]\n",
"\n",
"plt1.axis('off'); plt1.imshow(img, interpolation='nearest');\n",
"plt2.axis('off'); plt2.imshow(eye_brow, interpolation='nearest');\n",
"plt3.axis('off'); plt3.imshow(eye_split, interpolation='nearest');\n",
"plt4.axis('off'); plt4.imshow(zoomed_eye_split, interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RGB (0,59,120)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R intensity at x=3, y=3 is 0\n",
"G intensity at x=3, y=3 is 59\n",
"B intensity at x=3, y=3 is 120\n"
]
}
],
"source": [
"latest_pixel = zoomed_eye_split[3, 3]\n",
"print(\"R intensity at x=3, y=3 is\",round(256 * latest_pixel[0]))\n",
"print(\"G intensity at x=3, y=3 is\",round(256 * latest_pixel[1]))\n",
"print(\"B intensity at x=3, y=3 is\",round(256 * latest_pixel[2]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: image_transform_frequency_domain.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Image in spatial and frequency domain"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# loading image\n",
"img = mpimg.imread('i/solid_background_1.png')\n",
"\n",
"# getting the dft through fft\n",
"fft = np.fft.fft2(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# White image"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kv4uAZ0h3/vvUbMyJiJU+H2AkBu1tQtpUFpTKF1D7sYnzAS666CJmzJjB9OnTRyCkoevs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\nwjczM6sAJ3wzM7MK+P+B9afP2Vn0OgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"\n",
"plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(img);\n",
"plt2.axis('off');plt2.set_title('Frequency domain');plt2.imshow(np.abs(np.fft.fftshift(fft)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# White background + ball"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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F5FIJ/j8DX1HVXwl/C7aa0dtV9Q978q7F1p0+gEWlOo6zdCrYgi2fmme5YMdxVjEXfQw/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\nIooiCoUCAwMDbNu2jdtvv51Wq8XRo0c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fBIfDVVnkZdi7oXHIJBfGWdypHSKCxTxXzVKy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\nVXuoPc57ZFmB1kgnpJhFP7qKkGq6A09zpaLZqxjseAZnKgb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DPmZmP9a//20ReQ+5E/Ch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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mpimg.imread('i/solid_background_ball_1.png')\n",
"fft = np.fft.fft2(img)\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"\n",
"plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(img);\n",
"plt2.axis('off');plt2.set_title('Frequency domain');plt2.imshow(np.abs(np.fft.fftshift(fft)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Animation background + ball"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Qdpd5VJNONfuhxvZyHGOBW+2RlwGlmzN0bJ7ljBiNA0bjdUbjjLJcJ5INFjgY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iAAnSKb5LhFTnaUw0OQw0CcBCQT+tzjcyD3eN3iQ4tvWoIXgoFQQXABH1t8bOh1RzcNdFlMe737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\nLaXUl4H/Fvh1IFRK9Wa8/BVqG/VM/ON//F8xOi75w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549Q+VBoYDVns/F+ZAL8wFx8DzO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BNqachpRTdyIYAuQoYRKM1ogW6QSrEDyNjXvGxlAXKSdxMBKLYTmRryvyqGSOZ3RfJ9RCoj4k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\neaYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mpimg.imread('i/smw_background_ball_1.png')\n",
"fft = np.fft.fft2(img)\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"\n",
"plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(img);\n",
"plt2.axis('off');plt2.set_title('Frequency domain');plt2.imshow(np.abs(np.fft.fftshift(fft)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Super mario head"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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i9AMnGpScxtXz4idlRw3ykj6ZfujAxcuUizt7BAzSia1b89dMeFgtl9eazoh1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Kpr+kHXlt9sGD9x4bw3LpdkrLjfGrSOO+1TM13t3sMifV7K1h27nh7smW8rlW9bQ4PVKhI4tJnL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J944yfx1euuGZiJbQ/7eobLdmyI1P+uQ6mtXB3N7Tv8o+uv2lB55rVf/rIqTcMzYo/f47pt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\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oIhnlEIaJeoYRJkZxTHQ0FnWNAcbHWCoSYIITFoRDWQwVY4x3womSfCkbDpjYsM9FuZq7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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mpimg.imread('i/super_mario_head.png')\n",
"fft = np.fft.fft2(img)\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2)\n",
"\n",
"plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(img);\n",
"plt2.axis('off');plt2.set_title('Frequency domain');plt2.imshow(np.abs(np.fft.fftshift(fft)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiply in frequency domain == convolution in spatial domain"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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G+UaO4/rcqigDL3fDwG8v/6H1RTjrnAR+61UG+G3v5x8BvG77XX83GIwrzTUvh76Fb/SN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\nveDfg7qVWCz+xgIn8F9j2mDfXPvIrp/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zaQQaBoP5TLQZAKRNs68KaaMsTZo0NzPfaKNM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BJXMSkC4IPFFwQhVDwWhUIB1Go1MBgMAMOwXNL/ZDL5VxZ1IeG/OHG/IJblz0FYM7Gnmfif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\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lcGH6WALjNUGX600SMWSzF+PvTid63QyVMJMJhVegYtnTztm9oJU1JX18D4/5lcAJ/Ryp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E/SWDXiRccdZiLZG7ZML8I2WZsihXB2QDfwkbHvL//P6EdJo1EYRZSB8ZCFmZmK/qNKKPub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\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yuTflmpkSYLK5FwAATdNAkiR4PB5gGAaUSiXI5fK0w0oikYBEIgGxWAwikQh6enrAbren\nHeUfVaic77EUH0txo9T70TQNXq8XgsHgdbzuRu2WNzrH1HlKJBIwGo1QXFwMhYWFoFargcfjpUUp\niqIgEAiA1+sFDPtgWnuqIDjfsW/02I2EsRt9D/O9PvOxTAd/arATwzAQiUTScRwfF0Sj0fSgiAUs\nYAELWMAtlFF2+fLlW2Oh/0OkHGXl5eVAEMTNXk66xTLT7h+LxWBubg56enqAx+NBTU0NiESi68gH\nh8MBhmHgzTffhAMHDkAoFEofI3VcgD8NMgD4cAtAplgGAGmRzGAwQF1dHbhcLujp6YFIJJIW0jJd\nZ6ljfNTfzEwvHo8Hubm5sG3bNrBYLOnnU4Ily7Lg8/mgo6MDaJqGyspKyMnJuS4vZD4RLiXQpZBd\nqZzPzp+5z432T70mc6BASuhTKpXgdDrBbrf/n3An/jWBIAisWLHillf+hBXmx5LDwQssxkEwRUEl\nFRzqwUi+mMYCPlygzGNjpBcRK5WgyM+hx062shyCYPlcHEvgOEOFvXzt6vVJ79U2JhGcw0RKFSJg\ntGhUjDECMoILCkyku78DIUlGbK6r8Ac6gzDn6eTnFCwBBFVSbtc4IxEIxA1f3JA89txxBoEEqC3l\ncXdPH/hcQ/iKe+8hcleVxZt/d0Kw8tG1XDKZdB76wW/5ijw9sXrLjqTb5U2O9Q/qHme/jXoE9pk/\n0r9XbH5kR9LncCSbn+1kVUUyzbe+uDn69NNP83ZanqjpRF62ii011CVbN9FUWkqxgwL18OZF3sr+\ngT1jczUn53q/K5QUPmxJDlQcvtb3ZEJSHpFWGfUat8sVVJlMDc7yR7bclRR++tcvf3ljQ3k56HK/\nMS51T0weuPLM7m9u/4z9kPeQfMU999RJQ6HOycnJcf5hId8UNcLJpmWrJ4nmHi3jYwXNTO0bDxHm\nxwnzz86++DOlUqk00k1Ng+IrV1YJJ5tYnXWyvb2yXY+W6TtGOzrsmoqKzeZerL7+s/XRUZ+yM6F0\nD0koee+RF3/2LDf+bM69d0WOm2t8l+yvCyu9P8xpPfm5zy2vqqpyq1RVycPWY1KOVBrUB4PK++67\nb2XEE0EoAfLfsWiVbTJx7p6mA9v9hyp+7vP5fBGz2SwNh8PNR9448uLeqr1tX9py4Zj42DHOirq6\naoZhnn/++eclEomkurq6Wr0jd4d9/9h+q9VqLd2l3ZVoI9pIkiSL1ug+8f6J8Wcpl8u12bR58xRa\n/pCYl+uauscVezBJ+d44efKkVGzYqWWNXJv7wos595V8n9OafO3w0TvuWHJ7d7d+RvkpA3Wka9Jk\nOP723tee37ZNsG1my+cfvE2qpN3907mePsfT/M2lmztfPDdDrb5z6U5vW7OqFFPtfWbkHNfIcYvD\nm/eMEM+/QjsQh7Lg17/2XW3uUeIzfZaCcLxb0MFy5qSDJZaikmZCv4xJjjq4yJqteuM+ycgbpp8Z\nNm9ZrzHPzl519BbSt2st4uAhvv37kl+jDILJGorvTNI6j/fwkXdNP8I/HZ2Nen0vaQ4KqurqMJbA\nyHNHTkRRb0i59bu7o94xGzXYauNuvqc2cuWyg+k7/CaiyqkkpHlKcqbvGi4QyrlNX7orfO6Z8xDz\njuNaXQkvKuf6bc1vcXPLNws5edzQzIUuVKRRoXKVgg15EYr02Tg8dRETiUyTUZsbJVRCRCQR0m6H\nGwRiOSCsBBKhKQCKwnNWLYG50RmGDLhRnsDEhLweFnAPEDIzwuEnwB/ishAYpRLB4Zv92/OX4rbb\nbmPvvfdeqKqqutlL+ashlUuqUqlAKpXeNGdZZnEwmUymWy9jsRg4nU4YHR2FcDgMBoMB8vLyQCKR\nXOd0ikajMD09DVarFbq6umB8fBy8Xm9a1JrPYTbf+6daIsViMWg0GsjNzQWNRgPJZBLsdjvY7Xbw\ner0Qi8WuGw7wUcJZZlGPz+eDUqkEs9kMJSUlYDabQS6Xp4uPLPvBUCmfzwc2mw28Xi8IhULIy8tL\ni2nzFT9T7zNfZ0H2Om7k2Af4aN6V6SZLOftSMRfBYBDi8fjHqjhptVph//79cPz48Y/FSS1klC1g\nAf9Y+FtklN0yrZd2u/3JGzlsbsUNRdG0a+lmu8oArg+Yz4TP54PBwUGw2+0glUpBp9OlA1RT7ise\njwdVVVWQl5cH4XAY/H5/uhUz2/mVOakpm6ykwnblcjmUlZVBfX095ObmgsFgALFYnBbgUsfNblnM\nPGa2qMTj8YDP54NKpYIVK1bA1q1bITc3N10lxDAMWPaDYQVerxf6+/thZGQEMAwDhUKRJqnZzruP\n+n6zJytlnmc2WZtPGLsRYUu1XIrFYiBJEqanp//s1MxbdcvJybnlWwCe+OETMpZEQwgHxSHidqM0\nCwguVjAoRdFU0gsiiQanIhTtHh8BmoniQqOWSUZ8LJ2Io4SQR1FeL8TDIVyUm8+iGI8OeoYZIOMc\nZY6a8Y7bWSoWwaQGfTQ648fDbBDTFuSDUKeMz/S1A1+EixRLigKXXzrEoCyDWpaYk0DykGjAjRsL\nyglTnTl6+hd7ccLIoWgKC/e9e43HVYp4jYu3IbEilJweHBYXrS6ig3Vzsy+8/TzGC8WwMsVixi/G\nKdY6Zbr7we3I92iuX5EIi9WYcIpn0JOtx+fiBC+uaSg3S3x2YKW20BatOnCt60TOIEfmxPdsKZ7E\nCoexeEGFRDU5u3HjtrW0ymzWXG6bNW3NjfzXhWsnBKbaVe7cxfkS0wjLMJyIUrsSPc1aWbbZ1ayC\nYHBknX9db7OtefK90Axa8Xgpf+DS0+8z01cnjGKI9k29zy7R3HuljTvkiLe0QG5ubiCRFENgom9b\nX+sdJ8nag+PjvnEjYjQuvktXf7t/WSEu3bo9AuNXbbbJPr+3v9vtWFpW1KBQkJpFmtGBXkNH+3uX\nk33oaxK31ZpbedcnZE2FBWLvrNyJJ0dH7FZrIpFIaMLhcMv07HTw9Kkl9u6u735vq31j85uKXyuV\nSWWuFJYTZ3uxgErmUEnUkgOF4ZUqDO0Ilq18YCP3mDEYd0zMqapUGoFAEMzJyVlkU8RHfL0NXEtp\nqBRl0VWi1avoRkM52Ra7pFaIRI0lzlfY3N0kXmrv0LoujhqZ+EwnjuNf71p9B6lWeARcHXdW5eY2\n1p3LPfCHmT+YVyVkD25aVu4qZvvfXYvw+GeGj2u1Wm2/U1JQYeCGBt7qeNM37X6/qEhcdOb82HlB\nuYunCwl6h8vk6+Ndecun0QZ+pYZcU1Ua4Q90ytzLHlv+oDzWeniMdUw41PI8bLEYS3bPtM4GxKVy\nKesvwJNzYTFtEAoFfH9J44Sn/exINNxDxxSrNvISBbTndz1HJX+c+THxOXR72MbzUqoaBEGKRExf\ne7f3fe/7YvVPqjAgyITTNo5rSjRsSaiQHve0xNyjSQ7PqADn2CTT2z5N1N1WxEaSUiYRmqVV+RaO\nUCuKz13ro0Y6JnmqEg0VDiaZcNiHGhbnYajcQAdm+hOQ4GECmZgKTo6igIkxcbGFCcy66UTUBzwu\nhnFlPCrq8gAuUuK4VMHEXBOAiwkcE3HZWMCDRNx+FMdEDF8oYYJuO8ITiwCBIJKIBoFM0MDhCoBO\nRp74wXfmbvZvz1+KV1555cny8nLQarU3eyl/NaTEqVRB7ma6ylJ/s7kMwzDpbLKUqyoz/iGzUKbV\nakGhUKTd96ncrFSRMtuFn3q/VCukSqWC3NxcKCwshJKSEsjLywOdTgcKhQKkUilIJBIgCCLt/pqP\n/2RP9cZxHAiCALVaDQUFBVBZWQnV1dVQUFAACoUCuFwusCwLiUQCAoEAuN1u8Hg8EI1G0xMxJRIJ\n8Hi8D4X1Z77nje7/OS6Ufazs46denxm9kYrmIEkSIpFIuuPg4wSn0wl9fX3w0EMP3fK8C2Ch9XIB\nC/hHw9+i9fKWEcpmZmaevNlr+GsDQT5oAwAAEIvFN70FM1OQSZGEVBaZTCZLjyRHkA+cZJkTh1AU\nBZPJBBUVFaBUKtOZWanzSx07tW92pU4mk4HJZILy8nKoq6uDmpoaUKvV6amORqMRTCYTKJVKIAgi\n3R7JMMx1x8rO+kgdX6/XQ3V1NTQ1NcHy5ctBoVCkhT6WZSEajYLL5YKpqSmYmpoCmqahpKQEiouL\nQalUfihPLptsfVRI//8mN+Ojvo/MDDmRSAQoisLk5OTHOsD/4yCU/fhfnlIjgOKAYnxAcR6C87jA\n4TAsm0AQnMdDgcHpcMDD8gg+oCgHY3h8wNgkEHI5wuMqGNdYKy6UmlCOWMAEp4YQnojDVZrMVNgW\nQLmEApVq5SgiwmnX0FVCW1nNIBRCeUccGEcIHEN5HgsIl0nEUV7phiUYR4xTE+fbOKocAy4zGsn2\no9eAkAv5pU2LaSTqR0MMK6pcVcbqh6ThC61ncULIxzEJN+6YS6CJ6YTsU4X3gvwawYxwOqUby9ez\nfzjyMztycRgRCJKETOqj9OU55MwiQcUqAc3VR3RzsSV8A+Pp68ufKJw8IW2m4tOjSg+dMGt1Ot5M\n/7l7GjatCq9+ddP4aPmgUkB5R1SVlfGwbSwxdJ63lF+8Un9F3lWuEpdwi4SYXYAK1J6monxUIplp\n1yhzWKeVtmi5y3g4/uCKZXe1dXa+b9Rqtd6J8QnJbml14ZVZe4muttBULpOhY57pHYIdX/lx0XSv\nkTXaW1tbW3UVOt3ivKIVkyYskN/DBCiGolzYJReXa+AibHd3XZ4yjw2HuvLD7TIRafZ2DA2eDCQC\nge7OIwdU8WC80O/A52IwWbeous75jsdpw2y2VXrOIo9Ifamzs7Nz0zcMP2g/mnjH4Yg4WAgG62h0\n5cqa4D0FFoFU8s/M5JLKbWUS66nhHonJf8GEmBeJF3FD6pwcrcGlbTqvXqeqxvu0+RbtmaMdR4k8\nOSHkyDkhXN34CJ/TlPQ1nptEO9s0cp78qsilGa+frMw/jHVac4esElfZYj+/+Ry3vMCQ/C/p1WLi\n4d0FmyV1x/e/8XR4aGjIEpAEyorzi106cgcnzp7FZLgsaQvbworSz08HxoKVYojWKPOVirC7AD0U\nOuRRFQhHJ//f1cBacV5naJWxSNB5OsiVryp9YOYx+1xht97r42KF6zayU6L6OAn0dGJkSmuUyngh\n3Oa8dPY1gfVaZ+7G8mp7sJRlyxYZESpBh3s6OwNYSCSb1Exy1+95gLR19HNWS0qJPIEu2mY9x6Ai\nIQcXimjvqC86e3VCtFq4SpTYaUpGnTZMU6DFFHJO3Nk+So60Wbm5S5agJMtBfbM+jnmpBZUaTEzI\n7kTkCgVXZM5hQ04nG/cnMIFciGM4j/Y7pjGFUYsJxLkMhGNMwh3kisxGJhYOo4l4GBFJpAgdQ+mo\n1w4yuQZHJVKUBZYhfTYGw0UsigAT87sxQigHiowAinIwXCRkEQRnUZYEYBAAhnri+9923uzfnr8U\nr7766sdOKAP4k4s9MwfrZoll2WJTZvaoVCpN8yyGYdIRFKmCIYZhIBaLQa1Wg1arBY1GA0qlMi1w\npaZDCoVCEIvFIJfLQaVSgU6nA5PJBAUFBVBcXAylpaVQUFAAOTk5IJPJgM/nA0EQIJPJQKVSgVqt\nBo1GA2q1On18sVgMQqEwvYnFYpBKpaBWq8FoNEJhYSFUVlZCZWUllJSUpIusFEVBOBwGr9cLHo8H\n/H4/RKNRAPiAB+t0OlCr1SAUCoHD4dywmJjJu/43ohnAR3OyTN6V+X2kpppntlx+3HjXglC2gAUs\n4FbGP3RG2ccVCIKA0+kEPp8PGo3mpl54U+JYiiBQFJUWZQwGA0QiEfD7/eD3+8HtdgOGYSCRSEAm\nk6UneBoMBtixYwesWrUKhoeHYWBgAKanp8HtdkM4HE7naHE4HCAIAqRSKSgUijQZE4lEwOPxPpSp\ngaIoSCQSMJvNEI/HIRAIgMPhAJfLBR6PBwKBAMRisXS+QmrdarU63bqg0+lAKBQCwAcV23g8Dj6f\nDxwOR9oFh6IoKBQKkMvl6fHjqfyyTIccgsxv+c8Wu7KHCWR/vx8lkmUeM9Mhl7L/OxyO9ASvBfwf\nBsJBgQ77gUH5rFSrY0kKh+DcFKIWayGJcZiAexAlpHKUJ1VSwekxiolFEKFMgZKMmAnOjWICiZnV\nFpkoz+Q0KyCECKE0U5FQkvKEAoyIcqOYoDg5d22Q5fO0ZE6hhRw81QUojmCqwnI65PPH7d2tuEip\nJhMeihy90oXwcJxn2VCNTHaOMVwxQlRtXsTEolHy6uELGE8qjsRkWvYMNoIhQMkbDWvIaWmMvjo6\nIXukcCNtU834zna8wxeFcqOn9aPi6sc+h1hfbtbkWGTCqNcbGrDZytCuh8N2xU/GO07ZGEeBk1y5\ne4fqFfc5RiTRVJNGKCUrc7uWluUR3acOTTj5/Ld+NP19gj5rCCqT8Qrr1dgMB2BVo59no7gtbtnE\n7ErJXGuQLjcEn+9tRZQly68G22yVCXCOiPz6JvhkxdDA0aMDO9jCtWuL1ra4we3xBD2GCytlB5ln\n9hth1lhDqWsUS/SKnms9R/hKOaUiVKr77rvvPqNxyjg+NRYQN95fdqzFGWe9LUdLSqIlYvGQ2Oaq\nddn7bH3V1dFq3vCXZhPohO+2XO1tuqYc3bvvvvuu2ew1v3eefk8qBanrdMD1mUfXfOaH5/f+UFZ6\nnyoyORmpra2tPfIfTe0IchL5TP7tnznF9p46n+s86M3daGo8eNl9rnDuzWjv3OfDRJ49MjE9zdE3\nNgSjpDEivuxQteGVCaaCc9Xed1UcOiWuqcmp6evr69Pw+fy8h5WGltfaDlnEhYu0jT6zazDuN5Tc\nXi7pmnxv+oHG+5usr1+bjb3ZNjgTHNxeayzN+2b9uhd/se9FtFO+TFvV1JRHRKO64njxme5Y6IGL\nOtExDaaquDgijsnnVp62L3HGCI+7jTbm1tdXVs9gzd22GpWUyz3X07CcyhscKJ41ha+854lDzHZh\n9PT4VUkkp6yxcfnOl8b7+QP6cmoc/WP08peBu/mhIfvq9XFF24F4XFoZVKPOau8S06K49erw1Ht+\nKTszEF1kJrzNncORvHtvozrc7dgEz5eceP2IavWetYYv3PlP7rcn9uMriSZxXk518tCp14NH8IN8\nkb0K9/iRhH/fSUntni14zefU4bY/HMVJJsiaijXxa4eaE9Y37LyihjqURInk6FUrpi8pxHSL8hLO\n9j6EjUU5uYuLaP+ojXZc68PVFcvBn4iSpGeIUaDFrESjpYMOBmOSXDanqgSd7OphfY4phkeIEZbL\no6O+WeARWlygz2MYzMl4Xf2sRJyL4eI8ymM7iUg1xSzLkUPINoQiQsXN/tlZwI2R6WZKFcwyRZm/\nN7KFndR1XiqVQiKRSIfGkySZvp/pDONyuaDX60Emk0FeXh54vV7wer1pXpRMJtPCTsphLxAIQCAQ\npN1iKbEwe4KkTCZLD3OIxWIQDochHA6nXVXJZDJdQMUwDAQCQXrogFgsTmfbpvZPTfnMXFNqEihB\nEGnn1o1aYrO51nz86c991tn7zefuy+xISLWJxmKxdMF4AQtYwAIW8PHHLZNR1tLScmss9P8HWJYF\nHo8HFosF5HL5zV7Oh6Ykpf6mQmaTySREo1Hw+/0QCAQgGAwCjuOgUChArVaDWCxOO7WSySREIpE0\nuUokEteFx6Yqd5lB//P9T2YSyRSBysz3SCQSkEgk0oQtVQUUCARpAkjTdFocm5ubA6fTCTRNg0Ag\nSFddUyQttaWCZLOJbArzkbbU30zSmd3W+lFkL7u6mTl1k8/ng0gkAr/fDx6PZ96pnh8nLF++/JY/\nOUQsXY6QbBQY0gsol4+gfCUH42NJ0uMFmmFRwFGWg7EsS6EIDQzCF6sQjIvSEY8L44vFCFcoIOMe\nH0rSJCCQRERCExqiKVosEHM0RWrK3tuL8ZRiDOUSyeSchw17nahAKsOEyhI64Jim0SQlqn3gNsZu\nn2H8k5Pyup1NgaHLVyjngAfnEnxGwpew4VmECviGxff89HM4XyIJHnn299oH7/oK7TjS730/2iWS\nFAop0TiZHLcxTCBmQ5q+usXwqO6hue8f+pZu1apahe/KlTGXVMrL1Wg4jmnCHQww2Bw5hyStb5ev\nXr06oMrLY86eOLFj944dp7waDVo/p6QuX+2YbhuPqJZ7tzkOiZ94dF3NpngjfbuvWfXatar6fyKf\n+dc7jdXV1Uo2xHYhyxpKp8+e6HKQ+ju+s3mV3+psaztx6sS2bdu22S6fsSUfX3c/d699ryinoEDC\nhGLvNw+qS5bEplR4Ne73+/2PP/6Vx3/Y3NE88Mwzz5iKFMdL7lrTzv/D1SvOr61ZGdtXAaGh3/8+\nIAoEGjav+FxncPGa8ZMtT93e1I07ZsAhcv/uCSh46qnGa8sbfyd4+XcWi8XSR167e/0inyqnuMFB\nXGkMOMqC8iARS7Dnbd2JMS93/7grd0kxcYDUb96M248eFQ4JhYIljEAsUX4uUFp8KXrp0iWVSqUK\nBAKBvr6+vqji81+p/ZpKJT781OHZATn39npe9R8Pzr4QWHXHHezFl182iVQmRsZntCVLHvKwB1rg\n5BZLUN3+jlqtVmNeDBvwDAzoMJ3uwW/c//Bb+9/ZLxKJRF1NTU2PIbaCIwev/jIAXqDtcfv69Xse\nmNvprb822JLUvRVlvvT1lfi1E/bz/QIXJRaB+OWfTR+qKPnqfzAmW8d6MTLRFs7Nnbz89NP1jWsa\nbWNTY4sWLVpkt8/Yj5/rCGs+8fidyIW9riVLNHMh41KjYPbk5m/eS8QfffQ7WtWeiUsx/fvBodff\nO8tMSec4ivyKAsXiRTHeUSkTum8mr3xqxOrV6xMTfSHeY9oG7+/t79Ldx1sxmbwkJ/9Td0bc3u4w\nedWj3W7ZFonVuhwv/ehXikd+/nhyYno6evoX72EaTQ6PU2lGAtPjSSZJCfI3lXGlAoGn/cW3eZaG\n1fD/sffe0XHU5/r4Z2a2997Ve7O6ZFmWLfeCjQudYFoglEA69nIyNAAAIABJREFUSUjlJoT8bgoJ\nSeDSAgEMGLAB994l27J671pJq9X23qbP94+c9V02ssn9kcQ21nPOnJmdnfKZ2T0zz3ne931eMU8Z\n7fz4OMRCULYgJZMOxVAKt03BYnUKm2uQEqjTBzMwH0rJT8OmznUCPGpHeIo8EInaaIDHaA7C4nKM\n6RTmstJ4KIBI9JkU6nUBDPUDFlcPATZDE/5RBobYEMLTsVliDklFoxAFcQFDs2mYDNJocOBqP3u+\nKNasWcPceuutXyqPskQgCAJEIhFQqVRAIpFcVbEMgLkbH8V5Tpx7oSgKUBQFBEEAgiAu+YYlZqUB\n8PdGDHERK9nyIrHUc64u5HHM1WEyXtoZP3/cWy0+1kTuQ5LkZ7xvE7dJNMePc63ETuP/TBZ+fDlx\nnriczLk+j3clVlMkimQIggAMwy5x2C8renp6wM6dO+c9yuYxj3lcl7ihPcpmZmaeudpj+HeCJEkQ\njUaBSCS66mTtcuUA8S6L8TbliWn5XC4XuN1uMDAwAIaGhoDT6QQEQXzGT0MikVwqJxCLxZcimXHf\njUT/jfiU2NkxkVDFxxQXxBJT/4VC4aXjUhQFAoEAMJvNoK+vD/T19QG73Q4gCAIajQaYTCag0WiA\nTCYDIpEI8Pn8S+eKE85kYjmXoHUl77HL+XpciQAmimQIgly6dpFIBFAUBU6n80vnjzEXvhSll8/9\nt4klTNUjFAPReNDGUJEQQ0cJGOIiNCBRmCdScxGphMKDs3xlYTUdC3romN8JGCIIIzgfRGkaMBgF\nC1UCdmrdCirosoGc2kqBsc6E9+4/AyNcLgA4QYAQw4TsTpgn5vFylmyAw6SLg8gV4tWPrgZjXe2E\nfaBbmlaZ5x3a2UQ7e0IUHnLDGcYqccY31pH+sE/+/fzHVaKFAvsr9/484wHpd8KdzAn3wdMHWHQ4\ngELDAlXe29/jpmVyOKu8hfUIPGLdfuHdkp/e+ZLsdZJpNx/aTblGbNHRztEUwdc2egZe/zkjUCxd\nl/nVJxW3L8oJ3lN5R+S93W/2Y+np9tadp7FOLytb6npY2Fcz4hDndUb6LzaHaSTctX/s3aWsurq0\nXW/V90tSOwTZ+au0vuBtm+ndFX3/dcdayh++ZTS1qse2/d3tMAzDAPQCn6FqYYa+DjEtzVwVuNh/\nNtRJk3w9PtITfmRrmO90bhCUltZ8+m7t9sk9URkvpd864XrZ3WFru/sb6XeXcFGhIkvrLcjxZjl8\npxxML+JMc1dyWWPDZT9//sz6SI82ct93PIsnjvh6AzORwN05+rtP+bzDpVvXpF3c4Xhu/46uHQ0L\nH374zfNr644Sj457NQGO5ZjrfQ4v2Kx69Ik/9jbWNjaWgsZMg8r79ns73l75xK/vAK3HIR4jdp3v\nOH/+jq9WfLVU1F8glYp64d3DAwilR0KjYUtIrw2qinNyBvZv377t7rvvRi9AYFogkzT4InCU7bQe\nbTv56s0333wzoybVpQXUwql+fPAnmHyfBTGcG/EFotlaiaZwILrBj2M7GdcY8Qij/SrBFMY87Lem\nispLytt+0PZITlFNfYoiM/twegkVtoqs4mnLFFufz1E/nL4u+4JouCnSZudoG+tjQzPo7HRPE8It\nLVUYBesHuts/iRoFkDSyeqncgU8MIqQf7fQqZpuJttPOe0W/+gHlv1CPrV89qAR0s++wH5VlCsbq\nn/OyT/4SH0It4dm+SZdZJK2xzgwj35bdIfoT8XHWZlOalxMUSavuX+DHxtujMx/7OB407LzYdTDN\n9PBmxeq9S51vffR86n0zD5EZOVLaorGyiou1eGRAQrhGewh3qwWKCVL0abfVuXv/9qlCvqqCU7Ks\niLT2jyMitQCkaXOAw+omqUCYgRgxBwi5JOaxko7BTlH1Y7eQLFLIEDE/O6UknwzaHDCFkTTudcAA\nFwCCClN40IkgQjFHqjVAsVgQ4fDZNIulYoiojYOI1CThDTIEEYNohob5AgQS6Q0/e+rxsav97Pmi\n+DJ6lCUisdN2nOPM5X36n8JcJYLxzKY4FxIIBJcysHg8HmCxWP8QNERR9JJIlnjcRFuKz+Mec2W7\nxwW35GYBcU81HMcvZV2FQiEQjUYBiqKAoqhLxv5xrhbngHHOFS+B/WfKIj9vXfx65rqnl/s+OZMs\nzkVZLNalgC+O4/+Bf8HVw3zp5TzmMY/rGf+O0svrJqPswoUL18dAvwAYhgFSqRSkp6cDPp9/XWQK\nJWebxTO23G43cLlcIBgMAoZhPpNWnxw1jB8HAHAp2yzx2ufKmor7k8VJbjzaimHYJZIWDAYveUnw\neLxLAl28dDFOygD4R8+KxDEkE9fE7efaLtk8N/E6GIaZs8tS4nIicUskbEKhEJAkCWw22w2T+v9l\n6HrJESgXkBRKAhgwLD5Xz2dMnHBobJjNV+gogUBA+21WQFMBRMJPocJ4FAIkoGGEgQVqPSRKM5CW\n5sNIbt064PHYIJ8XZ3hMmGEiHgYwaoiiXIhMk8koS/MY2+AAwhAot+rBtfhEcy8IjE3wDUX5UU+v\nh3L7rYiIz2IrskoAhgMmQtt41fduQnJfU2JH1B8J8yqySAiwgsfe2Y/IxSIWrNAh6fk1jNU+JMTI\niFLhHsFWeh6DAvVYCv/+mqldf/zUk8Eb50UggNMzSj6QKNBZWwvJOLtNlZWVkIK9sOqWO2omL148\nP4zx2At459RDgyUeET01qbeEtoxBmjd0a+251Vtyvnbh26P3WK1Wa+Fy9XJpTBITNXNvO1LM2l3M\nUO7xjPTi5ZqyWsGn0+Lxqb6XYg+EMhxZ4TrudObUVqWT/rDD72S6mZO8Rey1ml7D0Oq7V608feH0\naWwpd+n3T6lvPVzgerFL6NNU7oIXWISCjwc3LCm8+e3+vP7S0F+NU0ZjhbWm5o0t584tUW/cONj3\n+usjIyMj2dnZ2UePHj36i3t/8Ys2/962kXT0Z1ugDbumrXbh3oi9uR4A0F+tqobfPvs2hmHYz3++\n7OevvDL4yp3199//ftObb1Zs3rz5wocffpiRk5ozO15eWLcwFAph3YFIkbJoyNnunQyt8N1cNFum\n0kWV9qayJvP58+dhGIbtFeUVZR6vh29UZHcea957y83um98yPrvwvqM/ofYMKX+SV5iX179cv1zw\n13N/1ayi1/ugteTYW0eP3iaqqfE8kpXV+7XXX1/yU+uPPEcadk/EbBOiFJHoyS3SJ3/4yciuTG6q\np26xc/OMuby86Xh4JnNxQHvxWPBpxWpTXjEGhYapmCaHQmbMt2651feVN15fvGDFvRMrOLrJkakQ\n6tz/vAJLUxkCTGDBxg23R1IjscGusS45hmGye0OPdX8gfcPeLmA7PEf7M8rz72eRWWhBdnbJcDiF\nKlGGLOiCPdDAm1kDAubiBcfg4KA94GbzCwyLEA+bNgofLIvAFEtt3flpylM/+XbLkb8cVy/SbMGK\n83Hbk+//iJCnL6AVEIc/7IwyLKk4QllGDctK78OdbBs9oySxrQvUWFN7Oz54og0Rc/UQrNEjHnMg\njHkH+dq8Kr6wRBubOddKmbL0HFNZBnrhb8cpAQ+GpcZsxj08SofCGGAROAIUWpohnRTumYI1+VWw\nTKqjJnpPwur0YiZsD4BYNAwYHAUwm8XhSnPISGSIQWgAKVOzYK91kqIojCOWqAgyiFMEYAGScNF4\nbPJqP3u+KNatW8fceeedoLS0dM7vk9/T1ytYLBaQSCRALpdf8kC9FpHcbTsukJEkCVAUBZFI5NIU\nF8viXcwTO1Ym7j/XsS/3Oc67EpcTp3jWGADgUsZYPPAYD3B+nlAXx5UEPAD+sVtl4vxy/8XPyySL\nB4bj2fvxTufRaPQz1RBfVnR3d4MdO3aAgwcPXve8C4D5jLJ5zONGw78jo+y6Ecp6e3uvONDPe0Fe\nT5DJZECn0wE2m321h/J/Qpx4xdPyURQFoVAIhEKhS95mcQKXLIjFo52JLcuTfcoSzxEXyRIjwnEC\nFxfAOBwO4PF4lwhPYrTy8yKqyUgkZ59H7uYSyObC54lkiUQzbmzrdDoBSZL/v36f6xElJSXXPWET\nqAob0NC0C0JYf2fdGOqBEJaQZvAQYAALcDlyiIYxWKAyAIwEgI65KTbCZ/CYCyKiKIDZDATRYkAj\nJIBINhCrtRAMw4BgCwCM2rmKjHzKbTdTdDjKlmWnUN5+M02SJJutEFNKdSrtc0ZgmCOQVMgycXqd\nmHS09iqEBWl+LU2T595pASKdFGYQmMcvkjKO7gBaaTXJir+7ju0HFoF0WqQcDh+x6mQ5kcEJjzDH\nKiUGCp0obkZi1qGuLEk2TjywbZvbO+JKOX/hmFIZVk4Zi+qL7uY12p5zPzMx5uEj/GyZLLW8AitH\nfbIThw7xVzzwbalZzw8iz39UgpQq6FSRNDbl93TAbpNMOaVD9+ed0u5Z8zuo6cxw9IPJZ71erxfO\nyclZ2d319Wa15scGg8JA1Mw+rQ/xjtp7NRfGdbbHCmjNsKE1a8LCiJSlNCTr2xRxLjZEDViQq+0a\nnzoUWJe3LvD8uefN5cJlD78TRqy3FVo1Glrz0UdnPyotXVCqq0mtofajZslCiWShauHC/fv272cZ\nWawqf2Njb/bEYkKEvTbT19ZZ5OYVidJdomF0cWQ4l6ZTHQ7HaGlO6YO1fRvoD6UDYvW091NuKsiy\ncLtOK2SK2zi1teFI32QkeGpgfJwYl0hUkkgkErn/fvT+k5SIck002oRTQ0PkkI8wapzqqLckam4o\nLub3HzuWZkhLe++j9957ombDnwNBYF5wT0zvby4UPjeiclXK9+41sPPyemzNzYsWLVpUaCksfHbH\nhx8W3Rxlu91R95IlGUskkqBEG3ys6IWUt2cyuhRdt56nVlsJDd/HfzN0cnGdfEFoI80WwE42edh3\n6HTH/wiFQmHDncY7pa9JxM7GBsjKm5joD1vkRUu6Kvt/j3yHQ5tMpg1p35+6YD1gQiLtNpvNVr+w\nvn6Ink1N4RWv76bEY6Zisf386dOn0xwFRajF7iJpN6h9zJCJV6XeOft+4BgLHz7Wb/VawaKMpTI/\n30V9sH+/hwnkwtpUhZq1SM6vCSzxnBdd0BRmZnK8R/eEUhYsQOEFCwKCT2zRVqSVPdrnRqTGFIBD\nAgLyR1lhZxSuebSS7bTZopgrzDJlZXKxD/3OjsABmCPSsXgyA9sfcuFkIMDmiLWMWC+kAy4XxBMx\nsFKWg1mHBik2E4HZIhODB90g6nUAwMIABCQMzaEBg6GAJ1FBEMRGKChGIQQGs0RqGgt6GQYnAYH5\nYJhjYBjYzTA4CvPEKTRJBAFEAxqNjF/tZ88XxaZNm5h77rkHVFZWXu2h/NsQfz/HfVOFQuGl5j3X\nQ7ASgM+KWPGgYdwTLG4+H/cGiwtncfEsMUMMAPAZIQyAuT2/krlSPOiZWIEQF8auZKafeLzEeWIG\nfnyeKIxdaf8rlZJeKdssucNl3Lctnin3ZTTvT0ZbWxvYvn072LNnz5fiQueFsnnM48bCv0Mou27M\n/BWKG8sbNxAIALFYDLhc7tUeyj+NOOGIl0zyeDwgFos/Uw4QJ27hcBigKHopNT/utQHAZ305ElP8\nk4WkOJGJl2wmlk4kjidOqpLN+BO3SSZAcwmvc0U1L7dvImG70v2a67hx0ha/njhxxzAMiMXiLz1Z\n+7IB846MAIiBGAJhAGBgCGLxGRhAgIKFLFiiJMmol4EYDox6wzTJ+BgASEBgbsAADgRYXIgvl8Mc\nWgETDIZweFyCYdFsRghxxGZW2M/HMFv/BRhC+BCCCADuBTAk1XH0bhE/T1OJTQrHWPpsAdeYYmCz\nD9DhwTdaAKX0+YOTQcqtVkAwW8pThPSixowV7IGCCEb3/Em99icPOC/uPSkKCRBUMKLodTIEdzRs\ng8SVJMcckXBS1VzKI1MujEykjvKxfrizrbOEqiwrXg4tP3ni9GiU3THT+YfFuyWsoCGtVqgjhypX\nrK1K9/X27zhdoXz4YfsZKILGjp+zF/PUIGMy88SErFWEGJezbcWx4pRO81lJ91DRZG344j4uB1Or\n1XKGYQQZaLZb3PC2xGaTuFyzLv2U3irk5bump09O66XasylQDXcms8+m1SrxWS9k9x4ZHj5Rnp0t\nETTcHCwJV1jOGC1LysrK6piDvDckxCtbe6q3Mvf21NSk1UzhkzgeNHqtFV8jF1P2svAEODERrddo\nVOFwmFJ8FPPmy/eMvt13eH00f716q11tP7+5EpO1/D6GGwshbyTyLR4jO/+rDZMWcvvL8CgMG+GZ\nRn/51M1G88a+Gf6u3VOsTTchWN8UpDauREhLO+SDoKaPN8ycLDhcImzbS8i4Su/yNeXl7+7du7ey\nEqkVfnDihHaVXl9WXlY2MDow0ExY34DKjfnRsRRvaTqzcAu/u/vDVofjkWhpaV5RXh4AALzc9PLL\nypoxZkHar55r4r70ioaHayhcx6cjCMJpX8th94UL2yoHmnqmW3qEULXQimoMOBhX4ac//XTpUt3S\nDTdX3nkSKWB57CdCM1t9qDGkUzpazp6iCrWF1Ns1Q0XfHfxuYAezQ+gT7IXY2ZBby9zPyqsZAITL\njUy0H88KpQlwssIUnPrr/pszMzPVEkAfwyhUYBTi5sOUwH3mv//GQ5cYAgwiqeSwnSPvnvibZPHa\nTaKClJTc6dK8YYNTRYPRLlev4AM2k1I8dfF3p1kp+Twlm6b1g7tZvjRzpHjNo09YPdPfp0qVKbD6\nooo+5G1BYYEfGfy0m2FECIOFwzTeL9AUZi3glS+Wh2y9oxTPzWKV3VSLn3/nE4IIO2DCoYYQmg1F\nXSSF+SwswIHEIr8JQ7U4xWLEiJIrJkMxByPk6Ogo5aBjMQ+DBnEE4usZBA4gFBAwlM/LMAwF0TDF\nIByYpgkfjAiVDIHbqVjQymZECgpGQ1f7ufOvQLxrNI/Hu9pD+Y8g7mMKALiuxLJkoSdeohk3/49P\ncX+zePfMRI+zxCk5SDlXmWKix+xcDYXmErISj5G8PhHJ5wXgH034r3T8K3m3zpVJFr9/iVYf8Wyy\nONe8EcDlcq/ZbMp5zGMe87gauG6Eshutsx+O4yAQCACpVHpdvqTjxCOxc1A8M0oqlV4ygI3P49Nc\n6fzJpGkuwSu+Pjkqmrjv5cjXXBlgVxK65uqalHztiee8XJQ0vpwsAMbvWWImWdxAdp7EXH9g2Dwl\nBGgS0ACG2Cw+wyAkYCIoYGA2zWJoiEFgmghPERTM5wg4RXgUdkGALeAIpakwRwljIfMABMQ8isEw\nmKBIJubx0zCfhwMhzGPDObFIuJslMolxnb6Cso/3sxQ52XhGsRAMtZ+Hw9IAJfQoAgM9Zjjigtkw\nxaWk/BRq2bZGcPCNj2R1Fesybl5SMnH48CgtZePczJISaufECWy4s8fBVQh5U3CKrJ5Z7W9C2kUb\nblkCDR8+oMrIXyoYPnaU3Jx3U/ht30SmvrPGOeJl9p6Z9qLynGIVUUQKlgm3BEedZ2BtuvJRqwhB\nFWz2wegYHSTff99oTEvrayeK/dOxoU8Hgv38hiVLtqTScLPrwP6LR1yu+5V3333iL4e/ZexTKHL1\n/IW7786CMouFS+lW+5nBE4MnioqKitLqa0yxiz7qpptuumn79u3bN65d/53+mBN4VUEV1YHygCgt\nMjSt0z1TRoffPx87ww8eCcI35X6rVZ9aXT3lqc/mHeh792LKQTTYMToz6Zqouf3Pf3Y19+Z0D5l3\nGjIw70UxR7yprbTh8Mo+a/QgepwkSRK/98Ti/adYz2fk5gyEzubmpmGfCg1lwoK+sdS+Xc4MThbk\n8TQ0NDSEDP0FyhHx/jWnkao/qHq2F+F4rLEma2VrYwASzOTnhxSI7Nip11/NgEpLly+31fB3Pvr0\n3uHf/giLulzdZ0+erLp98+aJ9vZ2i6XbstVY8fTB0fDbVNQosZq7u6vMkcXmNZSobKvjsZgr1ucc\ndDrl1tHFdEG46zYk955Di/rD/lN1CjPQBwu943xcODUV7N69uwwq3Tpmswak0qBUJkNl9P72vZKc\nnBwvDMP9RVuLZlhpVemzrQdGTbM6AarWAungCOTgp6BtbU6eY/n3yQNpO9dApUtOT505vQZRf521\nZnXwrcPhgiMDZ89xyRzj+9H+kysqhAUuB4ZNWbphJO/OVAc/IyO7apZvaz3n4DUUaOBDjUEG32ey\nei0Weaa6vkyaf3czOuYoeNDUqOxXHDcUrnuw69z21+h1WZXMXsbL0Y8b7MdHm+x+/zm0i0LQ4nbY\nlPO1r1mwSTsctkPpEtnIlJa9nPTJ/VhxZSbTtPMw8Di848ftx9jSQC4X5mghHzZDRs/F6OotdyEd\nBw7QMMOG9GlaxDYzQqMeDyyGOFiUH6KiFhcNaJgjMGQQABdCGCsCUSTFlWcWEKjdR8ViswwNIRw+\nIqUitBswZBhw+FKGgeSAxmYgFqGBeCIDxOAxmsQhCBFqAADWq/3s+VfhehCL/hWgaRrgOH4poyou\nHFxP15/IvRK5RJxjxTlXvGQz0ew/mXNdbh5fTp6SmzElc6grBRgvt0/yfl8ElwtOJpr3J4pkc3G2\nLzNulOucxzzmMY9/FteNmX84HH7mao/hPwkIgi6Rtnga+/WIxFT7OHlLjHzG/bcSI3nxcsnE9uWJ\npq/xtPhEs9XENP/Ec8RJbuK5E6Ogc01zZXgliljJkci5Ms0S8Xlkca70/3gmGZfLBQRBfOm7W14O\nYrH4ujeV/e9X9y1lKB/GAA4CcQQyiMF5NE54EASQDEOSMJujogENsVgUl21YX00CQs5j4SJBZpmR\nCJqnAc4QFBV2wQBRAAriMgQipUE0Il7y2A8QUmSjFbpSXsNt9dRwc6tUeecSUtvFJlp7dlFBzEtT\nbBkZ9EWFRanr2KnrVUjqhFz1p+//mNhz/F1W9oO3pD08nN73u4ufEFOsLtLThLv7enrIMJzDTaEU\nXNlyEUfDt/vD9TRcmZ5xPwudHlqy7g4n+j4uWGNtCD5f7XbMnJx12oXDIdI9uaFyuZTKKayeWPJi\nbRbF7k7fyfXSwmM6zKo+dNx9RpX9o19ucf3lyIGmye5+vKwkXbMu9Djrtorlm3qmRTtOhx3KLHEw\nR0fp+us+1giRBqTRwan6sMoEpg51H+99f+idsTNde1/c8uKLBUXVjSOH9x0pcBSmdGrwsHtsZGRt\npuZxf/rRQLSN2+YNmdtmJ/qh792yqfIPbe/02dGcvGlasnHqpmnQ8EL2y7JzD2dAqQ5mdNqzl12b\nVcu6deOt4//fs0+X1ZXzlmYVFKhyxaoenkEzfOH5XzvTVnzTzvt0LI2AidFe0xEWS8/ie/l8dprL\n9Q2D7Pby3IVLLkjO4CxwX2G5rcMpK06VNe/ue0OzcsnSP5bKPMWVoLIrJY2Vb2wjoVSZqSPUaBrb\n/twz1gZePaccL+913EwP0Hs/alikKnG7Q+4nf/bI32RYv7231907Pe2aHg57mzellj/YPnHyfes9\njnvyfb35ZkvWex7tHYsEahu1esGpBexld9Z3fbBh8am+Fy1888hfimRSmaNTUxaJtbcfN/eI7lq2\nPiNwrEuTU7x4W1MBOotBBRAGQZACQRC1Wq2+qzvzZtvoS89+o+Ce2/wd0ZPYGeXU+tNjmbMRasi0\n4sVvPdXzP9mnjKodff6ebs76++9vQjtbU/f5RpanBW/rvUebTmk3qv0TNparU8zLmuV9Q/HrsbXe\n94aPr4K83A5fuGNRSzGbuNmlHs04XljGFl1MUfEz2lusk5YZH+qYPHU2RIgx9eMLnuu60NSp+fqP\n7lva9OlbI7q1m7AZfavImCtBAiYZKzWaZx3q+9hhOdwjorIyY8PIpE9NlYqmLe/TORuXKoymXPnT\n2Q+Hzp07Kq/56V0Y2RQOTA4fBCTJwr3T4yKrl2GqeHWsqHaGxZFwkcV3b8Y8fb1Kw7blWBbGxs3j\n/TRNugHAIJrGo1xCmcWAWEggTc0S6gqK0fDkGCPTpHJlFRkgMo5RMIuB2QgPELEAj8XLwil8CmIj\nfJgj0zI0EeTwBek/+eH3Rq72s+eL4oMPPnimrKwMGI3Gqz2U/xiSBZ/E9/71hstlgiWWRybysLhQ\nlNg8KbkrZTLXSuZEifO5pitxsGSelVwV8HlTIv7ZwORcXmrx/b8MVi7/F8zOzoKuri5w5513Xve8\nC4B5M/95zONGw7/DzH9eKLuGERfLUBS9FBm8nsjaXGawydlgiSQpkYAld7ZMJHiJZOtyZGkuknU5\nAne5dYlE6kriWeLnK0VT4+NKHh8A/yskxqO/YrEYcDgcQBDEZ/a70fBlEMp+8vQ3KBEpoNSC1EyU\n8brlyqUGXICncSguzSsoWcIGEJ+lUKXKpMUIPtPu4HNFGATFrFHn9ASDRZm0BUWPRwhoxFAlXiNV\nC1NYVK1S/Y2nboeHXpiNiERsecYCKXTh1BEyFg4oSVgVsNt7hOkFC7XLiu4QmmYUgiDnbNEK3orw\nhJAlW1u4yPvWme0owdCqCnsB+Rf0TdTnNmeo6ssgKFsmNdZsgb/dslTGzWeMy+uUxXKJhF85sGzt\n6dzVbYX+bPNfdv5UxqtYwOzFu23CYHPBV7LWldxZuzq1oiH9wlsfv4UZ8jSlzpqxDIYrHxYxXeFh\nYk9foK8P4RRVTzZ5UQ4hZPQF5UU6rK+9VFnrfdQQZO3YM/RzkwKbIrx+L5utZOvS12/mnbt/xVu2\nZx7BpvpaN9ZvWscVNmxYCzmqB1rOnW/nlKqLMsTRmYjRqMqIhmJisfhER8ubgdUP3aWtWVPxcMOG\nRW2R7jH9moe3aoKhkSc29lfwV6Vyi14S7vffZrvPD1pe3N/Z866OVVLCeG0jpRtrNioduGNiYmIi\nS02s2Ts5ezLdMjZ6V8XAkyMXQj+r1+Zq8cpbV5PmzoFflzO/ft8jmym6Y3DpOIVbLuZXkc6PPO+c\ne7isbOKtP//K4XA4XC6XKz1oEKp0E1tH3u75Od9sNguOaBVKAAAgAElEQVRnS7QqoTFYq/grLTVt\nXoxLuNS3Vv9i/d2qUc30SEcPBMWgMefme/qCvlMXh10Fd938nTVL1lRWIudGttSVmKFxRjc+9Z75\nvaPCJaOOFfKvsN/d97OKzQsfV+ylvWc/mfU1epAWaLGzqahIVzQD0rOD1PQRjTFHkqd68onpJVO0\nL8uu33pzCqCVoso+WsJ7LDeWMgsXSyLqfeIuCiLkKW4m3FaefurIjndSxUHVjoJyVyxVijgmXzvy\n4dezWdV9qVHDhvr6ge2vvFLAU9ADegBuH1i7TCe7sFc02t0Khzo7AuqoZTLmPtjbJ2vD5Fp7fmU6\nomSiAapgcJkR9w8JRiHPkHIZqWLrMfkdpUtvazj1uCh120rXuAFEzp/fxZKu0Fme/e73mbuEt67o\nGT2RtVSYqvf6puhfPfo4NHzGC7bh2wSzVdMIh/Gnsk05XJuBl6dauyRQbSsMtc7OIhbEp18mWeU+\n/OGra5Y+fg/kDo8JlhBfQQpWVGPRkB0dc54RARaPFxbJUh18grOmeqnXstssEZsV6tX6rxDTGmv6\nIt4SlkxQzA0Xw0iaE4RnLaOEzx4wSHIW0jjsZYLT05mmb66PcM0EWyGRcU01q0in4zxLm1op5eXq\nOQTBFnD4ZCTsGPv5z34avNrPni+KG1EoiyPRBzX+rgfg2n8Xz5XplThdTiy6Eh9K9maNnyf5cyIu\nx8H+L4HJ/4s4djlvssvxzcSgZDwAmxiQvtFEMgDmhbJ5zGMe1zfmhbIbEHHxJW4mmigQXctITMVP\n7ogUX58c6YvPryR8JR8/0XQ2Pp+L4CSLX4lZZnMRtsuN5XLjiZ83vm6uaOTljpsoCPL5fCCRSACC\nIDdMZ8sr4csglD373C8MfIGpKAxiLpoGMBSLeXgMzMGZoI0TC/LRsG8UiqCuWNAyTdNcBMZZMIeT\nmUJJgEIgl5cjXMpJaNflYcO2TiaSHsAQrx1r3z+NRW3jlCU6jDgnZgk2xGN4ihy5xMeFFn9jFUUH\nPVIVrofKOaqYT8UJzdr9kHwxQjbNnuN4PcrodE8ft48dhGoZASqtL5X5psb0d2tXco+9K00Tb3Ah\na7Okgdf2fegOixSYexDtQdOP1clRSXAqK4PnRkPKarmVoGMx25heVsmST8eae5qdTqczn8cwvmyo\nYqbnjDOyQbLEOlpE59aWlpZytEZsRB1ad09mqjXY3g6Ksn+S2i1KjWxwKmR7CtPgDMypr5ZvcPNT\nmb73XSC0xjK0RtWzYePSjBqzhBu+adkZ9Yqe2rLOdYYRE2fQrRKzVdGoe1Qs5nC6zp05I5PJZFFr\nYDSoNGnsoiHBPRkr0/d/8sG71nN9fSEYC1mPTRw72nr+qCJdnzHePHW4uLi4WKlKSbknU7Tl7KG2\nVxqs2icmI4sy+8iMoMce8OtlGGYOmfqlUraUU+AsiDWfPlmUyy76S8E22kFihbnh8v7D8jQ2eiSF\nwl0tLSyJRJIVDAYXLhQt1NRx6pxWarh1b8sfHnxw7YOYPmP14kyhdJwvJl797dHf3vRs9Ludv5/+\nvUooEAy28MJhst/SIlj7gNS295c1S7PXV02webjPdfG5Pz7zjPyx+7LOxmT9dbbUrTwBXK2gwx25\nx2qKJtCmg1X6GRExW4f5jYFzcG3VQ4MRehuVlh1gcVWzWi2/1jo4cX71gQ7ZvnrKs9Z0K+iPCijX\nqYm3i6tyco6/2/LW9EBHBy+YGqzPKLo/B03hB9VNF43Ll5Y4ov4J/frspdKoP3pfasEdoa7lUUdK\naqrjRGtr+ULiTrhSnGY92LdvkBo8bfGgljzi1q+PVFXp8zGHPTdXox4Z9k4Vazkclh+AA2hR0dTJ\nT3/sCm8qZ+dTgUU+xOcfGxsjWs2tcuKFkv19Tbv02Z1Oo1SQOtVybCdPrVU06jQLz8ysEaLhMa/f\nmyGi9x89YHoor0FyQNmjQsMObU1uQ2CGslkLMjKy9ZOjw/v3HqX8gxa+PzaO9FdGhEXDZe5xtxXL\nqF3Nq47SjMM2irM3F/FrFi7gerFAEJrCuBxaGe093UwEKAqOugl0MHVIwC3NiNqRgZiHg+ILFhqF\nmIVPcDg8CBHxMJyaREOzZgAgAR4eCzEYjVLRCI2gsxY+VVoO0XYrEfMHMUCgLFiiYbgk+ZMfPGW7\n2s+eL4obWSgDAHyGvwDwv6b1AFybglki50o27U+0o0i2pkjc/0rllcmWGMmlmgB8vjCVGOxMFsSu\nJJ7Fj3ml7LHkcsnLCXDx8ydWMcRtLW5EgSyOeaFsHvOYx/WMeaHsBkWcFMS77ySSjGsZce+LRLKW\nOCVGbOcicXO1IU/seHklE9o4kgWpuQjb5chZcsnF5wllAPyjh0fiNslR2UTCxmKxgFAoBHw+HwDw\nv6LfjY4vhVD28muLGSoYYwvlPACxUC2/rsRHNdsYgqAJmCeiyAjGk9AmuXSRHsdIvkC5MI1hMApH\nAiiXT8u4ahMUGu4fE7JFfIiGAebrCfDEJRloYHqSLWWrVNXCBRDg83kpnvTMynC52WwYktJ0FOcT\nMc9B+244ZGXE4pjI1T04phBCKj6flIejnLDu8a9vllZb8wPTpLNRUdZATA8E3Tx7m36LpWbwXMpp\nb/8MrTLhXH+3yM3iRKhWi8UBsxfoxOLgtNA/1ufM4K/IE7BHlZhOZxZayEKtXstk6bMuvv1h26bC\nFY+ZL7a+FnHZ/GW62pLjJ195Ds32eIgMhQKlKCqv1aV1l6KCzrfNPxpI82CUPWzuM+nzY2jEtSJk\nwo927R8szFnmGhYsUVrUKmHrqx2v+vJ0XkouEMgkcs4IrLnF5oJmWzv2frwuf926ftpQUn/v2iWB\n6elp/BwhyBL/LjpTKyhf61ye4oyKylhiaIrFErJMLBqtqSmrwTAEkypZLN4MKrVV1Jk6Z8++tvC2\ngrQsm8tl4pGk0+Z0ImI2kpkpyzzmCXiK1fJVpbNyRWjGfwKrTVdq8HdBZmE4hxUtLIidKxBnpc3O\nGoQDQg5Hz/GbFeaUlJQUiqKodLIgm811jxfqRYU4wXWhubm58s6u0Z4hogcf45Tm1EmjIyPuEVKl\nrizOM8KB/mMXMaCcJJRCIcIiCHHF1CKRSxuYHe87JuVoPA7a4UitRaLnzp07V1Z0W9mwb2o418jk\nCsLlpRqV/w8aiuv1j42NwTTbqyZU2YIUHTS7UlZJXLSeOnHq3JEzeq22ZNw23tIl0N1WUr0gRsVi\nOGYsBfkBN4+XGe1rbW1dlr1sGT8nqh1RCCsjdiF2bJNRbX3plY/SshUsv1GADLLLUk1uz9hYCmuN\ntqbBxPf0XoSnIgHGyOJwcAJ33GH4ikBTq2UHfT5Ly759Ac/srL4qK33yYtOUIq9cH+WJFWNYLFwD\ncaBo2UkiKEyBlUiObdY6ZcPlel3WSF6JPdxjhgKeiwKBTz5gRUUSwBvX1tlvnhzPtKc4bV3iMqFp\n1h2msvIpwjfd24LiLKlQM8RGA2N+1yjcYdArMn2jE5PEVOw8VyVUsz162kPMzBRVrlwVInkxtWo0\nlZfGy/Tbp8eYGBKmWEgY9XWYBeIcOURGOVGX2akypBShMbefxLkQAsn4XEW6jsZ9fpUmrygC9YUx\njPCQLD6HQYfNMJ+lEvEKhTBLpeUJ+cZobHr2pz98+rr3KNuxY8czZWVlQK/Xf26m0pd5SuQtAPxz\nhvRXE4njTuRHibwred1cy3N9l7gu8R4BAP5BiIpzm2TONVfWWDLXimMuzpUsjP2z2fuJ1QvxktJ4\nA6hkHnojTvNC2TzmMY/rGf8Ooey6Mb66FsnI1QBJkiAUCgEMw4BIJLqmvcsSxbxkspX4cp6LnCQL\nZ4mf4+LaXCJbIpI7Gs2VNZZ83vi6xO8SMRchS9w2ninHMMxn5nORtuQOVYm/5fz//csDHovkoBg2\nQRNugoEBjQEzg2HcoJ4uzHfFzGMCoUGGUQ43wCYRJruqnBWE/QxGMlyUZDDcMU66cDuFhzCYqy8m\nEFoo0a42RqOjbin3pnqwXC0gnHtpTJkjVyg2gvHznzSxLBdcVBpPRrb0TxAOX1hVoM+NupehTOlk\nOlApC/Eh/lT697S3l952orr9acPulFsVNSMXtkftexZb2Kl1uKTphMC//8x5oTGl1DXLCQbUItoU\nOjpS9gj3OfGL4j5jkPH1SCMRvJyWhl+e6j0okUpNotkoItIyEUsEV/vrUi7K0TGPalvequWTwlzo\nHOmh7r/faWqCauQ8zXvqMvXaiIwY8O57Y3Z2dvaJ6mf+64Dv7V+Tn9gOsTOhBpevUJ8GE5/qMjPv\nDfQfNolbNqUas7KaO8aOdJT23/O7bkPrr5ctZm1u2sBSyv8il5+3nz+f5dE9JmgFraru6Wn5CW3R\nroJKi6W33Bbwn21aSnGX9mlEsY3cjRvJPJJ0Os86RSJcRPILlsALqxzUmaY2djqRPjo2MlIOpaT5\nxBPRmybybxotKhNR6O8cqm5hc8mdrALd2E1pJXrFOnFPoAKQy/5sbp4+lrby8L3+VY1pMoWR1l7Y\nyG9vCJESYEeKi4uLZbnewqoBlcm6wGTffebcbgmqX7vim5l5L95x/JHvPHrf4+1j7e2dnZ3RjAxV\nhomXITv+3q/ei8ViMR7PyWtsbGwsUBcUBCe21rTsf+y5u0Sqb+u0MSQH25CD0TbsoScXPaMYoiCP\nUujpGPd0LFv2kvQ3P+189cGXa172tYE2AYZhuJ/n70tJlThaLhi2pd5R7Ph9LEYS/f2vHD9+fB1/\n3boxzYwRDWavsd1LhoXHWD27Dr5/UMpHpW0SSRuyB+kk3F7ElPH1bUW/+e0v3U+v/vbUq/07qNkM\nPYz7CbI2OzuDOCUf7JZ11xhzM0mxf2HXhn7t3b23njrOjoZ9PS0tVRqNJjdFJsNv/tZTEXxfamiS\n/CPGXFyxbOXta4O/Zb/5LjWB5bg0F2IQCikDcMEmclH2MfvktOTHB6uY539cMoMQWYbIeZIvnOq0\nfBJmQ4zqtJhwFfSOu1skgampzEfoX/QfyepsfLLkyY43fW96CVUup9GzgBqoA163YJIr6GD7Rvw0\nEXJ7OIpcPdx2dK83ZpHp0rKq/D6xhwUmuYbNkm/OfBR+igVgNUdTkOL1t02KEb6RjBBEsD/WTbE8\nDhYuxNh8rYLD1knQTFaF3zka5cFZJpiFhagIw6KggAfDYAmLcQoYUkCTMWIGUIjoaj93/hUgSfJS\np+obHfF3NpvNvuSdyuVyr6kM//g4Eu0iEkUvAD6beZ8cgJxrnrwu+VzJ3OZy4lfitsnHTuZMiTzr\nSuvnEuqS70fiWBLFOoZhLjU2mMffEYvF5u/HPOYxj3kk4NpVWZLgcDiu9hC+EOIv+n8F4sSBzWYD\nrVYLFArFNUPU4kgWhDgczme6LsUzza4kiM2V3p+c5p98rkSimJgRltgtMpF4JZZVJBKuZEJ4ue6b\nVxLaErdNHmc8ysrhcABFUcDr9f7L/h9fJuh0uqs9hC+MmNM7DHP5YggRhSCIYuSFF4pm3IulAQdN\ni9KXLGAg8zTsxqzplXeW9ze/PxoM8QIQTLEI1DUpFhWXKIoVOvPIqDWo0gJezi21sdGWloayxY8P\nyQf6bF2WVhTLsyL5i1ZOnfr4SG7B1uUx699ozmQ5BAmULChblD5ribhVBWZ25ar0b4p7ii3Ta2sL\nfVUUdWDLA0+Bspo06eiUIYO8LegRNyPUoFj1UbP/Hpm6ZrkwJ8vEZ/tdnBPLNy7dcOD90x82nsPF\nb+2vvDvt7s7TAjFL052V+5LwJeVDxT3do61nJcZiAxzQ6VKN+/bV7V1819HlPQdHZvHwaFl9me/s\n6zs45bodIlboDPXxu28dkclkQCaTWYIE75zz8Buq6urqMoche3rmyOiRwK9b16xc+t7BXe8/LHs4\n7+GaM+/ZC9Y+svlXfyN3oqvCtujuO+88SNtnoGPDF4eiKHfd0/ff737vT+91nfdEc+DsR7c+ebN0\nYGXrtv27dvzFEVKHxgtM421nz7aV38Y8cceDjxU+82HMyX1v+mjIZP/oue07m3Acx3/4wx/+cIdA\nq3V/8PrrXC+XO7phYcMvc84a2t7a6D3o2+U7dCjjkHjb7D2Ztne8k/tY3xHlVeWgxbHi2baBPd2O\nLsfW9x5+0vmD/hA4h7brAVkrf08u/+/h8z/sqg4WxY7EYhxp1+Lumv2xT36z4Y0Hfpz1VeHvjsrI\n5YN56ekZPrOZMbt7//a91cZNmzrJCxdqqxXVI8aL9zs/4D6kMY+Oaqqqqg6aj75tb+NbK7bc/0K5\nItoTHDUNucRuMi9/qPjkcCDW/UfsjwW/1/zef8R0JKVCU4HNTM+I9VKx0hoIrKCehY5c+P2RmZK+\nPpaVojQajUa52qYc6IA76v1rHv34UNji0gSKJFx5xZTtpadKKlc/mp5utfbv2rUrtv63A96QxFSx\n55VdZwJCdYlgZQa+isWeaXqjh9Hk2+2nsZS2u85407Oq2gpfii4Fa32Ad2JXmqwIXXQ2fNOMuL6+\n3hjNbCA/yfizWPHfvxk69sc/Wo4dmLC6rEMKIp3rglwu+KvGV929hHOB3tRqqkzd9N5Tf9pYqn1J\nrHTaa7S1ulvDobvp2cn/+p1zewTjSbiK7AX6krSsx2q6Y2+LIejxLccP/eBZbnp6Os3PMKr5R9mi\nWw5qpLueLm+ztb7OT8lsgKLeXs/Y8Q84wqCCAWWIubP9lLqIt4zTWhicDXg6Mm9Vfxed0l0IBMVi\nUKhuoIa73RDcMZqR3lDeM7jzHZoIkDjd7wWhIQhWg+ycCnj1xCC/T5SbujboDXeSM2EJklGdrWQt\n0IfNPSEeKVWGkKmmq/3c+VcgLpSFQqGrPZRrBvGSPaFQCMRiMRAIBJeykq4FJItDiTwnOTNsrowz\nGIYBTdOX5ok2GZfjKMnllnNlhiULWnFOlSiCJQthl1u+HBdMPP5c1QSJAtlc57zREY1GL/nizmMe\n85jHPK4joSwWi13tIVxziEQiIBAIAJVKBdLS0i6Z3l9LSCRM8VT3OMmhKOozrcqTyzLnEsrmIjXJ\nZZBzZY7FkSxoXY4AxkniXN8lZ5xdjnAll1nG1yV26QyHwwDH8c/cq3l8uVBY9OSKGceHDopEOnlG\nVw1DPx1hjb9wtL5U+LMe30RzFE4fJ0kBablwuBnxx1xlhqeemCE+Osmm8xf7uSpVkDlrFzK2UPkq\n9KuB3pOWmDu/aFi3MOzuPbqTj5mKNVnKbHfL7t3SEKUYaX7xtSzepjuR1GlaXp+xje5oPYYYIZHy\nlh6j84jCB7sO/kGdF1nifaolbNRLK7wDkSbnHmdfhx4R4UEWK7+ub+NN0qW/69M/KBvyjzWD4oIi\nYeCT5xnj3bdFD+75saiiouLtC+QwRytjodtvGtz29XMr/1T/zDhzJgpGw+NuPqTYpzPqdHtcr36t\nJLjkx1Mz039WhMNh2TrZulJu9Y539xw4WfpUxgs3HX5K3kwcbm4r5yuGzqCBh7Ra7fvI86+aTHpT\nzvKlci7S+UL9m4feRN58880PLnTs1na/uZURbmwctFvbRHf4TZbjMfMqUF2evriVxMRAjOGyh1pX\nWTzNZ+iXLGMvCPqxn38L/fiJvXes4j8sk4ywQ2St6aKL/7eBb3+NcK1e9EPF7Sg0lh16sKh/W87Z\n8Rd+Pzk5OVlFjo3FROnpo6w2Pxl4++jXX5ltjyEL/6pVq3e1tLS0qMbHx5c9818fHgn+9j3bmTNn\ndDqdLkigBIbqUd/TYLRwwlW934ntGeG7Be6MAwdiI7FYjKIoOo2mb970sM68S+6NYfaq//nNx794\n4beVv519KeUoyErLIsuzq+8u/2vJidc4huCttz/Qd9LVImyCfz4z0zLzUOFDD70iPTaWWn+fQdbT\n0+M3v3gkVmAp7fLf4v+Jb0/dyUMrj+dxRkepHCpnFj8hkFZ+s9LQHI0e6Whv16zXaW4VHr61/rd/\nyKmurq6ub8xoHCAqllpTz2gDH6R4I/jF1qGs15+mOx2OwgzWr9nP/RidfOAv9JH+lpas/ulIfn5+\n/qFXe18dGmoaEtfW1tYtX/VINusbg1GuRsceKhJbp/LztRWmCmhspqMPSlGnf0UhGxPm5LyW9RT0\n3R//8dXKykglh0Nw3MOHXs74XnMDp8X+RqBYdLv/QPkoDRDTvRvXfz+j+qXwLz+U/cE90jry6elg\nEMriZZ20nTjwyvqD+4/tee3X7iHsLGZ91mq6r+qWlECxwDLjcoWtKHpxaN+hwC70o415f/5dDyuW\nY6Ml+KIfyO4x/1b/qRtvbFcJD3Vp8oKraFaETstHGsZ3Q140rLC6uw9O8hF2SEttmeTk+HXjnR0f\nwN6673ilozbKfPRMiangPuH6voJzLzl3uTOG69iKfIOQRmmYNugx/uxQyGLx2rAFvWy+WiqyqoUk\ndwdXl78o1zE8PgqV6nU8lU+pDS9WeJjj1w23uhISva7m8XfELTAwDAMYhgGJRAJEItFnfK6uBcT5\nB8Mwn+GFybzqcjYWyUHLZMyVyRU/T/L2VxK2EstZ5/r+8/hWcmDycn5k8eMnXu88Pov5+zKPecxj\nHp/FdeNRZrVan5nLmHN+gkAkEgFer/dShlJiRO9ywtLVRGIafLwdOY/H+4yxanJ78sT1PB7v0hTf\nJ965iM1mX2phntgtc66ss/hYLnc/rlSKebnlua4z/nsktmKnaRpEo9FLEdyr/R+6VieTyXTde2X8\n5qMLa/xes50rL9QzeAmbmcGQADTDtduhg2iY8VABF8KHYSEiMMkiuAcVpJqy3EHnCKzONUGsgNLV\nfe6vfFVDiS3QgLr6epuE+ed4sLvbERrz2jEmavWYu8wUDYuBKALV1/70EbP5zBinIJc7Bgp42KC9\nPf8J7WY16ybu8I4jr/MWFN878EnnJzGHY1wU0xRFw+QEwdijbKMpVchbUmtD7d1cin+0t3nn33Aw\nlVPkHa2dDd69qqPz/MGHJN9482yVMlhmFxhoG+ScPXX2w77z9TGnLoU0CB8oqRYIZ1E2ik6xMzLU\nxJq11up0Xj4S9La3t7fLDTxDc1tkzMZTZkda3P5R+c4hbnU2a+RIWycsmZGO3p5dvLV+/YLW0+2n\n+dFwNMW43Ei3tbWdnByerM2V5k4vLTRqhg8sKTuwXobaDXkW2GsRZh4IlGuqdefe/NsrKBo7iw4K\nDLczjlVulqit4mh3/Yic07WpRP70m3jW8VLYiJTV+TJS135n28Uf7twtI/GOlchqKARGhhYuVCzs\n7JzuHBgYGMAYDBNAciYzvzo/WD1RHekt61lUgYoYhs9kZWVl/c8f/vAjtVqtrquD64aHI8NeL+wt\nS0lJ2SeeETuizjZFWjA1OCLYu2LFihWTk5OTzsnFi3/5VPGde04M7UH5/+PrqVvGZMRiMZC9ORu2\njnpBWMHDZgYGkIg0IleV92sdwW7AlkOe9Lqlf/gW8+N9L0y888DjZY3EbGxWSJesU7cLZIVLliGZ\n4Bh45Tj88oq7jCt0umJdujQ9veuP4S693OMQFWrlSBiGTrSdOHEeVp0v0VAlpA6/HVWb/Jz9k0eD\nduFFvQHtFAn8G3LKzDUGdnrlh/b6Ae4hqah8lqbNC6NRZe2CHDAzM8PLzf9eqkRsA1KpdKkyqIT5\njRFPZaFuQFuud/S0NOvEXZ5Uw7BrzDcyPbo3aO7vzpegLQNijgYMB4PBYK9KpQI8F3PhgGI6mvKt\n21P1RYZl4QzltMzvE/NE8ldfGn+B6O+CcvIV22BRVWpYTS/dN/zWliZ3mJ+mFRIp+WvXjoUnTciD\nk9tq6uxpw/uLrSGBLSoVZZZG3INmiloRnfW1n0WGsajzKDImLF6aMvPBawcW/TrwAS4sQWdbMvrc\nioVCHhT2eyfGB4oX3rMSDdmRac+55mnzhWMstjLVYT/6rkoZKGDzA0prv3jMS98qFMX4wpAl4EVS\n1HwSaEUExeLyDTxZyOOheGIpEvO393lC7edxP+VDnR48inhDRJB006JKSRDxhMK4LfST7zwxeLWf\nPV8U77777jOFhYVAq9Ve7aFcE0h8pwPwj50xr8QnEo/xn0TyO3Wuro/xKe7Zlfw50c+Lw+Fc4jGJ\nXOtynq/J44gvf561RaJgkyyQzdVAYK5rJEkSRKNREIlEPhOUTd5nHn+Hw+EAg4ODYNu2bdc97wJg\n3qNsHvO40XBDm/lbrdZnrvYYrgUkRvASW1uz2WyAougls//E7LK5SgGvJXIQJyuJxC1utppI2hKn\nxE6RySaxl+tgmXzOOC4XnZyLmCVul+ytlnjMZLLI4XBAOBwGLpfrkg9EYlv2a+n3uJbwZRDK/vLs\na6mRkLWPi7MhLGyOyBlDSgwb9irkuTIG8EmewAADhIeyZCEZEQiM6/gLc0GUz0FgkuQFqVAAHRng\ncWCljJTCLIor9k8He4TcUm2AmJ1IM6yrYVjhEODzIlxR5RIq+BuUr1odW1wSIW0XmycjEeewv1cJ\noT27LZCP789Ra9JcU55RFi6EQ+5pJ8GlPaaq5bUlDctW2McvNFUvDopG4OKb/BOuzhr9krrSpTcV\nz14YOZuzzF/R5Gs/QJwfOlpYULYy6rK4RGmmhZxFbIV/1hChPW3htExB1KtSqWhzmOSt2Jei4YCA\ngFV//zpJpWZYe6aqekquUKZLLFK2Gl1VDedHpFXcQq5XU7FykakkwrK1trW1WQmCYKMkOj09PV1u\nKS+3ytyOPGMkherFehZJddEO4YWdivzBEa8s4i0RRrPlQ82ErbRR4R4YGChb25jGM+SW6YcvyuAf\n5Qkzw6bw+yrg48NRkhMOjEsUKYr1R/z5smqTZsgWOk2GSHK9yLOFn/VoljRTKi0XFhZWo8JqSfWE\nxOsUO/MvaGIeWdibViJcKlDrJRhZlFFSJjLiESoikhNyFmSAjGKjcWh0aOimrJoMJiQO5cpXpKs1\nDoWVS3IakbV1g4bR0QVeq7qgzlrgtioCK3UFaSNMY3UAACAASURBVKlROPVC+/l9wQEy2Odsb29s\nrGukUT3drZBKVy9uaJgaGh8viU0aIMCMmC35ORPjB4/ICsrSxNpgDO3tG+r0OztPnLCe8Hq9XiE7\nU2LzYrdgjO+M3BgOnT8/ch7avHWzMhvSCTAW5k9PT1+QXqLmSjSOUB9mgfPF4qBJoWhobGycGbIe\nDDq5g0w+rFelLawrXZNSlDZIOPtlU1MiHo+XnrlkydJZhTs8HQ5HBMEgGzPCk1GflZzWConOI7tq\nN1RW5SqIki0LeSthBDMgLscxzsZbakI9nT3OLGOqhIMyHu55CWPKyVDpB5cz09pxlto9xBcP2Hsv\nNrV7Gft5w8Ki+yCS7+WKQ1FIpODzd66w2xfsZaekbzXCs939JWlCqSlQXEi+fQvZR0/2yVIXSAUx\ntnRhY15DJLpAEJo5N+Vx917ksCRiLisGZ5nkqomuk/tD09ist6MMEzoksJ4ZD1bclr9ysiM6HKIc\nBJefZxTgerFWk5nDF6loFMVFMKmKYS6Wh83iASUj5OCRAYhHZMoQQDvYMEOTkRmnUCBFgM9vFCrl\nMjam4wqEGSqC8EXVygJjODJuZrMEAtw/EdYh2XI0SHJ++MN72q/2s+eLYvv27Te8UJbMtRKDdgiC\nAJqmAYZhAEVRgKLoJYFnrvf6tca/EoWl5GZHifwqPk/kacmBwCtxrDgulx2W+P1cnmiX42GJ50nk\nfBRFgUAgACwWC5icnASzs7PA4/GAQCAAotEowHH8M1mS8xzs73A4HGBgYGBeKJvHPOZxXWJeKLtB\nkUgK4ubvcX8MoVB4ibBRFAXC4TDwer0gHA5figImEoDktOprjRwkRwSTDViTU+mTxbDLRQmThaz4\nuvj8Skaxl+uEFI8kzzX2+DKbzQYMw4Cenh5w9uxZMDw8DCwWyyXiFveEgGH4UqQ2Hm2dx5dDKPvN\n79/JoSCCCoeGUYqIMhgyjNM4FORJ1UIci+Kx2Iw1EhyfBhKtCApzlRgW8DBsAdvvbmoPBHtHZPIc\nI4lZkZjX6cYJd4iBaSocm7IKIG6K4P+x993Rbd3n2T9c7L0XQQAEuPcmRYoStSVLsmXLQ7a849jx\nSuI4Ttr09EucpEnsuGmatEnr7UiWbUmRNawtURLFvQmCEwSx994buN8fPUAhmFKc2Kksm8859wC4\ne/Heh8/7vs+LLWWEgCMWIYQRhHgRVW9VfMykP3F/NMikWK3KmSQqFGOTHmozLY6drZAmN/iChRoI\nwuML+djNTj/Ojg0RKX601RIzhlBoUwF1c7sRPzBsm/Z5HR6Qv6o2OOMz2nQTqvt2p0qOvTvwOpqE\nxcZjfpzZZFdLONH5xge/9wyT7A8XikwR/bRtmgkAaJvG3F78q9ZW+hW22dCjPjMcq6urd89gic31\n8tVlRS30lLPEEyAr3XKdzO8xRgKWhKKnp6cHjsViBE5FRUpApwOfzwfXEwgiZIxXa/M8ZKMKZFdl\nXVcJhUw+HQfj/HOGOXIJgTUwmzr3nLrtF4Qt7SxJCMBOKDhRFKeU9PibAgMH3/j9/XVbKgtRfJQe\nhuHBFDrVehq7WrZbNKIcGhqirWlr00T0q2Q6w3s7zec3dsedXVUMTJUda7KDJCEedVXn0zgmE2mR\n6pmtw/KxBiJnVYBQrklYJ0vn735o4x7JKgyPztCIEMzvYSvumsbEtUvixfUqnSkcon13LfViyCuq\n9Xrt40m7j8Osc2gr6At2xem2p1B3TA2EBiofwT1JTog8kJQtRb+XDM7dHqwt/xCLYTHdLl3AMVDS\noyp0PpXPk81aUBZ81DRrNZnqcRFeS+Fcyxj8g9YNxRR6VFCyzrv2nMhvEckqtl/abh8STYW3V9zm\nHl4azIfQ0NylS5eoyQ0bqskM0v4Eil9dMyMl6YMNUW9/P8JURNBaF+Zci/GpVWJGErYrh8eT4+MR\nS1VVNdPrRU6bze+OvfsulAdBGj6+BIvtrCc7TRxnDQrpGJvrh/1+P+SN5RG0dxHiBC/WD8PzFcUK\nHpelt7NkNK7ChtKYp90D66XEPURbwUcYjUZBSLhc3lJUYef6VWtxRq+q5K7Hd9i1XoV53mIJx/24\nuJ+ItikXRp/c9uSdF4dPygGyRerAYzl21dSCmA5ITEfXRe0CwmV1jdt59ttqps2//T2BKeFThEUs\nDqVCFHXCIXw+nehdEKTIDCkeG3ERMHFe1OPwqT0JRBRtoVXAOBCP4gA7GTMtwohUOBxQ2UEMhYPh\nZDISNtt8vukZFCpOJ2FKIYerewYZw2ARZBrGax0zwwAKoKA6fsB+dTYJx5IAhUzhCXS216/UpxA+\nYjwS8EVitgSN0sR/4XvbB272s+fz4usqlGWLY3g8HhCJxMyAx+MBBoO5ppwvFouBcDgMQqEQcLlc\nwGazAZ/Plwn6AQCuyYL6sglmaeRymGwhbLnu4NnL3Ihf5Y67XuBxudLP3PlvZHGRSCSAy+UCGo0G\nLC4uAo1GA8xmM3C5XMDr9QKv1wt8Ph/w+XwgEAiAUCgEotFoRjTLPvavI1aEshWsYAW3MlaEsq8Z\n0oQAgiCAw+EAhUIBDAYDMBgMQCKRMiJY2kckbZAfjUaBz+cDGo0GTE9PAy6XC/B4/F80xP8yIpuM\nfdZMsetFMq9n2p9L2tLjstP0lxPJ0uOzt5sr9C0uLoKjR4+Cubk54Pf7QTKZBJFIBASDQeB2u4HN\nZgMmkwlYrVbgcrlANBrNlJpmE+zljvXrgK+CUPajHz2HBCkkIZUIh5AILITAs/HIJMYTjyPxsZDG\nEI7ZTQiA4BVK1pc4TdNeFA6VSCVi9EBUvYRMJZk4PC0RdkcYMAUTIdMqxRAKQOEUiiygt5W5XTOW\nYMplwMApFhF171oIEbAl2FKpdubDg0k0JgXCLko1Z12lNTI7FY9TQnZ/wOB2GOxYVAUuEguovSkr\niGFT0UAo4ooAhdVt9k36Qm7abaV3PLzgWjhJgSTcWLKQVoxhlKgd00oqkkmnhWmFO/Z4NrEYbr/0\n7W2bwuGDbJ6gaTHg8XgiiZaWisc0xEtzcAppRCQvju//Nb8zIRILSxQeHo83fq7nfFFbyZ1qolrq\nKNmA7Xrv1PnG2nKBoKamRtI53+kYCXRvKa+vdzgsloU7k9/gqWZGlvgid39BPcd45vInvN/+8rci\nJ+S06nS6hsLVhT6N1sd5sBoeGCypmlK98V8oErk2edsGgu/Sxx+Wl5eXQ4lEQhGLxXhes3ctz8pb\nuEcILZ3xLoKUxRLw+/2WeesHq1a1tEhO3bFqEqG9IvY8sSUvqcSfhMnz24YLC6W1Iim2ycyYPjh8\ngLi1rrD7nZHjNDEMF264a7MJ6Re7Df0M/AS6+hPi+HF8o2VtedMeDmNytpdgMQ0NeE6e9Hq93oTU\nUB6a2Vs7IlQcGTh48OA99aV7Pr4w9xHHE4kW0ZeKFhXIEd+qqj23W1cVIadU0xcig2f1DAbjQuLq\nUJtzVapSVpi8HJkapHtiHoEdx0GZcDRI3U6y0Pp6x4suErAKc49vdXD18O+r9RaN7BPOomUh0Gm5\nrTWOWzO1yb1r+sMz35bcs2kTSz3Ryw1IeZppylAXckfTpr0DUpsMklmtVmtnp2N7eVF9YRLuTo6M\nyQ5HQ6GQqFYvCgnbhS88+PDDbBnslQgCKStrXSGNA3nHT506lUwmkzInpEm04mEX/QJ3a+H31nz4\n36d+vnlP+S8M6x9GkeTV1SydTGaVlAQTZZhil1I5hqQUFyO3kNfjYEpK0TV6NjA83IsUw6Sywjua\nF8eVXesohPttBIU2hGe6zV7goaOjq9CeQhpU453xWCcQlEYvwyInUcLJjTRXqMfpj2rm+fiWkmjc\n4fC6gyAZ8xAwUQ4Jh8oTIglWHc47zY0iEiinEhWKlTDxSH/KG0AszIvjqwq9PpPZG57XEugiViyZ\nRBKwDCqGVZIXCxqR/oB1iItvqLBFxlVJJJoWhy1WAbKx2R1Rjoo5dUU6e48sEQsm4FTAE0qGfRgE\ngwpQeBw6SYBicacrmnTY/ukfn9Xd7GfP58X+/fu/VkIZAoH4VDCSQqFkgpI4HA6g0eiMQJbt8ZVu\nfKDX68HY2BiQyWQgGAxmMtHS2WfLeXt9Gd/ty4lh2dMAWN6eIv15vQz863U5v54/Wu48uecvzYH9\nfj/QarVgYmICTExMAJ1OB8Lh8DXd0dP+culyzEAgkBHMIpEISCQSGU+37K7tXxesCGUrWMEKbmV8\nrYUyg8Hw8nKZNn/tuC9iHX/v9aZfzigUChCJRMBkMgGHwwFsNhuQSKRMh6VsspYmENmmrB6PB7z8\n8svg4sWLQCqVAoFAkCF1ucJRmnAsl3l1s3GjTLHrRTGzvy/nJ7Zcaj8A4JqOUNeLZuaaz+aSSRQK\nBUKhEDh27Bh47bXXgN/vBxwO5xqPsnRpLARBIJlMgmg0CjweD7BarcBisQC/3w8QCAQgEAgAg8F8\n6nhyz8FnuS+/yHv1/2K9AHw1hLL/+K8D7VgIH4vEbEkkEouJuA06SkGrCOcGtDgmEERDBDyERnls\n1ukkAy/hQYhSCSrqsiVgjw6iE8hBh4OaV/Forc/cN+x1z8xGYmYXG1PKt/tnFZxCCTEIO8QRJB0k\n/Gc1CRTWyyYUS0HCYESGYlFfQDXmTYjrWGSbQEh5bhVFiEmy8R01i/N9bhSJAsfq1jbW0/3UKkF1\nBSlgGRdjt22yYEMKg8UwymrD34fzghKyUxJ3F/7BZTZyznXe9cSzev/Jy0c/Ur863/izR7ibBizu\nmaY5Vf7SprNdnwSihH7s+GnaIdvwyHutWsoda9jf24vO0w8QWT7WLMIgcVvurmoYg5RW2Hxm9tip\nY4mIzzA5OTlpWlpaci9SF/HSvT8R3fnAzoP/+g8v7aatIQB7BYUVTjxsZuQtFnH27hX8yz/1VOB1\nu3d988cNgRlDYN06yktxfL72ypVXflEnrqsLoY46gr0xeSgVCrkvz7qZBjyTw6FAHBGGc+DA8AF5\nxOaNUGseKMVGFrVXMRh6fjR65eLFi+R7SUlrO2ubkqHvDTAXNypO/0MN4d/2lAUWNZcMdo9699Zd\nu99/9bc/D9K9Xm/0/u+Wuzg4Il92wTSL0U1/J4R3TctHAm/5h3gpqs5e01pe1XHHY6OXPHsJGOvH\nv4zf92PMfcjBhQsXLnxjz69eqXoZuxb7H+TyLva3moyRVlRUPzJMdR8xTs43Qb6aM71NjcjGKKHv\nngESy9yvHB/rmT59YE2iqmr9Y8iH0Hc8Im1EbeVYKvr7O6i+joLLxe6YTKgrj1W4F7DBud0d23dr\nHCDRhpC4ewc9assnih//NNkzPN/5tm1g2NotxKcec0Y0HyQX/vRJKtGYmKr17VjbUvioiPn4RuF7\nx929J4Ny/rY1orDX7daYGzl+rNv9vu7pp3vhtz+abxnfauofeSM+NjNW197ejvv3uX+/P5JMSXwb\nNmB998YVQz92yMhrAZnO4GjfP/Sqk6nTLfrn553DU9SWZ4QbQqdWUWTx110N3dB0eVlH4+y4z9jx\n6J2/7FaUYRwfDitat4i2xlpbvAMn9v9EWBu417wY6o/XuGgplh23YeS+OjrZULakEF20GK58VFHg\n96/ivvS8PC8WSprmFj1+hWJzXeV9JBqFbkJYJyNwv8/vwEYRLhEmgofVVHy+OEYMOBKeGCYW0/lt\neFMqidYFd4te/faE5sg5HIQlhHxqEHBpTBSIR6PWbmvUGY704EiCagJMisSDdpWfGPcjkz7I5Vxy\nUaECBgKPCKUQCXwy7tcxmbd1uKzdlzE4REE85tMV07a2PfPirlu+9PLAgQMvV1VVAR6P96n38Fdp\ngCAokz1GIpEAlUoFVCoVUCiUawSybDuL5YSceDwONBoN6OnpAb29vcBgMACXywUSiUTGTgKAawN0\n1+M2XxZk85vc9/ZnFchyGwhcj7PmTsvu0nm9TLJwOAx0Oh2YmJgAw8PDYHx8HOh0OpBIJACRSMxk\n6qdLSLNFvnRgORwOg2AwCEKhUMYaI93tM9t/7Wbfp3/vYUUoW8EKVnAr4+8hlN0ynZmyuwbm4q8d\nlzstV+z4otb7Wcflbj8tkKW7KeHx+GvS/NPiTiqVypQHpCNhKBQq439lt9uBVqsFSqUSTExMgEce\neQTs3bsXCAQCgEajr9letgfF9fb7y4A0qQXg2myu7M/0tBtFLW9E4NLnMjeCmVtqmUvcUqkUiEQi\nYHx8HBw+fBio1WqAwWAy5ZU4HC6z/wgEItMhK9dfLRgMAp1OBywWC2AwGCAvLw+wWCxAIBCu2Z/P\neq8uN1826f1b79/rrfevWcdfWu9XAYhYMBKLJbEs8iYeTEAl0ZyEQG88viQgfbMslbLqqRRuE4xB\nWXz2xQACC5BIQlgXCjntdGzD2mBQMQAjg24oCdcBCEuikLkSIlOIxsBKUUFs+9qIL7IoAKVxCCXl\nOQhjY1xv9R489DbkhvhxYW1tLZW6YwfbBkBFIUs8aZiZUWt6bPlwAxtGW/o25cf+2dtmk0WUD7WZ\nZ5MaWIJum/UzGEa1Xl8HMdse0l6ulhfcdlIGerS+q+uRdJF3LzD2/VlMKvbg7vt//zGv3ufoluGv\nrslr2laPFZ9FPttA7DgEN70a6r9U+XP0jxdME97m/pqZZEFHxyeMqqr1t/1Qcv/qf1s6dLh+HxNv\nY0J0uGn37f+yuuvIa6898UT1Ewh7McIW1U/t+8l9L0ulUmlFNaViIKAZWLfdNIJkcqvcAJUg1HnD\nB4z0ZwuvnGqewLe2RieuvCxU7lN+55tt30mebkgqi0lKbcIakxCwEmqhYM145WZU+cJAn2mAF4Yg\nCNpS99LzovDkhWCQyzVXTk5WV3OqOzo7OpkmRRvyl4Jz3tbJuBli/ey7lb/e+tOT9/M7+2pYFsHZ\n0YGBfQMSiURiayprcpdf8Qb+671hypH1z+qDwSPWf9J081CUKdY96ztGcUWdazwX7b39l9+94yfy\nTdqDpXkHNkx0NY13SPPy8vLG5j/60PIMqyL2e9VCk/uInACkdatRdz/vQG4TGzcvfeSlEL+PCred\nG3p/bs8eev0ai43z3Vn6RzPhSjKZ5aWJAv/8+vA3Hcrf/fCRH/7wqOiYd576AHAvLaGqW6fLng1s\nyzcaLxu3mspEfanxPmrT2nvvaxbeJw/sf6+YuD1/YLdlt+1S/ysY8oYHa155axP00TM/2GUqHN7S\nUNL4n4feeQDVtuouceIAzVBZl0eyGo219w40DeBxeOILDQ0MLBb7jcpvVLqpvF/NNaL8OsfIAcSc\nZC7o5lVdWBzpo2wcgxFaad/rP/r+7w9RX0RjSbt342UyGU+6Zo3p0bVrmT1TPcedf1ZzKp5pOXMX\nhx+KIC0Jnsv1+uGBP8AUHXP13vUVO3YhqrRdAp9mY8v9QfkGc0NJ4VMQOxhEdp89GoBf/qbM9GoC\nW/y2uIFc87g5JLD6pe+fJfonNSQul4SwRiXDavtYkpKiNHDu3IIumw/PGou1BXR/U8LOEds00QAF\nQiIdUeNcMfnBLQTkcYwnykZf9Vy6hKXhU0SciMYir+EY3WdcoVQ4iE2Ew3iALCBDaIBi0gRIL47J\notaxdYmPx1FIEYlMrhCnUnP4VMANIEw0YPccUhVKHtodCNvdeIKvaMH1524A3rnZj57PDRQKBXA4\nHCCRSF/4um9mVnv2uy+7sVB2k6BsYSy9TPq9iUKhrhFy0vYQaW8su90O9Ho9sNvtQKlUArlcDhob\nG0FjYyMoKioCFAols/7sxkPZgtmX6T2YPu70/t0oIJkdYExzputljWULYdcT0bK3kd4XAACIx+PA\n4XCAxcVFMDU1BRYWFoDdbgcwDAMsFgtisRiIxWKAQCB86lhyuWI8Hs8ELP1+P/B4PIBMJgMajXaN\nYJprjfHXXqPljuPLBDwen/nfYAUrWMEKVnALCWU0Gu1m78LfFWnhBIvFAgKBkPHByCZq6fnSxC6b\nrKW9sLLN7ZVKJQiFQoDJZIJIJAJ+97vfgUOHDoGHHnoI7NixA4hEIoDFYkEymQTxeDxjkp8bPfuy\n4XpZb7nCWHrccmJYOpq4XKv060U2s7cBwLWEy+/3g5mZGXDq1CnQ09MDIAgCHA4H4HA4EAwGQSwW\ny5DhtMCZTvHPFr7S4xCI/zGktdvtwO12AxKJBAQCAeDz+YBGo2XKbnP/2VjBlw+BoNmGQvHIsZQp\nmkpSMAG/2o5EYrFed4+eyCqURpNeAw5JBQQkjubyLWhwPlIljiVBI0LALy5pbXVM2TEW3cdyJAqT\n5NALhe6gTrWuonb1vCXZq3cOT6JoMIeLKxb7TXprPRvKX0KvOhr2XrZ5dChxQT0/aXBenT0wpx5t\nXV16e15xPi+PMomCuU2F/Ybz36ec/P7jKfKlaQLDAdetYW1xaoL+4mRZmQJVIB62un/jgT2ezqLN\nm8c8KSKGOtwFA5/Ya6srSLinpigBU6B1+9q8qP7UKdsMiyUtWbzr6qYGdUjFNBSdrmD6BYbFQa5S\nSWFNcTYEL3TbLlm8b2seHQ2rJp04TvXW1du1HO4Ywf2tZ7/xLGdknjmVl5yi82i0wjv+4a3khd9/\nW6836QsLg4XHjnGP9fYe6D28venw6K7wfWW9jPtsgvHoUj9Defs3EHcOvk1+/b1DM+9VPKZ4jDtR\nVroolSJJF7vG2VLoXPLIIYNbwBEofVdHWqrKWoBiaNKEdJpw0YaGxkYk0tVKWIP/6MP+/s0Vagak\nrSNoKkdqisUbVIk/qZ46RcYrnwKANkijEcgEAqtl83apyE1wHjUflTEZkeY2q1w6WNE+uhalue2P\nLMziQqxh55LOOFrBljns09MpzUt8ufy/5MbaHe3c6Lgc4UcgyFNkshcZeRUbkGDJHVSG0TXV+4bd\nfnT7RskbY5jdHcJ527958735jz766KNG94Sb3dBs3/zbzZuLhPEiY8x9YQxaldj4tODxN46+8cYd\nq5t/bBs98+rOtcy9hq7JrjG6lI4KMsKadQFyC6WkJWBYnAkRkLFwdbDMcJgXWbPeXiASeB+zCMZn\nT3WZf/2jtrs39U709hrkqO7mmortPYuK7roHVu1q7UN454uKihqo1ZwJY3lIUvZemYjL5dpsRpuw\nDnt2VWCk/pQqENT4Co5fSHZ0rG4lF5vmr3RNBcp+PN2PxqLZ09qtNDQNVLc/Xm/qopp9KvSl0dVM\nPmcA2V7bm0QsPcJPThvHV0ulUoXdbpeoVNaoOyH/8JW2xWjDuxgenhEw0hxuWgnFPjx67hwZT9rh\nzo8OWJBnkmsquXBSEzX6UEKJa9HslYRXCcJkGqNDuqfVlpApYSaFi7T9QnvlVN2isKgvCpHznIta\n3SwhxKGKsfFaOw6rtXj7lRSxmAjC5WWQ26Rg8iuLcQQKMaS3xNn0KonHqQ/bli7N0CkEcjGfUD5v\njaohPJcwq3prHwqVLKKQeBgYEYZQgEKJoh0LQbtCjSPRpH6fJgyhaFRkHBlDQsgvXlm6CUChUIBA\nIAAikXizd+ULRS6PSjcOyg4Y5nKM9DJpS4TlAmyxWAx4PB7g9/sBCoUCqVQq40e6tLQEZmdnQUND\nA6iqqgJisRhQqVSAxWIzGWfZhvpftuz+XHFsOe6RHVTMrlzI5VbXE8eyp19vO+lzrNFowNzcHJid\nnQVarRZ4vV6ARCIBlUrNzBcOhwGJRLrGxiKbc+UGHdO8MBgMgmg0CkKhUKY0k8lkAhqNBohE4ldW\nTCIQCJlztYIVrGAFK7iFhDIymXyzd+HvhnQ0Mz38JW+E7GwkAK4ViBKJBMBgMMDv94Pp6emM2JYm\nvFarFfzud78DXV1dYNu2bWDnzp1AIBAABAKRIWu57b6/TILZjVL9lxuXTchyU/lvROSu55WRRvoa\nxGIxMD09DS5evAiGhoaA2+0GaDQapFIpEIvFAJ1OB06nE/j9/k/5xOVmxy03pEldIBAAarUa2O12\nwOVygVgsBlwuF2AwmIyIt4IvKXAIOpRCUNEYJhqJRQTdwTgCnSTnRYHLiI4LOOGEXgUnrRQ2vr09\nEHN74wi7FkoEEq7g7FJsjl9Dq/FVWcfiAwAgQt6k2h2Nu4OXhkqGkpThSRosKXXHlswIVNhDJNNZ\nSRrL71lcSLHzqgrCAV9K7zxpKBN33hZPFFB185dNwroKoX0k4o1A4fDa/M61J3Uff0wjocpX8Wm7\nouOh+IKJYNfLL14sbbuT5GWU46s11bf32r0L1rA5xIzL4lxqjccbgblbAiLRR0nhqcO9M4UPSe5u\n6Z9//32avXKWv/OeBzsloUdP3/14Qnj88KhrvG+cuvrJLbRz5w+RuMHJqChSFWmjVK0S4svk8yhZ\nyYaPmW8XbayAT+he2cnGPBKFwuYwdv+5xRnPTNUPBD9A/aLcyqz12crLy8svbre4Yh+2D1aU332b\nSi0/g/dfHWmw/fCHY0WGR/NCoxeMdBndpDLyVrtMC6ec9sVWAmOfYOdmyKiXD7UHBcK4Oi9AkEaX\nKviGFsV+mqLbMDYWm8JOsYioJxvzlITIbME5MzU/3zB85IjR6DXicPIzq+mdz9Mfuv8RxohqXiKm\nEVUyCx5xIbULfqZqxsUXmxbXvTvDeCf4jmJXy2Px+cj+vmYNHobLYBiGYcKi339PofCeq+ePHpUU\n4J7zbN8j7Kwsqj9z5ch/5nV6Cyed4wORECHkdDqdUsc94eo/LoQs90Qily5durR6dflqIgJGgCnN\nlD5AKEoM++YRUhihiBweRsvilVu33rvVb2jOX7N5cvNCj+xiXbS47l8I47pfcu+u0PbqvUO04drb\nm1dLl9zxfXOHpg4UVRufEu4vk0Fr8vO9M6mZ1pQjFbKHQolEInFeff78au6uXa6agk3954S1pSWH\n3+XT4YLx49A5hoxbULKpqameVV9/bvbcOYd9zmkHqPPnJifOMZlMZsHD4n+ts7PPmogAECqa1Tbr\n7w41gR21erV0G6IC98nkjg7+WwUTFP/spVMwgQAAIABJREFU+OsSQj65AJ2gIN0oNSZfILhiR8NM\no6f5kZjE80f76G+LpOg6PFZMQfJPv2Ax3H1CehWplVIBSOLjF+vAticsY6l+Ocqe5JCIZYVCJSUw\nVs7M9zczlIQ335xXXtZ6YizPmnYK5fhQdIyez23Fslh5JjcqEk1OW0oKOKVXx3vfZQs2VKAQECUV\njgQCsMqOSBmWsFY2e8l/fpBGZtfAKVwsFLM5YGQCwhRvlgwtnrpKRAhK8UQuAYHA0BAQIoJARshw\nwI2BkigiAgkhAUihU8moFgFSFVg8G+VL6oMwlIrd7MfOF4E0d/iqCANpLpPOxM8Wxz6LiXs291qu\n7DIcDgO32w1CoVBGcIAgCEQiEaDRaDIZZtPT06Curg5UVlYCoVAIKBRKhvtlZ5h9WfjX9fhJbrZY\ndmAym2/lcq5c0Wy5QGYu50okEsDn8wG9Xg8WFhbA3NwcUKlUwOVygXg8fg2fSvOvSCRyzbYRCMQ1\nnq+5Ilz2NpPJZKYaIBQKAb/fD1gsFmCxWIBGowECgXBNpcdXAV9HX7YVrGAFK7gRbhmPskAg8PLN\n3ocvGulOh9mdlD5rx500eUoLKQBcG/EzGAzgwIEDAIbhTEkAAP9b5mc2m8H4+Di4dOkSwGKxQCKR\nZMo2swlL7va+DPgs5Gw54Ws5wpb+nj7u3GWWI2zp66ZWq8G+ffvAwYMHgVwuB8FgMHN+U6kUQKFQ\ngMViAYfDAahUKqDT6Z+KVud6RKSJXu5ndrmBx+MBTqcTBINBgMViAYlE+kqRtWyQyeRb3ivjV7/6\nT3Zp1Y6iJAKPgtEQVELavIlRWAQhgvhEAuE35YkqS8KeUAomlfJu2zbckecRlMd59jCeScEFPFQi\nidWMby64p4NV4Oa7ojiVlLN5lc01F4Zho/yRvU0vojlRXzDGRMcQkgK/a3yGiCbwKu/xSQol/FLM\nkr0KV+RIGIz4+SScdLFEpISYXSpVznXb1UFeyBdVwGgkG8rj5qm9Ibg/QZ8uaNnoKA76Hqnwlcqk\nhYVYcwrSy6NLG5u4m3nIPi2PZp07MNBhwG6mUEV1IfP4m8DncuWVcLl2u92uGbh8uaic+/LGXtzV\nJdvQULK5uZlkRAbvmt+zZzw+c7qhsqwoGezCBKRCpqoXZaJdsrh0Ewv7p6ampgh8EcJsDpjz/SX+\nqofAdzS/gd8MwVEjmotGM5k7dhwKcIyTn5z57wQd4UIRMMjW1tbW41eOH7fvmdsQPuc+VQ86QUjf\nhI4smsdC3P7QIiXBieUlZxgMEQwKyvInlHLHS6cbHlLFipb2S+bmOOFweHVddR3/sac2n/zuhSfN\n5og51IzPZ3mNARSahhKLqeL7Ro1rrsim3k2RACjgYMo8gwSm/aeoAsuZmQ9tNpcN0pA05J2inUvT\nMwyLxnzinnv492CLokVLE5GJyajdHprSJ+4r3b5TVZpUMcVnGNToBvzkOFn6DoVOwadcE5hYLLZt\nG2/bQu14qk8z8W8+n8+n0+l03ArGqsAAyXCu98QJmtTtTRAL8yu5wXy9J6TDUg3U4i3PbVfPH3kH\nx3PwmJgYvute2p4dH/gmFnoQkqGSWR4qgHq99HZG6fE/Xf3TQ5SXXjpr7f1vv9/vtMUsSxCODIle\n3PjinGZ2BheQB9BoAbq0TSicIk0QlHBqyISoETNtRm+/XHmR4nW5njeff35mPZo4N+G46HSGHaiq\n9qrNJG09ll+Lrb4AoAvx7oN0koI0DcfVGwsKChr99BpLX/R4X7y0uNcVNZc4GmPKuX5pBQ0zO1i1\nuUrE4XI5porSrV2vsy645/7BE6M7EHYsQhkaj7gFhhozp3AUp/FNMPKT0QACApFOeL1xjj3v26xL\nigkqjADLt3v8FRSbPhg2BT+exfvqeX7M6TkhzxpSKt1KOMxkkpjFZXSem+yPAd2OTsnuyqqp23v6\nnb9HILhsAo+FhyE/kUdaulKzbk2relwVj+FcxqoiThwdDFkrNjJuwxNWoVNJFivi8wSbG2/f5YuM\nLOBY7Pim9Z4HFPqCJTrEQghE9SUBmz1FZgrw9WXhDSGoxEXElxQR0CRca7Fv4/2PPXP2Zj97Pi8O\nHjz4cl1dHRAIBDd7Vz43sgUyHA6XCUxmlz3+NetarnxPrVZnPLJyM5ZgGAbRaBS4XC5gNBqB0WgE\nDocDRCKRjCCZ5nPZ3CN3u//XWE4Qu17pZDa/yv6ebox0o3mux7nSAplGowFjY2NgYGAAjI2NgaWl\nJeDxeDICWHrI9vBN86Ncbp3Nq240pI8/7WWW9jCLx+MAAPA33TtfZphMJjA5OQnuv//+W553AbDi\nUbaCFXzd8LU28/+qCWVoNBoQCIRPmfP/rWQtTcTSpGF0dBR89NFHgMlkXpPKnk0UkskkcDgc4MKF\nC+DixYuZ0r5sopYmMNnbu1mk4EY+F7n7m5v6n/6dO305ApdL2NLbThNtr9cL3n//ffDaa6+BmZkZ\nEAqFMuc0fZ7SEfi0aJZMJgGXy70mUpxdVpsWybK9ytLjswlb+nvaC8VoNIJEIgHIZDLAYDBfObHs\nqyCUvfrLfxfjKaB81TZ+SWnd6hKTbz4o6z34HhpCFSRiKWpZtUH4g3+8/cWenuneRZlfaQ+VpNC4\nDnxtx/oaFq2YWQwPbVzyK9UWO24Sx/FVVTW2l0fdA4cBpbp8Yc4/g4jUcp26menNVdqO8tthDDn6\ndKHZyHSaByJBCt9Kmpdbf+JNwdKn9tQ/uBSC/C01vC0WR2R47RPrH1hbaFjXuX5NpXIpjlm0+DV4\nX3Q4RVhHuq/Tnh90FyiPCYrZxMnx8QXO9HSjSCSSNOoEd5Zv4k5Slk7pcXmtLK5v5ntb73oFPsSK\ndkEaTfVd1Zv6xnCneaUfeDlmJGeod+iIRymTjcLnz3N37drlmfjTRDsXW0L/Zaelf2miq8c7e4yS\nx+WWtK1+ra6+DkcModHQQwX3vP/E4b14Fh7Pr+TxnUkmo7/7jTdoVAr2p9Knn9Zi7rhDR3HTgyr5\nVQiSQe7j8JEQm81Wjo6O+hLyUR/T59uKfGxrfMfEXcKieonoh8J5Tc/8mMz/yYnLz9YR9PqurvVC\ndum6Au86weLs9tdeg96LwJOTq195/hXH+xffX7M+uC7InecOnQmc+dik+ri0srS035Mq5y9gNU7s\nmfGt79xLSexe7CAVryE3LFVX6+UW2Zs/GX5IPuSSxw3ReLfhUSrslslw0WhU6bfL8lsq8q3VfXWW\nPwjOOuMpl9x5+J31d5RzfRfr6qRVSGQ3Q4j3fqz/eG5ubi6/du/ex0nrNsz6VP3bH2jbHrVt/hWj\nU+QVswDwr9vSkMefqVcOEi/eTZ/a8dqxDniCDruZuBJapQnVnxCwEozvqVsfkCPChSMtBVdPDp3Y\n+Z0Hd73KWr36Had4VUG5twBbR8Z2acY1Ddz13O63PnwrFqPFXDVtm+lTdmNrpIoUtxxtKQme4nSN\nun4lporFefd3bDhSKp8Z/cD3H319fX07NuzYkWyM1B49Z/uwLVFb++bU+6+0mAmb62fv7hyOXD68\nNDs6a6eS7BPhiYm2O8NFHuvRScIlinDDwxWUjepgCRT3TyL2MTYxhscG3T+7il/ScSY8xFAogPF6\n73zhkb31ME01PoWe2lhcIkLwBXxf2dWyknnaXP/V0x+9VFH/B0bLAG7pJAGHlyyhf3737D2uvEdc\nZc0N7QJscSGtJiUmA06w7Y7Z7QHZBoBsmo46FlLzC3OqIX/kCcAT5zF/cpvrR5Mm9GWX26jjYNe2\nLM5YLBxeg0RcGvUWVG54QWGbctU6NZsXIPcMkcqObWhqvANCj3gtvkKHSdnXZ9Aj7CVVvKrnn6t6\nYm5menbeOHgKgqIEqyNhKay+fZ2EJxtlc2YCcisIffupb43e7GfP58VXRShLm/WnfcjSpZZ/C5/J\n9RBL84S0/cLExARwOBwAgP9tBJTNnWAYBpFIJONjZjKZMh2v09l76cyn5awe/q/4V27W2PWywa4X\niLzekCuW3YhzBYNBoNVqweTkJOjv7wdDQ0NAoVAAp9OZEatyhap0Vh4A/9sQC4fDfYpf3Uggux4f\nT3ctD4fDIBqNZmw0cv3sblWsCGUrWMEKbmWsCGW3MNJkCYIggMPhAIFAADgc7rp+GJ8V6WXTJAOC\nIBAIBMCRI0fA/Pw8IJFIIBaLXeN9lU300p5kJpMJnDlzBpjNZsBkMjOlrsuRmOW8O/5eyCZNueWT\nudljaRP+7Ky4XIKWFsayx+WSvOX8yAAAIBAIgO7ubvCb3/wGdHd3Z/wu0uQ3nb2XLUzicDiAx+OB\n3W4HAoHgmmuefe2Xi3TmimXpko/s+VOpFHC5XMDr9QI0Gg2IROJXJroJwFdDKDt4cHGHRjd81aDB\nxMcun71iVPT1VJTev62Cb92IYNSa9Vqcu2tgrpuPay9IxVH+RAy4HKGeQd2ixa2dueIxA+apjUWB\n++yhMgMqnp+anZBNCsqh7z3xHKEz395We0b+1r9G4p6IUh/BDAyFuzBMNsmyODRUUddeGceVe+IB\ne23r6iYg6+7/AJXX2Tp0GHaYTHPygK2CM6o5cDkcoc2oUxJ8WfC2p5TW8WkG3FiW0JrJA4Pdf7rX\nW1yswONwpqvypdQWqAVwioUe2uV8c2FVkXt2/L8m5HJtUDpNOw6HBppKi2r1Nr1V3X3sAJy3oW4n\nd9tDCUHCUvyA/ykJAY8Sj9GpFguWVFx0L20+PD//8C+eePzIsQ8+4K/Ztk2S3F2xBXOB/W7vyDuk\nZvMWeBYe/yn6lZ+fB1e722vGCve0/r97Hzh94pGZF6xe0hhemaJ4DH7sTMXAWdO+9vaS9qSgWMAL\nslg6y8JCeXl5+eGxw4c3ah+FEUj0uKEDgpBcr/fJJwufXCdryhtyDQ1pRqScI2OXfn9K5Xr7P7c8\n9hT2zvoaxi/0M4x8WlXogsR8b4y/t+m5b5b39PT0lFIbG6VldEwqyWw5XZjnKnMxttB9Q3qVK3DV\nhZBInkBcWkd4/Q9km0RgseAKhE/tvSJ6rnLv83ojTq9ycThs5qlH3PKq15Uek/LK+Llzieb4m7PH\nus4VcMfyJUydT9unPedyuVxvVrz5Zk+dXCdD+zF+5dxQ1+X+LmXx4szM/hP7kvF4fPaoDbsGyYvs\nGt72radGP7yHQ1tYaGKRCRzUQBR5eLW0VDh5p9harP7FkOwXS/8ee7CMXGUdGT43vGV4KHicITt0\n2WkdmbXkt1oC4g6g6Nv3ne97vq+65FNWste8UMb18wnz5tSrZz65Uw6JR/PxeLwj4HCQgibJxp4U\nPVpVFA3VNtSaJ4aH68gG8oItP+qN9s9LSiokhCYK5KIogzDe47YvmHFCRFHx6pY1dW5rQLXgEmM9\n0aHjWMdze0aKYvPFDghCk61zZ1l9ZymYmg5dbyVLWucOi1etX0WAF5IDXYquqvWd+2YPj/zRZpqf\nh6BmSChv74RWV0mV84yF3vOhQTMrojZ4sVOjfsHERPcbHoveeWFsfGxIrk8sxOxa9T8Od/ws3jlM\n7xsiXN5Ssm0vGkpAfb3//jOmaMuWN0/M/Nlpm9TwIpu2In14EXsDcD/6MO3hU2OIRdOoQcsgr2KF\nwAYvuYSXQMg5nItjb70ZcnQyQjaFH4HHwCxSRQECLiJ0dStUjiAwrhE/vdfLtKuYiHrh9PgbZ7U2\nDMriJDICejjwwx99c/pmP3s+L25loSz9nkyLT2mT/mx7i8/Du3Jht9vB6OgomJ6eBsFg8FNlfrnL\npYUgi8UC9Ho9MJvNmQ6ZaVEPgGs7ZKaXu9F+fBG4Ht9aLkv/Lwljywlo18siSyMSiQCj0QhkMhkY\nGBgAg4ODYHZ2FthsNpBIJK45/tzrCEEQwGKxme94PD5jxJ/NgbOz9j9LZlkaaR6ZFswSiUTmPrvV\nSxdXhLIVrGAFtzJWhLJbGOksIyKRCAgEwhcagcoWy5BIJNBqtWD//v0gGAwCDAbzqc5B2S/y7HT1\nZDIJJicnQXd3N/D5fIBMJgMcDpcxpc0lbNnr+qLJQa5AtlwUcjmh60ZDWiTLzSLLbj++nAea1+sF\nIyMjYN++feCDDz4AHo/nU2n82cQrHc1MJpMAjUYDOp0OjEYjYDKZgE6nf4qoZWeTZQuR1yNqy0U7\nw+FwhmQTCIQMyb7V8VUQyr7z3T1ePD5PFPQq9Imo144kkGiRRDw6rZB94PHZY0GP3hN0OgNG94KJ\nzRBJLK4+OYVcXBhLOs2RmEMHR7AsW7jV6wmoLWQBi+Kxz6nipsro2WPjR80hjdkX8BvF0qY6KpUv\niNp8djYl2IAqKoEU+mmFiFlY4PHKVYFF+zSncs+eoFepDlgsQapgY2ORoKvLGcBKBE1t5ZvKA/eo\nTAuq3e27Ci7Pv/OOF5PAVJVwGG5C3Dcz7QgWNnc03rk9f43mg77JuisUF8YBD9i1SiXuxRef2+pp\nQGL4QmHJJI/Rubl9zcRMb9eLz5U8NmYNnoYgDNQq3MFxKmCHuoIJaiJ8dD/WZGpqKCqSTw8P5wcE\ngjXms4XK6rhjbN71MZ/P5xujNF8V9462cm+IEa1GBq9edV4NoSMhKH+Lf2Ep2ONFj+shE5MeLCxe\n08GXJMx2r7mOz+fnjdLp7W3idtqdvE0j8x75iPL8+arCqqq4R+EIheCQeZJsHqMny1A+3+yudaVN\njoTfUfHsN55dQBtVnpGhobLqsrL+2PwwX9rZKSeeR19dXCiT8goV9ApQF66150eWnCc6BAJBgKMe\nSJY5GGbd7BwNSUiSFjegf91x9oBjibiBuhEpGlEKu9WdV8RIK0vRWlgoFpRB5vz8ynyeECPksApZ\nv7z0RMtJ3ODv2HC5C0MJYSw+t4Wy5bmXejf+t19UDpXnw/kOmrBI+ABhxw5PCYTexXjxuyS8zy0o\nQ/iQfDryh8zJ8w83x5v5/EJ+PpfEpYeQZYpGCs4o979vJBrjsRjW/ZjoDkl1faLaPlzOHIYnj/KF\n9fWi1tLqWivKTjbru+99xnMvMoRiOYBE10SrS8gm9Bf36y+9m5+fn99aXFzM4XA4paI776y5f23p\n0aNT74w1N7/gQRkSuOrI3ViVpIeHRyC+JVv6lhT9ndbRVVDhGvwiOUTYhHwswdv2RgfaL7Vz6iyB\npUFcwGqOBgIBtfPYsV01RZtCRI2ZNJgg0e+oLZg0hkZanmduizdvKC8+auX2mg0zeIjgvbeTTBmz\nbCzzhSA2p6hKanaf+MhrGTULsaaO4sbNVUES2797y/bb4cQ9e7TapX6Ea6ovwS0So3x67M6O/BeP\nMlq6T4+O/ieXyuWGMGjmkk7h4DACDpPX4UYEbA6RRFzGILaI5s0f/xmRmEP8+YOpIzGXw00na6Vc\nRnP+jPZiT9SPAmrbhRE0jOch0RxWEuN2uF1TBgKLylGpBmRe16I8EDAYbS5TwKLq7kdjpJIECKJT\nAU8gFre5kRhS6oc/eFJ5s589nxe3slCWLmfEYrGZLLIvUsjIFb1UKhUYGhoCSqVy2W6N6WVyM5fS\nHqRWqxWYTKZMhlm6AVC2J1p2F8nl1r/cvt0Iy3l0LWdXcaNssXTHyOvxrOzxy2WRZQty4XAYmM1m\nIJfLwdDQEBgZGQHT09PAZDKBWCz2Kd5zPf6Z7jCeLr8kk8mf8n27kUCWfa2Wu27p4Gg8HgfRaDQT\njM7OCLwVsSKUrWAFK7iVsSKU3aJAIpGZFuvZGUVfNNIv/v7+fnDixIlMK+tEIvGp9PRsoSZ7P1Eo\nFAgEAmBkZAQMDg4Ci8UCyGRyxgcrV7RazsMrvf6/Btk+Hsv5YdyIrCUSicyQ+zsd/cuenps9lnsc\n6WPxeDxgcHAQHDx4EBw7dgyo1epMZlf2+cze9/Tv9PVIpVKAwWAAt9sNcDgcYLFYme6i2UJbdnQz\n2+A/+7qmv2ef43QWGwD/W47p9/szpb23ejnAV0Eoe/vjubtouCibKcFICNRmYiyRDIXd6ggezeOT\nCCwejSPlkCl8PoUhhsigsEhcHitm0opZOHKETKK15CVC4TCWUCZCAz8hkUDF0RCKVQS2tlD4gLRl\no6q9rK6uHEaW4CSiDVWhBOzncyoq7D6juobDESxMX1oiS3jlLU0SJCIpEknq+qWFApYipDYaqw15\nec0P0J5Pomjeuf6KFM2fwEds1gEkF4PZJF1bF68tLyWZXav9CWI7LuWcKMHRPOVVcPH7QDFBmMVY\ncQ9Qd4bePP+mybW0JKQmAaEhBUm9BDKKU15nI4V9lxQOBZHBYNgvO8NkH+t2o0d+LGWJx3HUSESO\nBkCIwWCESKHQxU+6EkGn2uWKudofIDwAT7I3VUyOKj7haD+B4TjMonBY3HEu9wPTRx+Y7E88sbWa\niUtyTnhKkUWiiwIKp4m2BAZZeD6pB+f31/jtxNLLUvqpGow2pppqI7e1OTEhZ2VlZaUqCsOyvuOv\n19VUV1etya8aGZkdYQUiASGBQFCVn23v/2Thv4VsNhvHcHlFbjtm27NLrP6L1DMULDNe2F0K8nmx\nfP9k4cYYfXoijwTwTn+J+xAyGqgiYipa4XK2ZnPE1FBkopbMRAWBxtV8W7f+Yh7LzMuT7srr6urt\nMmi12ra2pbaR7Wuamv0NxXaydmHRHFoEIA4iyp6+lHCT8PajUThoW1AtLerHqO18fr9Na7t9K6bJ\npXDQXPHAtF6v17fWdHQg/XjH2NjsGFy3eVMYLwpCvqlZVireRkdgPGNLmrF8iTTfqAiutjtY0coW\nJjpPD1FYohiqJE7diBmVRWbv4lVeNtdOCikJhs7hmkNxUSgsFovN89x112NVprvO9tQRxspUs2zG\nIENg3CYRLul6w9L6sjwUekqSpGJBO7G5y4HYjywh0Qlh91i8XEWjQnlRw+mrfgFZolMvTF/GY/D4\nqcWpKTId+WNUEtXfgDK3TGEQhoWaOH72VP+x5/PE346GilQqmz7aM7igJFf63QIChbBvX+8+NhUr\nohKAnUQNspDhgOGHBcyXPkHwjnqNfEkQaZgdkc/PtdK6p3HVEC8wY1ey+VLx+iYplVTWwIyYsQle\no0QQsDlgl+vqZAEnnBfBdrSTOFIkjzwrYhfslAjykZSyFh2lUNpebbcj9QgkIhyDROgU4nLIE9Fr\nkNhCEpSCY0xBOVeaHy8AeLSvqDzaYHdSF9n05koqlcPGE2iEUEJvRgAsFIl7klwOp5rNQYt55WRB\n3ItxvfDCE4qb/ez5vPjwww9frq6uBjwe74a2B1+mIZ09n51Flm1v8UUDgUCAaDQKpqamwNDQEDCb\nzZ8qmcyedzmxB4H4ny7XPp8PmM1mYDAYMv5l6WylbP+yXIHpRqb018NywcjPKpDdiHMtN1/2OrK3\nB8D/ZGiFQiFgs9mAQqHIcM/Z2VlgtVpBKBTKzLscn1lOOMNisRmREYPBZIK+uVn8uaJZ7jVaLtM/\nV5xLdzuNx+OZey/dtXQ5UfDLPBgMBiCTyVaEshWsYAW3JL7WQpnH43n5Zr9E/hbClt1ePS1c/T0R\njUbBiRMnwPj4OMDhcJlyRACuFWGul1qeJg1pL67JyUkgl8uB3+8HDAYDkEikDOHJJlW50c7lhLPl\niNtyPhjZ680lXtkllOnP3CE3qnkjH7LlCHYsFgMjIyPg0KFD4OTJk0ChUAAajZaJnGafp/QxpD/T\nhCuV+t825Gw2G2AwGBCJRACXy80QtuWM+7NLLJe7Vtnz5Ual09MjkQjw+/0AhmGAx+MzfnQ3++/h\nbxmoVOotT9jO9Dqf8dnCQTqjhFYkxVWYVbJpGmeDFIPAonApHJLMzcOzxKV8Kq6iQmc6Mb5l95bN\nRhPejqEJMOWlnNWj/e8fpLElzDxSjTgWNxmINCLAQ2KxJ76ggYnYlLhuVa3BxQ6HbVhMfSsovDJq\nGCoqlHCFrGKRVjU74bBKobD3BMIdqMGUu8NEFzq+AJKplL4lcFdtGw59+rR7X54kj0uPnmN/4sb0\nmRandDDEJ5NRJKxNgFX5kgnfwmmFCksa1zYttW+Zc4OlGcXAebJeikY01tbELTrdwtz0XMgf8XsT\niNTaduHeA9rYNEsxO1HK4/H6Ih7bd0zc1fueTHERdaRa/JJfHtHpdOMLCwtbOsN3BFh8zNnF8IzI\nWNzMSaEr5u/nBdp5kVq1lzAZFwgFxYNMpjZhRxAK0TGGJB5fUPb3M+kNdKhK2QD+Y6YQ3uiwLU1U\nJ6VRTEO+WB05sFAxhkMY5ztAR8cFx4UL4Wg4LJfL5YKylsehpTG5sH3NwwJDGUbcwudrNBoNkRgk\nlkdifHzqoZJh+cmTeDwRP25x9FHdDwNA8kImk8uILkajhxYVQ+fI1e3bPMB3Zl59xpBMJpMNDQ2t\n61XsGI5kVtlmbLTesioBfZEyzzIYLr8++Dof0/jArL7ntEKhV8RiUCwUCoRUw5pD3RvG+IGNG3dW\nO4AZgcAhiovzissHsYhhFpzgbaivY5uwiUuDXWeNwbwOm4csmp3reRmHRqNhGIZxWq93ybK0xOFQ\nOWuLAxWaKV1P2B11WztSJYIef3Fl+xY6jNDDHlmFne6MOYjmEyWI6lo3TIRYY45+Z15SxNNaEQf9\nFc0V0cN8XP46Hd2pvaqN1nnr0HBiPJyf9A7NIlhSIxLjM1w+3T0/cHznU5bbU/apaa8FlxScI9TM\nJQqwnQ3+ehPyvko+ycoOWG2T588vnvc8sfOlVGOMjL0SX0pJa3dWbDKLXT5mVaK2KcwX2lP6SXgi\nWVIqaVxVukr259b8APmN/IWrqP6Ym2ynis1Mup7KUntMMpdOE+1YF1vLIpFE/rxavHW8GaNQ2/Bh\nItMvzSeRCriYTpIe8jc8ZNiO92zLDyYx4YhXozHrQg6V0+fzqg0Rtdas4FLF3MpSYgsKuQ4/NTs8\n2L6GtV5jzTNSGOXi4vpY9fglgdIXMLvoVBqXAXQkPL4DHQeoIB5UFPsCp2drO1q3kdEdbG/Q67jj\nnrW7L58ZOpdf0lQl5rXWRXwhbyIp8gsuAAAgAElEQVTq8VIIpVIitYxLIg5F8osLJCm4CrI4nZof\nfPfxWz6jbP/+/S+XlZUBBoMBYrHYl35Il8FhMJiMvcXf03A9vU6v1wsGBwfB2NgY8Hg8AIBPZ/Bf\nTyDLHdLm9enumWkemXsMaT7zl/y+lnuv5opf2TxrOe60HM/6S7zrLwUkk8lkJhNerVaDmZkZMDU1\nBaanp4FKpQLhcDjDG1Op1A3LIXPPLxKJzJRfpn3K0oHD62WUfZYSzOtdy3R2WTwez3DuVOp/OqHG\nYrFM1tmXfdDr9UAmk4EHH3zwluddAKwIZStYwdcNfw+hDPVFr/DvBYvFAgC4fqr5X8IXQZL+mm1D\nEARIJBLgcrkAj8f/n3gXQBAEPB4PmJycvEa0SpOANFFJd8FcziQ2PX8ymQQYDAbE43Fgs9nA1atX\ngcfjAZ2dnaCmpgZQqdRM2n3a5yxNQrIzrnLJBwDLn8dsIpX7O/sz/T3Xcyw97nollblkLfcz3fFz\nZmYGHD9+HOj1ehAOh4HP58ucs/Q8uZlk6ehhely2IJlIJIDb7QYMBgMoFAoQCoUy5ZepVCpj+p8+\n5vR1ys4WS+9nbjQ1m3CmxbL072AwCNRqNfD7/YDH4wEEAgFisdjfdF/9rX9zAHz+vzuhUPi5lv8y\nYLG/q8ts7ZvX67GIOXQYGwkHvV7vFUcy5guikQSubWEhjtKSiYgEHhcMLflP/HnogFk9qUSTGAgN\nnqZBIgmwRXdpwp68LMeg8WQkGovUJqau4nB+t/USdnRxVqEweQxaEorGmlX5UFG3y9R/2mqco1BF\nkRgCH/bYbR4k3oH2nHcZyKhRJIyNWh3TbrIS80v1KX6d1q8Z86kQAp9e8RGiblVVjGH3X+j/eIAg\n51KQaIhMxIrQCBSTPj5qTbLv41thP69iI77i6Yv2K4Z/vPe79/30/NWx9uqqTRC9jJSQELm/fXf/\ne63rDaREw85GhS/kQ9hR1KfRx54h/ls8/nzq6Y/eXO2uAb7eXrPH67WRHoCM41M2FlvGJOzw8I50\nw0Oxb189MBO9664u9LlJQhOBMBJZWgoXjLclB8EgW2g00vF4fH9be1vJW+E5qTVYcqa6shp/ZWQQ\nnY+cPTZoPuZU9al8aDSaVcViPdy49uFpg3/aZDKZ2ot/oz8xV47Domdm3vG948RfrF68vaXo9teP\nffD63S/9pLlC6U8tsNlsp9PpLC1Nls4JUXdXztUdRa0aWltA3M6kcBrvT73R/Rb6af3d5fUbK3zj\ninHC6dOnxzkcTnHSW9xQWUTwCWzHRuEx2IaPPZT3MP3Xfz5z4ZcIs9cskUgkkUgkYiqsLTQMjw/H\nZOvAalnfPn+qjDcycvYsDofD3Xbbbbdhrm6scVYgosKaKCusBWAnufrey8ljP/tG086dr+1/7bU9\ne9buWWt+9FtTP08ltv3xZHBmST5jKysqa0/kJeyL74z17jy6lyP/w8I3N0PfPOSWHdqyw/TgK2fD\nSfRE1xspdBBtpMQpRpE0jxjv3px83/Qn0nznY90DzKL4ml2oWhveFpwNNv+56QhMoG9GJoJreDB9\n4SUoSHhd+yZPmag6b3fY3fbREseE02m9j6JZkPLpP4qcP5U4z12dzONJcTtP/eGjB4Vv//FfhYRf\nmMgOsyk4QB54pOqF1kB0es+RLsWH6xvufXps8rBO5qAYx+2Lk7++8+V/6Nad4JRWFvEUcCmqqo3C\n1g4PxDEQ1n7ybOxyMjpIC8cVHrdXf0xQXfJoII5bMJ09MUjj7ojb5y6fp8z4T2Gj2Aqjc/SKSLxr\no8uuVEYQ/gSOQOkIOBfm9cCJtWngLl/4NwtMUl3J5XPx7lRMk9KMTannxhkU9dTBfiKVlG82B6aJ\nEJYeSVwIRKJOBw5rD6WSZOzYpd7LIDKN9vhlQ4jYI+SQZ2lhaeI8XgPhpmJRpz8aMxlBSu9Foclk\nLxIgjQaFFpF04f0xreZmP3e+CKS9mILB4M3elb+ItB8VCoUCeDweYLHY/xPehUAggNvtBmazGXi9\n3mve1dmcarnl0u/27HHpeWP/v737Dm+rvPsG/tPR3luWvOS990ri7L3IYu+UUaBAgFKghb4PDU+f\n0tLBKLQFQtoSRsLMXmQ4w07i2E7seO8hybZk7a2j9f7BdavHwuF537dtAs97f67Lly1bls45cnR+\n+d6/+z4kCRaLBfR6PYhEIgiHw+ByuUAikYBIJJo2+DpTd9TVAiUkfpByprqI+nmmpS5m+tlMA5DU\n+ggFZDabDaampmJrtBkMBrBareB2u2MDkmjQcKZjh2oo6vfQcwQCgdjr7/f7we/3X/U4U4/HTN+b\n6TY1vKOGYi6XK3aFTIlEEpsCSq1tv8v8fv+0NeAwDMP+f/e9CcqoJ7n/F9fqJIWCEpFIBCqVCkQi\n0TWZ/oZO2Hq9HiYmJmIFYvy0QFQIoHXHACAWtKATfyQSiV1BMRwOx0IiJpMJk5OTYDabITk5GfLz\n80EikcQKIlR8xLe3U7uv0DGKD81mKtpQsXS1Yu3bbs80khpfsKGAzOv1QkdHB9TX18euIsnhcCAS\n+XrNNzRSiB6TWrzFHzdqUIaOndVqBZlMBgAAVqsVEhISgEajTVvLIj5IpHarxY8kxwecCHo9CYKA\nYDAIBEHA1NQUhMNh0Gg0wGQywe/3T/s7+Hf7PhSH/24mc6suGLC6owE2myTIEJ2gC4Mha5hGi3oD\nYe8UM0xLIAMuHyNEC4TBydD11LVGIqFgiLRCgC3vYQRZCRGC9IbDBCMa9tpIn8vD5sh9JovJQGfJ\nue7Bsz18STLH5Gn1KvyCiM8xZWVzw0kOrzMgEOVwHc62y3mCVWV6c0u/iFVdYnEP9RcVVcwye/Y4\nvf40R3KhZmUAhC4QqmgSMJutI+ohCI26GW4fN0REfJbUqIo56weVY42tf/lsiGH3tNXVXTR0WPzk\nkO7Jxnl9NH66+0gb36hOmXS7sseqqrKSPYcbylr4pWOVgZahY1VprqKIZEltSrHd9NBL/3XLZs2t\nT9/9061/0v18UPGzt56qcfgTy2atLpVW9/AW3STvJbf9ZEy997W9r8noZWWexsOHHV6RiCbJOpqs\ndqv5a2+8sXgyq1zQ1HepTdTaetsPH330vZXPbc18ZtXiE/v371eul9+37I4nVjT/8NAPDacNhksG\n46X09PT0aDQa/fz0y1VpZYNje3bs3mGzeW1+xawn6ommgz/ZtGVT57svCEy8lS1cLpfLyM/PP5qz\ncXO5f3XvH3Z+tfyOhZ+/f1Hyd7PZZrOl1/Sk1vRE0t/+0HBzZmZmZooqJaWDtnnzvPMMhpRxf+Sj\nzbMyZR8ueL7r8aa7Ckx5BZZKn1RjilbnJtSUsG9uk0ho9oKhptrRR+pZNW9WcRqFLrNZoVAoakO1\ntT3ujxdL8uWdHYfrjh+bmJjQ6XS6laea/yi7cYXee0fqhsdEjz3WMshmNwd+W1/7y8mid2768Vi5\nMdHj/rs85Y+zGxpCu8hdaWmPnRVnZma+c7KwNWV5yfLX3nn3l+Xnlha8B+9diEQikVWrVq1qtet1\n4Z7RL3lhRm7ps8cnBfY5PcvNSUnhjlbuz9t3PFP9aXV1L2+AVzTMLbb2Wt6kSbnJhxeEw3NcUWYN\n80r6x+c9H8+fP5/91M7B1oKcDNXyFcuXC0Xh8GfWPT09xvcOrHn8oWWyBT96YrSnp53B8JS/5H9r\nin228jy/zJX6EfFHRvSSt3MoWB6SBqzw4G2LboKQJNABESe3dOXsbc7y8qDLCKwFt88xTVxxg2O8\nSwgggoiPEIwKOxTaP+WZhZtIrzBfnVzVtNymy+hny68ItcLc+QzSGxBxCQnDLqJnqeYyOtimTCKi\nYflAbwaah2GytncIQvwET9RE8iISbn/HsXYOkVditTa3MZl8jiNsN4dDYUuUweKFAhabL2QeDVrI\nSITsNgfDPt+V1rc+CEcCQaejo4Ogc6QQjQaiIVJMp4ddvoBtPEgwBJEoARAx+cK0MO96v+/8K6Da\n4bv+Po4ukiQSiUAsFgOPx7tmC6uHw2EwmUxgMpnA7/dP6xCnnqtnqnHQ/WYKfFDNxWAwYssqBAIB\ncLvdIBKJYh98Pj+2xiz1dYrvXqOaqe6K70K72gBj/CL8V+sYi9+nYDAIbrcbrFYrTE1NgclkAqPR\nCHa7HVwuV2xNNtQ99237Q/0edV+QUCgUuypmIBAAj8cDoVAo1mWGfve/+/ug1mQoAI3/HtoGVEdT\nQzmxWAxcLvd78+/ou759GIZh19r3Jij7rkPhD4PBAKlUCgkJCdf8CoThcBj6+/vB7XYDk8mMhTrU\nk3l8kQYAsUKMOrUPYPpC/2w2G/h8fmwU02w2Q0NDA2g0GtBqtSAWi2Ojm9QCBx2X+ONALXDQ/WYq\n1tB+Xa2jbKZus5kKPgQVpcFgEJxOJwwNDUFHRwcYjUYAABCJRODxeGJhIToWqAiibj/1+2hbqdMn\n0H653e5Y+Gaz2YAkyVjBhjr3/k/+TtDrSN0P1B1Ifd3Q/VD3G7r4AOpuRGEZdm1EgjY3weRLo2HS\nAwSbQ4a9JmAwAYBGEASTiAKQHEIg8EfMw8wIP5PBJZjhMBGBaIjOYsu5geDoOBEh5Aw6+CEa4tDp\ntDBEWGwWlyYDrjop6jaPhaMRPoMkHDafjROJuBlOZ3CKwZNIrR6TXyqSpBusRjPwFYUma48ryvEl\njQxbTWYX+0x6TsJCy+DYOWWyI9vuDIzPUQgyjwbHiIXrbr3n0plP65MS83JoxdkpFbUW0ZdNXkd5\ndGCglxgZUVZV5U30eiLytCXVklVTeeOtS/p8ERspKZykn3/75E5mxo2Z0ebxOlVmduWg64J8bpXe\n3MMh04oKsuREQ7fsXf/ud0UbImUwVrL8tnlL7hRUfuXbuz38eLq0Yr3lYnoPSdaRix9bcFfoCpvd\ndPny5XGpVHpr8Zo1Ew1TfOX84MWpqdEpMa2edtjZ9EUgS+LI9n2Sw0694XeBEfMH7q1t50ZbWlrk\nixcv3jDnySdbL+7ZYzAYDDLfm88QKTcty8/Pz59wOp3aykAg4cqrr77P3bPH2BK5q7R0vHSdsWzT\nPom+oWL0jf8sndhxr7Tozq7OgLozPH42nAOrc+5Wjvxgd8Ydn1ZEBioEAp6ApclM1zS9+mrkuXkr\n2vRV+p7DikBNyBjKz8/Pn33/7Nljn3766dxXfvpQ92f7dhZ1zynq6ur5wkzqzb8tFP02QZSQINUG\npFF9NFoXrqurJTYGRrytQoE8a7OWxdppNpvNbxKBNx/1r9itsXX3NZj1rLy2Rquzdt7o7jm38MYD\nF5jqLp6xtiavKjA2tvPNk6+ffHhn5865eXl5Bz/++GOWeThxduVz7/iq695b079mTfGGORuiPqFv\n8q/b/rruyVV7T51QNZYYzwi/+GzXW+ecTufPfvbIz7RDa9bcvmBi6UW9+mJEZTTmQPlNzctYa2kf\nDf00o+DptSbn0NAP7g6/rVixu9Cf8NwB71RLm9liNn/gSeEm22ZnzhbdXLHsx9nH/LqDTa7eBK4i\nW5pM38fqsKfQRROD+j2skCqVKXdpstOX5KZk/lLd+/lcJ9tNEBGZzCXxcTiFrFNZnzGYqns3Pnz/\n2fPvnnV3u72OcYN7ze2P3lL/6ftHVgjn54+O+qwlsrOs5u7u1rSsp6uGukYvgWjIFY1OcOkRwuMT\nsDljDrODzg7y7SEnLey1jgb9TiuH4YUpBzHJ5SWrfLSAgkHnC308u4PtAXk4ErbS6TRhJBJycelq\npTdoMvGZ6kwak8+PQiRCC3HFXA5HYHcZr/DostQQQTq59OQ0H80YAmDImFHSH466TDQaQaMTTEWU\nHsFtGdcI6iSTSCSxkOzfNdVyJl6vFwwGA0xNTcU67OPDm5lCsvhaLL5mQutr8Xi82DRSAACn0wle\nrxecTifw+XwQCASx2gx1UV2t1kPifzZTyHW18Gum+mqmQcho9B+L3TscDrDZbGC1WsFisYDVao2F\nY+FwOBYKhkKhaYO88b5tZgK11qQGW5HI12ugBQIB4PP537j/TBdHin9c9NxXgx4LBYkoFESPzeFw\nps3qwDAMw74fcFD2L4BOfCgkU6vVsVGka8ntdkNXVxf4/f5YsUG9YhLaHmrnEfoaTZekdkWhQiO+\nYEPhWyQSgcnJSZicnAS5XA4ymQzkcjmIRKLYVRepVzuK726bqbBCt+OLsZkKtqsVamifqAVnNBoF\nv98PdrsdxsfHQa/Xg16vB4vFMm09C+qo5kyoHWDx+4NGkFF4hm6jMEskEoHNZgOPxxO7OAL1ceI7\n8eKfl3r/+G2hQgEa2pZgMBhbNwWtkRYIBHDBdo3Q6QxOJBoGDlemDAbIIERCHKCzgEGjC4PBwBSD\nyaCFwOsHWoQZ4dA9IZqXR7AEgmgUCILJkoSB7mPR6CyS9Ab5PLE6EmWOCxIytQ6j2a4SpRJBLlfN\nlyg0BvdoC1+myaQzedwIXUt3O68YRPJUGS3SKQwGohNeS+85rYCr5vA0gkFfhMeQqvKnEgUJwkCK\n1RYNh5kCfuLBE/tPRaNT3IZTe5pyVylW+A1GmzY3L2WCWymsiuavEwtHpaLS3Fz3qNMvVa4vWpa+\nsOboUENvulhTGkrqblSfnjq9V+WXPLo2v6iv0dvYZwx4QovT1GNNnFPE2ln3ulxfuD5ly4/RmMNy\n1inG8CTZaWhXqBlLTdyIRijUjJKJEa31glWRlZXFPtVxOMLXp6kVCh3D6EyQzpJKM2lTVa0+35ce\nT9RTOfuB2WxpnzT8wdEPGk4kvlq9MLJQERKJhtva2mpra2sfk6Y9eMl48tK8ef3ziouri094Jjxg\n143l33fffaoPL1xID/B4royevrlsNrs5lZEq3KTYNJlwQM36oiwqFAgE7as/vCLuWzZSIBMUGAwG\nQ0QWibynmLvX1DpsrMjmp1y6MnKpWCASVM3NmPuLX7zyi7y8vLy1s2yz3JU1lQnO9rl5DvvURvOm\nTbqLXVdCoVCIJPXk4ODg4BOKJ54YrRgZ9Xi8nmPHjh8rZBQWlt2Vt3m4sbvFKI/0z2EQ50WBkhJH\nlsOhkWk04+vC7qrxUqVQKmURqwMOnjA5OVjf2SmzTw7LUysrLf6+SwxZZeXpYxePJVaXVVv0Or26\noKBgQWVlpbkpOrCOlv/oR4me3zV8cPQDuVwu9/lIX8bRzcZdZe8OTch5qtR5pfOsra2tAA6QliaV\nSQOC1J72yV72HRW15z6//PGPaTew3k4Wlo7T+/uLLMJSCdfWQtNVVk0kpaSMt+81hTKkOYkSMt16\nLvVSampqKsFwRch0hoinG5/DN80VDmX7uovyTZ62YVZldVoRn2A6g4Xc8966KwpnTlalYrB/yuLl\nWP2i3KwsHjPTxY9eGjVOHfhCIHeqWMXBYpZWJTzw8Vtv06NE8oEWRjNPOSLo827yStTLUgbULEVQ\nRzBYIIjO5QtZbRbdOMOuFROJHK5IKk8XR4ultqAtwE1dWMRytbG8Af4UDWgBuSYt1WEx6xKEpel9\nntFmniwzy+80GCESDHkCRgOTyeT7aG4Xh80Wk2EyxOJzEnykxcTgiNMi4UCESTBEoagtEA77/CwO\nlx0Ns1lE2EswWUI5m8XlOjxTE9f7fedf5VrXMf836HQ6cLlcEIvFse6daxmSRaNRcDqdYDQaweFw\nxLYJnVupa5qiz/EhFjXQQdC5ns1mx+ouLpcLABBbcsLj8YDP5wO73Q4cDmdaoIYuYIDqmPiOdGr9\nhB7z2wKxqwVh8Y+JFrb3+/3gdrvB5XLFQjK73Q5utxv8fn+sQx/VPKhm+rZlOv67wC/+dUfHHk31\n9Pl8EIlEvlHbxc9+mKn2utrzzwQdC5Ikwe12xx6T+vrh2gvDMOz7AQdl/wKok0wmk4FKpbouIVk0\nGgWbzQajo6MQiUSAzWZP62yi3o86JQCFXtR9iZ8yiUbEqAuiomIABWEOhwPMZjMwGAyQSCQgFApB\nLBaDTCYDDocTmxowUyfY1UZZvy0Mu9poLHotAL4ukLxeL1gsFpiYmIhNj7BYLLEwicfjxX4fFazU\ntT6oHWXxRS71mFJDOurUTNTd5XK5QCgUwuTkJNjtdlCpVNOCNupoKPre1dbnoL5W6PVEt+N/Rg3L\nXC4X0Gg0UKlUwGazIRAITOtSw/49/KTHRmexpFHCHwEgIEqjsSXClFKPW99Dj9KFUSICLIIniYYD\nHoLO40nkigzSR1i8PqPNQ46NiKQ8ldsTcLPoHH4gQtqDIY/VC0QwymAkeRkeiAZsERZDKqZx6Eoy\nECSjfo+HJaPLgWSFGISIaxn3dFeUpZf3BoZtBRXVVYM6m16qSFZ4vA3tK+cnVF4anhhQ8blco5wZ\npZud/iiNb0nNrc2rFEn7D5uauWdO+c8XPd6+ksvOlJ+qb78QodkgHCVoaekp0OEPRVcJQ9XvEZ8Z\nVL3C3sV58qdbDd4HvaPd6YIAl0sPqJwpU1kBdrLwXsPeT5KZTCYzKUIYg84EJigcioW3/erFYJnC\n3PPyjh0MIcHQFgBUTPJuPQO0z5qL5HJWXeBEfnl5ee++Eyd2jzezpD3uUwFrIJBRkJHBGeExDiqX\nFMyf3zx/rFy9tlXoVteES8ZXTNkT1JurK9tvddAnVg7lTFxeONYZbjuZzF96D7/A2ROydYl07tbW\n1L65c3XKXR/LBuTynJxFOcI2aOuczDnqUYvUk2NjYzAJk7nlrLvSs3rIhIQirX/Ky0gv6Va9+IHx\n2TurHz3VK5cBzVJUNGDp7k4t/d3vfPa33pIoq5Qie8Te1gi/XbIk9T5/rt53pb7uRElKSUk4LAsv\nWbJkyUdtH31k2WuxaDQazcY5lfdPDXJ6r5gvn5iwm1qqM7mVHB/Hd9l3+XJ+abT0AqTc8iicCR2d\nkB5wuVwuV5ayyGxQr8vimrblrG2+q6VLGZI1DUlXlcw6o864oTjv3LFjkyZ3YgpHUbNv17EjK9Vz\n5nx0ZOCgcotSOT4+Pq5Wq9UqlUo1KnDPSWrxvcFdX7K+RMK7jV24QZu2/f1Z+ZkbPrX6Od7VNIYs\nwzhIbuMMCgZnXyGWfmW076ephDlZMt3QpNtte9//mxWL2+VX1i6eTQZH2E5GhjjiH51KW1D2+OR+\n4u+cLF8Oy+0IhZ1pIga7yS0OMJnre1MqppjmfROBkpLR4U//oimm3zPsttHlckulZWj8ksFh8+xy\nJJwrUuTlTQiFwo467nh63rzMW+Spo28X99zIbGrr9xDjtgRpQoWESBMMeS4O3bhKufnQlZQhNk8m\nixIGT+myFSsa9pw+WJmkmn2+z9shE9KywiHCSyO4Govd2E5nJnIJHlNIsjwcMuAkvEqekiQiPgZw\n2eFAIAh0npzLotEDBJMn44nVbjIUZBIcLpcrZAok7DS70TtIp0eYdAaLGwnRIEoLGWg0mpbBj5AB\nZyQUgiDJJlgS2v+QN1UUdpAkGRv8+q6ghmRoPahrNd0SiUQiYLFYwGQygcfjiV1pcaa65mrnZ9SJ\nhFAHXlFQhmovAJi2PiuaYhgIBMDlcgGdTgcmkwksFusbgRnatpmugH61GmymWgzVfaj2CwaDsXXA\nvF5v7G/G5XLFwjyfzwfBYDC2n2jpD+pgLKq3qHUX9XjE355pOim1nkEXX6LRvl6f1ev1Tgsu/7sB\nyasFZjPdnmkbw+Fw7HnRPqFZCt/FsIwkydhrhmEYhn3texOUxRca3yUEQYBEIrluIRnA18cHrR/G\nZDKByWROC2JQgUDtcKIGMtROqGj067W7SJKcNhrG5XKBx+MBg8GIFT2oYEMdW6FQCKxWK5jN5thz\ncLlcUCqVIJVKQSKRgEAgiBVK6CpB8YvDxhdo1OKIGiKhQAvg66LV7XbH1r8wGAyxy4ujwDASicSO\nTfzxQSEbKtqoXXnxi/jHf44vduM76EwmE0gkEmCz2eByub4x5XKmaZ1oP6nrlcWPtsYXa9Tii3o/\nNMKJimmFQgEsFgsCgcA/+Zf37/NdKyT/XzEJGjNKp4UYBFdGRj0kg+AKg4neoMpeVmSz6vTRINA8\ngclheoSQEmIWx820kGEXXx8CWmBWzQ/uaO34aJQVZHeHaHYXnZaoJqI0KT0YNlWvnL2UFZgbaj77\n8QWrXWcSKQOZNgffyPI4ozShiMtwS3khQUSRdEdyub6vvdflszkukCRDlLikJKSbmJBH07jhQcZg\nRGxSBkyCkDc1wGX4RUHCK5TeeFf+ut0fDu5iWMsFYV3DZfkqemF3svlM0fyV1YWRqanG0aHxLuOI\ny/HQXA7/mCOU3/Y4d4J3VH3skvLwUy/f9szLf/5zV/X9tz+m3p4Xve+rYDi4Syzc+4dDL3RGJGmD\n7eebxY7CKJG7qSA3y3vJ2DGuzRYmLjsM3V+okgniWK4neaA+7bnwPv+BCn3NurClnlU+526Nn+zv\nbyIslgShWDx46dgx6ay1ay++cmrrf23OX5IrX17m8vjNB88fb2gNMUO9Z5M2ETfu+u0m0rtSVMUp\n0DBmJatdEkn5vN1lz9seUy4oy64ODAQCdDqdXldfX89jMBhGo9Hodrvdd82qfiFcZBVt396/XadL\n1Gm1Wm1xsa2YLmhjvzzuDFmtTmtrj/Xtw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ZJdLRyLr8uoP49Gv76wgc/nAwaDEQtBr/f/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\nHJRhGIZhGIZhGIZhGIZhGAAA/G9yFFqAEtcAawAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy import signal\n",
"\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"kernel = np.outer(signal.gaussian(3, 2), signal.gaussian(3, 2))\n",
"blurred = signal.fftconvolve(img[:,:,0], kernel)\n",
"\n",
"fft_blurred = np.fft.fft2(blurred)\n",
"fft_original = np.fft.fft2(img)\n",
"\n",
"f, (plt1, plt2, plt3, plt4) = plt.subplots(1, 4, figsize=(15,15))\n",
"\n",
"#plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(kernel);\n",
"plt1.axis('off');plt1.set_title('Spatial domain');plt1.imshow(img[:,:,0],cmap='gray');\n",
"\n",
"plt2.axis('off');plt2.set_title('Frequeny domain');plt2.imshow(np.abs(np.fft.fftshift(fft_original)));\n",
"\n",
"plt3.axis('off');plt3.set_title('Blurred Spatial');plt3.imshow(blurred,cmap='gray');\n",
"\n",
"plt4.axis('off');plt4.set_title('Blurred frequency');plt4.imshow(np.abs(np.fft.fftshift(fft_blurred)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Back and forth: frequenty and spatial"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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94fs20/+tadumUuY1ZsfHjDCBQoVSBO8cBcIAoSoKRtWAcT1h0jRxaO2Gtmmi\nWs0OmW1dYoZhrDa6/79ncQI76/WdJotkRlw0HqbjM4du87qS69HzZI7n6aznCTzgeRI9T+taM89T\naRqhadAjep62rdK2iHOyDM8TVZnneQqznien7Xl6uOfpjOfJAZ4nM54n+3meyzxPRaRyjnJ5nrdf\n59fDfn7IOe1tO+7yj3IM5m3beRyzVTwHH7lNF6bjyzAeVXTmd8Jtb7P25JNs33mGzaeeZP36darR\nOuIcMpnAeAI7uyG1PaW5p+Kl+TDVTdtH6XLxadpMejQ+V8e1xwieEkUnzlNUIUKXUtKblBbf9llS\nnVAQonQui+iJj6numaBodktj2hEpokdMuc+FiOyR0tS1DWn4xPaqhOyybh1p+vh/JzQxeOYkW28m\nc9CvO6XWpwL9qVOQvO3StVNUUU3F+4uwjmz7NA1GHpslAgOBQVGxXg3xG7BX1+zWk5ABtrfLbtvG\nQvh0YT9YnSuUYRiGcabY6f8CYJ53ep4nIKpe03yyoOcJoKfseWSeByf2vIOyvPLXLBvMeGS5MB1f\nw/NugGGcKTOd2EWBG63hrl6huhIewxvXGV6/zvDSJQabG1TVABelRsaTkOY+mUQRqqdFqMn/Tmns\nMeKX5KaN4gB9OnibRc1cJhhdIVMf5KXNps2jZPmjK2KfCQOkaNr0JXp232g2rWbGMJtev++uTdNo\nL2pKlBLpO8NSW7oZ87YqDxRITRnmmqJ90EUkxfU1IFTRrgYFYV90BWK1b2Nqp+RqI4gqrlGG4nDV\ngFIco7JiPByF+hBtg29afNtA69HZ95NhGMaKcU6e97B/Acy/Gxvnx5yL8KGeJ8Mb13UQPW8443lk\nnieTWo7qedI0sp/nqUbRWdDzdI7nyZI8TxbwvMwKQwfQjOeJb+WonpfCjjrH8zTzvBQwVdL2nrnn\nSeZ5ca/O/cinJ8/ynHcU+Txs2v3mf9jP4SflvL4ArOQXjwvT8TU67wYYxqmyz3m7KKCqcKMRxbWr\nVM88zdpTTzF6+ikG29sMNtah9UjdBAEax86ucXzU9XTHV1fTK0lR24/go9CNsJPEKu/ISUVSkywU\nhNdS1C8Jkff0qd8xstcNG00vC12dq1xiknTM5irNdmblApXqLSSX1OzvtAm5Z2bLStG6qZsLZtqZ\nHCKXnLCzYsdXJkAua4OmyXR6f8V9KN7HzRTwMfVdfbZlqYMxShf99mscaakoC4qiZFiEOmA1yrge\nszceU0+1fymgAAAgAElEQVTGNJMJXmqIEcIH9t3UPjYMwzg/VsTzFr3lZJVPnKvctoM4aP8uuk2r\ncIHbpzvnQM/TOZ6nueexgOdp3WjyPKlrCb9jjS+viCJH9TxBJPc8zTxPDvA8jZ4nsK/n6dRuUpBU\nzDX8L5309J4Xe6MQka7Xa1HP07xTSAm3W2ooTK8HeJ6ISL9NvedJ0sTleJ7OeJ4c4nk643myoOed\n9DOxX0fTKpx3lt2G0zrnn2RZ57WfV+H4znLkNl2Yjq/1826AYZwR+Vm2uHmT8sknqJ55mvLxxygu\nX6ZYX6cchkKm7Oz10jMeRwlqohCN+2hfSoOfKmCasrtiFDBFwLzvX5MikwnPVPYWqU5CbHQaWUeL\nma3RvnNobtBzpuNJoav7kPtjl1GVO2WMrM1eh/NOq7wtXT0ImV7UVPRN+lOpesJpMpM1SZ1aSd6y\nTi7N1tOluc90zOWp+lLQ30rgM7mjl0QJ/Yma1u1cv64kmhJqRVQKrhowqAY0zYimqfFtjd/bo713\nr0uPz/a+YRjGSmCeZzwEzEjH/EvtWXqetrEzrFUUUdHQoXIRPE8RFVSS52m2cFVVyetnpbaY55nn\nGcY+XJiOr+q8G2AYS2dO0KQskfV13GiErI+o7tyhevYO1TPPUF6/hlQV0rYh2rc7hr29ID+TKEOT\nmNo+mYRHPZPt1cmO7zu9UiHT/MKcpk3p2WnEHd/22WGSRZZSpFDnbFO+rZJdhg+6IsuUrfTzpWDg\nVEr4zPKmXp+5/KfFzk7bbYrOLG+/RqaOwtimKXGLr8e0eIn+FcOUMzKV2jRd7D7sxix7DBCvsQBr\ndrup9gsXoFClcAVaFrRFia8GtNrii4K29bRtg4+3K2ibOgznSKphGMYZc06eZye947EKF45Z4Ujp\nMvuJyDnQNaXfR2Wph3keZ+R56uNwgdHzxKscxfOmDSvdbLdcz9MFPE9yGTyC54W+MdmnqbHnLPO8\nNKVGz1NVZMbzyDxPj+95cgLPEx9HzDxjz5vX0XuUlR23YcvaoPM8p807ly0y7VHbeZZ9orPrWMb+\nPcp+msuF6fgyjIeV/FPsNjcpn73D4M4dBs/ewV27SnHpUrhuew/3d2KKew3jvZj2HtPdOwHKRmZM\nz3USFCN5qRMrCREKrgwX2DbeppjkSDSk4qufTovvNiBFtWC6TgHTUUQRkDIbBYeY3p13UingQ/FU\nzeohiIQImMSwmMRioiJQllHi6n46V4Tl1GkHp/VIjL6lNhGm7Wo3xHakebr6Fj6L5MUj5rKip13U\nUqcFq98RdJHM9LpXIEZBXb/POqd0gsTiq4rP5GlG7vJ1KaGmRB3uJBURpKzQ9S2KwYimntBOxrS7\nO+h4HOtPzI4MZBiGYRgXkhXq9OovrefpedLEWx1RxJVykT1PmlpURJfpeeqKsKWr5Xnhr+N7nrS7\nO2qetzCrdN54GFmZ/WsdX4ZxZmj2s0c2N3HbW7jLlykffyxE/554gvKxx3DDQbgo3t8Jkb/JpE91\nH2cyNJ4T9UsRw5T63kYpSg1o2/4BkA8rnbKSfLzAu2L/09ZsodGuPsIsUQaSOCXB6Yq/p2nc/PnS\n3svFayrqR/Z/bECazqURhPL1zNEAl0YP8lNRwelAlvbtlPT/bJOl3y/dPqKL+k3tOzQM7JPvS4LM\nhEy7PMMLEEWcEEYUEtRrP1Q4GhzL+Rj/dDgVtCgRV0BRIGWFFAVtNcDXNdpEYc4zyaZ2qGEYxplz\nWhHvk8y/jCj1aUTBjZ5l78MjHh+dawWyuamHeJ5wfwfdHet+nifjiYQ6XrUe1fOkbWPOUu95coDn\nyey2Zp4nSMxcenDr5QDP08y/pPe8bI0S1zvteTrjeaEJ+3uedr08+TKz7XIujNaYeV5v5yn/q/e8\n/IhO7ZTle1626oU8Tw7xPFmi5837HJxGZ8aqSehR138a5/ODrhnnxVkep6Vur3V8GcY5465fp3ru\nWQZvfTODp5+muHoV54p+1J7xuC9oOq6zwqYp7T0vYB8fKepX133h+fQchE6bLiqYokyptkLsjOpG\n6JEYtYpRtLx4ptJH0LrRe7LaBl39giwimKQmCVEbo3qQdTxpX7srn7+rj5B1SnlPNyR0N10cVnu2\ng82nYaXj82l5qZ1FrHVRN/12QS9y0Efu3EwHmaZOvCKuc2YfdPssq/HQ7W9lSkimhGom3KcQKsZG\nP4zy2lV1kChYDsCHwxoz9ouixJUVbm0N1za0u7v43R387g7aFbo1DMMwjIU47y+miVVpx1xW1fPE\ne1FVReNtjsfwPHkEPU8oUPM8w7hwWMeXYZw6mv0EqgrZ2KC4dZPyidtUTz1F+cQTlDev4za3cCJI\nPZmWob2Y7j6p+9F8Un2HJERptMY61nLwhAtcigp6zYpkZlGqqabqTASLbFRB+kiWKlCG1/ys7Lgw\n1LV3QbLyecWF6buU8ygEaV3ehwt5kpj0wqxYEZc1G/Wb5QERk142OjlyvRh6n6XaZ8tTjdHEtM5s\nvZ3EdD+mpce5PqrXCVHYJs3bE5fVrdUrEG9X6HVn+jWlj6J29THC+jTWkxDRPjEt3nLgRKAokbUR\nvihph0P8ZIJOxuh4HG9/TTNl22UYhmEYq3FROI02HDO7YF/P0/KJ25SZ5xXR8zii5+mk1tzzRFXU\no7Qt2rR6VM/T6BuiyDzPU1Wd53mCipzQ83QBz1PQ3POElOk1Q5peY2GyBTxPZjxP0345gufJo+t5\n+31GVuGccFzOq+2LnG9mpzlJW89rO5e13hMvxzq+DONUmJagcL0StCxx29u4mzcZvvRWhu9+J+WN\nG5SXtsNFaDyBnd0oP9mtjLu7QYpSJ1d+a+Mkq/eQaj6ktaZbGZu2lyHILvh55066YGfRpykRyrYt\n1U2QTFy6WgoC6jKJiPPk60xCkuYXR1cHIcmPY6aja7bjK87XicjM9uQSknLMcwFpm5Da74p+3X7O\ndLOn2al2zHQU5s9PpeEXU9E9TbU10utpYKJMUkNdBpleFnn7tZchjctxLu7imBqf2p+kKIqw03Ds\n3GANPxgirNOO9/B7uyiC+h003y67G8cwjNXhpCehw+Zf5heNw+a3E+qxO5vOhDkX9bwX5wHP0/08\nr1iy52mStAU9L3SMSPCGE3ieCuE2vCN6nkTP08zzxM10Zh3iedpVvk8vixzgeZI8T12hued1nWjR\npx7oT8vaIafveTrz9pIV9Lz8idP8vK7i+fAo14PTvjatCmfZzqWuyzq+DOMsKApke4vBiy9SPvcs\n1dNPU16/RnHlSqzhtduntaeoX5fqPoHdFAmc9NHAbLjqrkh9+j9PrfbaR7qgl5h0AU4dPqqhE6go\noJkRohQ1yzvL0sWyiAVG2ygKroip9nknVpEtR2MKebwlMC0vZVvlUcBU4yClpqf2d7JBmL5w0Ka0\n89Qm4lDd0hc2TUNzx0hYL0yxnUUcdrqd9OnxUsb50z5MghL3RSd5aXoXBwrIbhd1Gp6b2odRLPM6\nsum1tI3d+ugFczYTTUNbwiENxzioYXxeNUgOhBF+4n52zqGqsd6qIIMhWlX44Rp+bw9//z5+bwxt\nPSVqhmEYhmHMYJ6XLcc8zzzPMFYP6/gyjKUwHflLyHCIrI9wW9uUjz/G4N3vpHrhBarbt3HOhUje\nzm6I9I3H/WNvHEbzSZHATpCy+g5JfrwGAUn37jcN0RRCI1JEKD0cIVJHCjLFi2Qe2ZuKuJFd/yW7\ncEecC8tz2YU+3y9TApBHx6RfX1c/QjJJiivtnottlZl1dMvK2isx/T5Fwro6D/F18mXmAcQ8xT17\nzQm02rczvZa2Py8mO5WpFvex117WukYyPY2G310h1xiR1VxcSccvk6S8/a12JSe6zcjvNIjzhYqo\niniPxrR4KUq0KJDBEBmuRWG9j072+roQXZQ456IEqAzDMPbFvvUZ8+is4RDP03meJyf0PGkaoWlR\nr5o8T9pWaJqY/XR0z5PM87ptiVqVMon28TxZ1PMUNK1DxEnuebKg50lfVl4RQbv7+ZjreeKc4H3q\nIyL3PFEV7TbXhdsPUyacalhT9Dwxz5s5vlPbYufJs2cVJfvCfhGwji/DOEXczZuUz7+R4fPPU915\nA8WNa7j1dSR1Xo3nRP/29pg7auPsMNYpvT1dcLthrNPFM168FbIrYj/MdJIbpY+COemjeEKMRDko\n80he1tE0daqLYtM2scZAGaZPdR5cXEeKcGm8lS9FCYn/+xkBShvRReJ89n+cr7tYZxagaV+k7df+\n+a7zLLMoIewXLaCqwjSTuH8VunT7NK+vQ9u7jrcUwZN+u/P097Q9KaqaIn7i0LaOhVp9v+3OhXm7\nbSP8bn23/0OUL+37THq99ruiq2UR/05DaqfpVJCiAK+ItmG5ZQnb27j1EX68h96/j793D00FdA3D\nMB5eZr/ZG482B74XTtvztG31InmeFoWelueJqvS3Oy7ueZo2do7nqXmeeZ7xyGAdX4ZxYrKClM5B\nVeG2tymuXaV6/nmqN78YUt5v3kCcQ5om1nIYPxj9SwVOp0bymWQRwCRDvh+2OjSh7whrswtWN2R0\nvKrn9/J3dRm6iYlXWfqLtWYSkE2mWaRrZvapyF+ar5gRnDx4lEeXUk0IH3+PJ6EdLXQj+KTXUwq9\nc1CPQ6HY0kNZhWU1E5js9lLnm5DO7X1M9Y/rzdPMod/+7phKH+1LothNrn3UbipzbCYa2mWSpW3O\nd8LMPpyK/EXJzKOiaT/6mTZLiPCF3em7Y6Ep7d/16fBA9MogoYJEidWu6GtRDfBliZQlPtZC0/Ee\nfjzuR5Caav+FCfgYhnExOIuo8n6dGnZCOzsuwL7WPpdnAc/jmJ4ndS2552nb6nE8L3SBnJ7npYwp\nmfE8BT1tz5PSy0GeJ3M8T7yXfDv0AM9LNbX6A37qnidn7Hk643lyTM9b9uc236GrdE44zbacxXau\n0r6cd73dr32LvB+O/J6xji/DWCZFgdvepnz+eYZf+y4Gzz1L9dRTYbjntu0jfkl6cinKo4HdaD6x\nsyt1eiXh6X7HWgOpfkN6LaWNp04o7/saEPloNil6pW18xIhgF3mK+dPdLYpM/54tiukKKMsYvYoX\ncleGzqg2ylxXMyKLmqXoWIrutXGbd/fitJNs+2ItAinDuqoC9nZgvAvlGhRRiNoxNHdhuAENsdMr\nFv4fDKAahva1dS8XMR2ctgnTShSvsgr/d0NRZ1G/FOlM+0+yyFu+D105E5Uk3noQ1+kKwq0J9K8R\n0u4VuuMm4lAfI8FhilhboggRW++ni6p6jTJUxABxFFAhEy/Cup0j3Q0ghE4wrQZIWSGbm/i7r8Pd\nu/j79y0iaBiGYTwKTH+hekQ8TySUST+J50kRh4tcYc8TUdQ8zzzPOApTKQAXCev4Mowjow/+5xzF\nY7cobt+mvPMGqjt3qJ59A8X2dsg4ThG9XIjyiN+sHI1nMryaFOXLhKiJKeEui6KlCx3QDYXcXXBj\nvYeUyp0u5HmhAIH+Iq/9ay4+1/os6hWX+cBw1dovJ2Us1ZNeynZ3w0hGXZCyiJFFh25vwvYmbGzC\ncBAiWYMBDEchLb0saJoQ5SoGFZQlWpUhEjgZx8KiDvGKjnfx919HxOFchezuwu4O7OwiO2H0JBnv\nwPh+vz17ZSyGGk+N6sHVWUQzpqSnI9/tNyGkuqd6G2E/af56PvQ1oKphZCQnMbLWxsNThDaoDyP+\ndMsTcMmZBI1FS8PxCMdH2jhiUh6FJR1TEB+FswB1EqcF8FlfppBGHxINbRfncEURjktZwXCI7uzC\n3m6oC/FATYhVCjAZhnFBmD2JnIdcz1vn7Antop3sltneU4nCz5nvOPMftKwjLqebXbr/DvA8OaHn\nadPoUT1PQNSrzvM8FafJ80RjttOM56lqKi6FiMis56mq5p4n8SOpqR2HeJ5MxrIkz5PoecpkrEfw\nPAn7/0HPkxnPE1dHX6PzPE3vA4nZUiLojOfJxfO8ePimPE/neJ7s43kytb7TZxXOtSc8l8xdjsx5\nbj/O6Rx4KBfh2vcA1vFlGAsz5/yUpGA4pLzzLIN3vp21d7yN4tYtZFAhu3tw//50HYe9PdjbzSJ/\nWW2HNF1dwySr5dVFwVJ9B98/r7EdXYQq67jy2qeJS6qxkEUHu+Gf822K29p1isVlpNoE6SLdpYZr\nbIPL5qO/ICeRE4eKg70J3LsHTOI0a1AMoVhD1zbh1m148jZcv45ubaCbm+jWNmxtwmidSQveOYab\nFVKV+KKM7WrD0M5ekbpF7+3QfPmrFJNdinqM++pd5LWvIF/8Enzuc8inP4XufBUm95MKhH1WDGG4\nHWtSaEit1zZ0+HRF8rOOnhTFE9/v+ywam1LOxadoYQqgaig46gpoxiH6qITIYxEjj/iu1AXaxgif\niyn8JbQNXfWHJJwQj3d2QLuAbYwsKv3tp0I3nyrhNg3S+qIM4fAiyGgDt7EJoxF+eBde0xCtbZtM\nrvLPyoW8LhqGsTocdhI5SO73e+2gLyCHfRFZxSj3Itt5nOXMW+ZxOe6XsDO+mOiD6wnXcs08T2Y9\nT4/heVLXkjxP21YP8zwRkVnPE1XBK+qcLup5XULSHM9TH9sxx/N0Ac8TcZI8T3vP0xnPkzmep5nn\nyWl6nqiiB3iezvE8FdF9PE9O6HmaeZ4cwfM08zyZ8Ty9oJ531HPCUedbNoedLw96fdWuI2fVwXiU\nZS9yLT9yWy9Mx1d93g0wjEh3qh+NKLa3GTz3LMMXnqe6c4fy9uO4rS2knvTRvt09pus7ZFHAvTiC\nT1fjYTKd8p4igPFC33c+RfHQ/MIjQV40dXz5cGFNQzxHaeikqSxDhlRT95KTOvJSajZFmD515KyN\nQlSvqfvOorT6dLH2GoVvF/buhyje1Ztw9SpcvRyWsT5CtzdpNzep17eQjQ3c5jrt5gZ+cx02NpC1\nNWRQhaGXqwG+qmiLkr294Ahr6wWulNCZ5hVVjdndilNF64Zmd0JBQ0lLMZngxhNkJ0iq3r2L27mP\n272Pf/0e3L1HtXMPd+8+cm8vyNOXvoS8+hp89asgd8P+peiynrrisl0thjbW/xjEaG3dRwTLQZBS\nF6OEKTKnGqKIpdAVrK33sjdcjKy6klBRIzuW8Xi6MhuhMhVWVUHDSEghtT3ecdAVbW1997ZJMitO\nu2VqGio8Cp54HwYaKuP2b27DYIjf3UV37tPu7tLuZe3Gur0Mw1icC+J555GFdhir2KaDWPlLw36e\nV5rnLcfzNqLnjXLPq6LnVYd4Xgi29p43pqClpKGY1Li9SdcZqXfvIjv3KTLPK3fuUZjnYZ5nPIpc\nmI6v8cz/F+kKv2wO6gJ9VDhuOPP4ZGsqCtxoRHnjOtWTTzJ497tYf+c7cFcu44bDcCvf7u6D9R2S\nCHX/x8hfEqDZAvZNGx4p8pRqN2j2OwrA1J5JFTlTWC/VdkgZTSgQs8CKfEQe7S+Y6ZH2tPdBoFIN\nh7SCJGbx4k9ZodUgPOeAtQKuXIOn3og+8TjcvoXeuonevIleu059+Qp7m9vIpQ2KS+s0Co3G2qiq\nOILktECrStMqu7th8eNhLFNBar4QqySEzPwQCKUowqMUoRCJ3xCCKBR1SzlpaF99Df3ya6zdfY3q\ntS8jX3wF+dznkc++jPvky7iXP49MXoN6FyaCSBRF34Shr9OoQiJx3xbZbaHxFgFJxyN7O3U1NdJx\nCmJH00DhEJE+OitpWG5iRLANKfLiuqBf2P6wTo1jd2vc5jS/dpE/pbuNVejaCiBeQpRSi1581ceU\n/ZAOL1WFX1/HjUa0wwHqHK33tG1sVy7MZ6hGs2t6VM+RcB7nydXDpPzicE6ed5zVnPbbaup+on0a\neNR2H3Q6WGR7DlvfTO/ModPOm2ZKb094/jrGMTrY80bH8DzZm0jyPK1rzT1PD/C8kMnl+9sRD/A8\nDaPS7Ot5ur/niRzgeV0aUfQ8zTyP6Hl6dM+TRtHM86TzPFVahcYf5Hld/lr0PPAq0fO097ysQ2iO\n5+mM58k8z0OK4G6p7tfZeZ5mniePkOfl7/IDM3oW8Lx5yzpuGw6a5rzZbwiK/PdFY3b/ztuOqeN0\nlA29MB1fd8+7AcYjT/qUlZubjF58E4M3v8jmW97M8OZN3OVLIapz9+50xG8829mV1XpIqe9N82D0\nL93imKKA3Uc8u5rmxTbzuzS6yJHro0IuXqjTRdnFiB1NJkKpbkHbXRi7IaGLMkyTRw0h/L23B7tx\n2zYuwa1b6LXL6I1r8PhjcPtx9PYT6KVL+K0N/NoaujaEwZC6qNjTCoqCcgx166lrz2Tc0IT0f1of\nHr5taRtP3QRpSKNyh90SajD4KBjSbY9D4h0AVVlSFAUijqIoKKuKsnCUrqTeugxrG1DcovXxuNy7\nj9y9h3vlNYovfony85/BvfxZ5NMv477wRdyrr4ZbNvfuhWKr1SA8IKazx1pgRRXakob2TvUZFLQq\nQq2HJhZk9RqmjcNnhyta6uisYyQxFFvV7niCNu3MpcHFfRBVSVOET/v3snOIK4MYp8uG9yGQLAJN\n3IVl2bfFe6jrMAw24ETwVUVx6TJtNYDROvW9u0x2dvB1NpCBYRjGIayQ561SBtV+nUQ5p9HeoyxT\nZ/5epL0ryX6eN7h5k2IFPU+d0/PwPI2ex8KeVybP0ynPqxs0Ol7bNvjW07ZL8DwcRTnX83SO56l7\n5TWZ9Tz5whcpzPNW3fNO81yyyLlsHit7fjMuUMfXBUmBP3UW6QZ92DmbfZBdOBJFweCxWwzu3GH9\n7W9j/fnnWXvD0xRliUtisLcXi5smAdrNUuAnoQD7eBKKmqZhrJuml6I4hHUvQym1OkblUiAvjySp\nxjSgOTun24p0n39cTj4iTbcM6ZfZZXyRzUe4yNd12A4crK3BtevocA2Ga/DM0+izz9DcuoF/7Bbu\n9m30xnXay5fxgwFtUdJqyN5S75nUnnu7LbrX4PA0dUtdNzR1i/ceJ4pqGr0m1GaoUvDM95uQtstl\nQhSGpA61E0SUtlW8axERanGITHCloygcLaHegh8NqSqHcw5RxbWeYneP4t59mi98Hvfy5+AzL1N8\n9rMUL79M8cXP4179IvL6PWRnL6S8SwNF2RcshSzWPnOA0jFLopr+6fZ3iib2b4FOevNbILrl++6d\n29WccDGV3adiYRni4+pcPlh7nF6B+F50LtZtTc/FYvhOcIXDlyXFxgZVVdGUJU1Z0d6/RzuZ4NtU\nvGJ2ZxiniV0rjItGPZPxk/297LfvvE6d2XWc1kfm0I/mMT+7y2hvnjywyLfZ/aZZOHPjgAVPTb8C\nnieyj+dp8DxhPEYO8DxpGsk9TzPPE69ymOepqB7T83TG8wRV5Iiex3AtuF70vHa+52nmefKg5+3t\n53mydM+bHNnzdFHPo/e8/mg86HnSHZrFPE9XwPPkGJ6nmefJdGNPhYMWfpLz1kmnhYPbtt/17djT\nLtHzVlkR53VAHru39cJ0fBnGeZE+XcXakO13v5vL7/k6Np57lsHWZtCMNIpPqu8wSZHAcZ8Kn4as\nnkyyIqeztzU2fUHT7pFFAZO4TEUCoUu7jgU1p1qeCpN289J3oBTxdsd6EqJEkjq8YvFSl1YVL4Jp\n0XtjuHsP1hW2LsFbX4IX34S+8Bz65G38449Rrw1ohkOKqkRdSS0FjTraGuooPfW4Zm9vwv37Y9qm\nxvuWtpmgbUNRCoOqYDQcUJYlReEYVAVVWeBcFBZS/1wYtaZratxGJZVSCNtet56mDdNNJmPu7Yxj\np4xDipKiqJgMSwaDimowoKoqqqqiHZXI+jpy/TI8/0b8bk35+lcZfOVVqk99hsHHPon7Fx/C/cxP\n4V79XCj2P7pEZzu+AXXh9oCijM+Fh6Soq5MwRHWbCtPSR2Q1HscyDd+d1X3QMAy3xsgc9SRIkW+D\nghQFUlRBcH0zHZb3HtUGfEi1p3DhIXEobXy3DnEOKQq8E1Sluy1DnCN5Z1kUuPUN3HBIuT5i51XH\n+N5d/O7uCS5RhmEYB5J3Yh1X3ldZ+le9bQ/F2X2e561HzyvAPC96Hsf2vD3apsH7VtpmoivmefKg\n502i533pUfM89U7kEfK8Rc5hs9eY/aY/j3P1Q3MOPius48swpugjgAqhxsP2NqMnbzN6wxvYfsc7\n2Hj2DoPNDQrVID+7e9NC9ECB03EsZlr3BU7Hk2kZSsNXT9V5iBLj+3YFuRHCk1nGFjojPj6LGmqX\nNk0L3f3+sZhlNyohMTqYZkxp000bUtwHQ9hcR5+/CVeuoI8/hj71FDx3B33qSfTxWzRb29Qbm+y2\nUHvQiaepa/bG92kaj299JygOEFXWh1CsDSicAEMEpSiEqnQMqoqiCAJUFuEhLlzAgxD1AbUpGZTe\nF1O6edN62hicq5uGzdEAHwXJq+BVaLzim5px2zLe2wORsP4yPooBxeY6fn0drl/BX7tB+9RTyNO3\ncZ98G+4zP0/xhVcpX3kdeeVzuFd/gZCGX4K4MLR0kl0FjSnooV5XuN1BpAjFaMXFQxntrs1T4F2I\ndGqM9zU1fQRRgiTFHSFpWG4Nv1UACftxar+l959zqNOwDwWQkMovEp4QVVCHFi60mTDiOV5wolRF\niYw2kGtQDIfs3b1Ls7tDO04VfLqWHePzaRiG8QDZhWvqucPmWVUWadtJO/lmek+OvMxFv3AdNM05\nHYPFPc/N8TyJjqen4HkKugzPExFJnqdtq7nnyQKex/M30StXNHjek/Dcs+iTT6C3H6PZ2paje54k\nz1MYykGeVxQOJ9HzJLqCRJfLkX43Qrg1sm31OJ6nD3reCL++Adev4q9dX8jzyDyPBz1PZzxPjuh5\neoaep6Iq5+B5+TnlpMGMfJkHcdRz2GGdXvOuQbPbdBDzlrPIPIucz49yfdyP2Xadxjn8oGvUftMu\njHV8XTBW2dTOiuXvA53zV1COYmuL4Rue4eoveR9Xv/69lJubFM7B/fshypeifqmo6Xim0Gk+hHWq\n7WnolKMAACAASURBVDBp+kL2bSZDSV6yC2YvRuFChYsf2VZTGCxexGKkKHVmpaz5PL3dpbT3GAHM\nazwUWcdXXlPCN+h4gtwbo5cKuL6Bvvvd6De8F//SW4IQrYXhnz2w28DuBHZ3GvZ2G+q6Znd3h/uv\nv07b1Ki2DErHcFiysT5gfTRkNFpjY7TG+mhIVQbxOb0jPOcKocpe3bI7rvnKvV3u74wZj/cYj2sm\nk5rCFRRlRVkNGA5De2VUIVtXaK9dYfLiHfQb34PcvU/xcz/P4F/9BMMPfojqw/dxn/kYUKLlMEYC\nXZDglLnrijCqzmQSI7IeymEYGSgZifOhCO5kHEcZWo+iEt+kbRved0UBgyH5TYWoopM6rE+iWzmH\nFCHaJ2mHAGkkKXVp4INYGFcA8f3o5a2gg6ovnAqIOrRtghRVFYPBkHI0otrYoFgbsvOlL2VC1B8V\n7VZvZ7ZlY3vUeAg462j2sr5s7bfchZZ9yASzmQeLdPAddR/my0zrmN03s+047roObcByOJ7n6T6e\np+MxckLPk8zz1Mdq7wd4njiR3PNURWc9T5yTUKspFr3yLaI+jANZFHKY57Gf5w0rtFiu5839UOj+\n36z1uG9jOYnnXaa9dpnJi88mz5MZz9PkeRzP8yTzPJ3jeYpH5nhe//lX1eh5coDnhekX9zzVQSXd\nEJogB3ierLjnHXYOy5/bb/5F13OmLLBHF7lGPFJYx5dhRPIzVnHpEhvPPcvGC8+z+eKbWH/yScpB\nFSJ9TROHcJ4ZvnqcFTftanrFOg9TozU2fTHTqQigdEL0QC2orChlECXfP5+iQ0l+uvk1RPiKIvyd\nOtQAfJSffJhrIS6rgLu7YRuuXIZnnkWfeQP+zjP4NzxN+9QT+Cduw7WrtGXJZBwia5NJzc5eze5u\nEIm2bSmKguFA2Lp1iaoQBoVQFEJZOKqY0l6WBWVZUpUOd6SxOY5/fKd2LVAVDhlWOCdsjYahuGrb\nMmk9rQp1C7v3xoz3xrS+oRwXuMJRDSuqYUVRlhRU+BuP0by9ZOfa4wze8VYGn/xmBj//81Sf/jTu\ns59G7t+PRVCLKLMtoj50VhVlX6zVN90xVDTeyjAIrzV1FNYQVRUHDEcIGqQkvg8kRX9TR6KEyKvG\nYrfq2664aogMh4mEWOA0CTqgbYguS6oZ0o1kRC7c8U6MGDlUR1kUjLYv4YqSan2dyb171LEgqibD\nMgzDmM95dHpdFM7yy9hBy1gkKr8yHOZ5xQKep0v2vK6v55Q8T12pJ/K8oug9r16O553WG2W/nuvO\n8wYVblvYGg1oW13A8yTzvGphz9Plep4e4Hm6oOfpMTxPHyLPO+1z0yqe+1axTeeOdXwZjzCa/SSc\n6IuCYn3E2tNPcelr3832S29l64Xnw0jAc0VoVoqy0Xy6eg91Vrw+Rf60y7DphCgVLOgeOtVOoM+y\n0azNqZBpN0+fiRMupDK9zDhcNmW8zS4tV9oYbXTxtRJ96mn4mrfiv+HraV98gfaZp6irisY5VJV6\n4tm5X7O3WzPeGzMej6nHE7xvEN9QoKytr3H5+iU2N0esb6zhRHBuOv6ziDkv2iW2iKF3640S4Jww\ncI6qKrtpakIa/97OmN3Xd5jcvctkd4zf3qCuShxCM65oBkOq4YBiUOI2L+E3L9PeeSODt73E8Etf\nYPTj/wr98I9RSkvxuc8jrfQRYG0BieU7CrqgVNtkjdYwVLXE03WXqRcitioFrixDFK9ueiFW4vHM\n3iOE95q22q1K0m0Vku27GCOS6Ng4j7bhvSROO4EPixCUFkpFKIAwmpGoCwM/rG9QDIdUmxvsfOlL\nIC5KUYh8xpDlEY+yYRjGFPNOHgfd6nERWDQj4aKu7wxY3PMkeJ6yt4v2bifL8Dxd0PMEZFHPk1Cq\nXHPPU0X38zz1qvM8jynPe572mad7z/NKPWnZ2dnX83Rfz5N5hkffwffAUaLzjvy5o35oD/S8gVAN\nCpQhMOV5svv6rs73vIZm4KmGOs/zZEHPkxN6npyR50n0PD2C52nmeXJCz1vF884+b+Qjz5+z37IW\nXce8vt395l/mdW9Zx2eRa81J9/tcrOPLMIjn/6qiunyZrXe9g62veYnNN72J4dUruDYWJJ2kWg55\nZtecWxsnk16G8uhfXti0zaOAbSpSQLgKORDP1Ggu6bcjvk6UKg/ahMhSWYYJXMHUMNU+RJlCQUtm\ncsqVbsicxgMtuBZeeiv6pjfB296KvvFZ/GO38Je20UFF07owqvVuze7OLvdev089GYO2bIyGXLm2\nQSEgr7+O/tTHKLyHJx/HP/M4fmuUxh06F/KzaNzaUPaM3ieTj0487LbQfvoXaH/2kwx/9hMUgwr3\nje/BXd3CVSX1pKGe7DGZ7CHOUVUxKliWyOYItp5gb3udyQvPUb77XQz+9UcYfujHKD776VAXYuMS\nVGtQ78WhsSUllYcIrgjaxM5R6nBsXRlESlvQMFy5jsfZPHQ1HEK0UemG3E7Ljre8ahgWCfFt7+FO\noXBd1C8NCBV2XIo263SmmPfQxJ1aFnGe1KnXUBQOGY3gxk3KjU12v/xlJq9/lXZnZ3r0S8MwDGMV\niF+LHx7M83jA85jyvJtzPG/C7s7eET1v7VzfOAd6XuZ40573BfO81EesKuZ5xsOKdXwZjyDTEUAZ\nDHCjEcPHH2P0zDNcfs/XsfnCCwyvXaUQ6Wt3dbczzt7SOBMBTOnu6ZHLUJOyvLRPSU91vGBaMzV7\nohMceoHpojspOqhMRwbzeFlavmTTEGRsvBcunpcuw/XrcOsG+s53wNvfhj7/RtpbN2jKglqFpvHc\n3Zlw735DPalp6gn4hkEJVVFyaWvI9uYIXr+HvvYV/Cd+Du7dx33xVdp6wr3NEWsbIwajIdm4Nsc8\ngkdjPxlq6ftz2vhoPIzv7zJ+/T6Dj/4cgx//Sfj4J2g2R+gzj1NurjF44jEm4wljp7S1p2097aQO\nKeZe8RsD/PoIv7kNjz1O89RTtLcex482qX76Jyg//m8o7u9R7OyhdbgNQVP9NYkR2iQfacSf7pBr\n/97pQqT9m0cB1MeEdgnRulj0VHBoXu6g+zOOlB6j0iJpT8Vhz50gXb05SKMCEUdb6kcAiin0ABIG\nT3BS4aowapIbDMJzZcmkKGh3d2knk6kja4XvDeOR56xPAvklYqHUhHNa9mwU/LgJMfPaMLus/MJy\nlPVlF5iF27Jfm47J4p7nFvQ8OSXPkyRkR/Q8RfQknsc7oue98BztzRs0ZUmt0DTK3Z2J3Lvf6NE9\nb8y9zTXWNtY7zxM94pte9nvzJNk4cNZub0x5nmoX3Jz2vL1DPO8Wk7E7K8+T6Hl6TM/TBT1PZjxP\nM8+T6HmygOdFYTtzzzvoHHaUhS467XG+cpx0/kXPocfloI/k7HqXcU4+ybY88M34JFjHl/FI44Hq\n0jbDJ5/kyte/l0vveAfDm9cpRyNcHYelnmTy0w1bnRU0TZHAfESflOqeUt+74qZNuOJCH3rqOrei\n1JClPyd5SUNYq+9VNI3So5qFsvJzgoYIocRCm77tl+miTLUNvPYqXLoEb3wW3vse+Pr3wGM38deu\n4UdrNGXBxAu7Y8/9nYavfPkud+/eY21YsTEquX5ji421irVhRVk6nFfuffQLTH7yZ6h+9mNUr7xG\nWX2M1+/d5yuDNS698Qm2RjdYg6nOr2WeWQ9aVi5DaVe2GoJttYe9BnbG0HzuNfwnPsHmj/8k2z/x\nU9z90qvsjNbY+2f/krIsGL3hcdZHG7A5oqlbxnsNd++PqZsW7z3OCYUU/P/svdmXHEeWp/ddM3eP\nJRckdoAECe7VRVYVyeJarKK6ps/0kaZ1pHnVv6cnPUnzqqOlNZJOt3q6u2p6qovFKhIkwQUEQBJb\nZsbm7mZXD2bm4RHIJQBkAkhUXJ5gBsIj3M3Nzc0+t9+1e8UaxORw+jTle+8wfuUVio8+ovOb39D/\nP/5X7G//GfEGtRladGK8jgKpy5DhR8PSBM3ymM1SUefQKqStFptB0Q3ZFqtxmLjSkMJajcV2eqF5\n1AFYFI1ZIAnKoQ1pvlUV9a7J+OTrCnWCmKAwiongox5VieCmUVQ0YdGFaHTt17C9DuVI18NkljzP\nMKdPk6+uMu73Gf/wA/6H7w91hF/a0pa2tKXdZbNP0vdv7Yc02eGzR1Gmxh5nzhM4cM4TEE0xnfbi\nvLNn8KdORM7LgvfTxDMYugPgvAs7cl6TinsP26/R3BfnieC87sZ5sjvnnaPf6/85cZ6qqhwhzluk\nvzjsSaSlHSFbTnwt7c/IpgqgAmZlhe6ZM6y88jJrr/6Y1Vdepv/0U5jMBrf35O7eBqImc2MKaNp6\nVdX0lYCoaru9RwVQYz+tTIOYtmHG2um/m1c8BYkBy9sBTsMGyOIiwibweOuVYnmZqD6WJeQ52uvD\nGz+HF59H33sfXnsNfvQyda9HlReUtWeyXTMcV4wnFeWkBq1Z7+esrnRYXSlY6RV0i4wit5STisnm\nALn8JcWnl7Cbm9hqgq0d9ouvEDGUN15k8PJzuHNnKNZXKDLbxPza7+otQsN7fS9d+2ZlARGQFMra\nM9wumdzYxH17nezLy+RffEZx5RqmLDHqsYMhnU8+g7OnGPz0R/ROHae70ouBW0Mg1KpyuNrj1THa\nHsUU2RabW6S7Bk+vMzEZ1co6dV7QOfsUxedfkn13HRluQq1IWhBqLCnNOd5FlZcAw1nWwLC6KojB\nsSkEpTcDJAYhTfmQYs3E9iAEpTGohNNJrZQ+XNJvUrZQQspwTe0pmgAYQY2E/XgflEKNM4riGrXR\nSAaZhW4PThrEGkyWUW1tUo+GqNdWaZeeX0tb2tIOzPbqUPZ6MNpr2/12Uos8vx92B7jokHo/+3qQ\nB82dfrtgXdw75+ks58mDcJ7OcZ5EzpMW5yk0nKcpSPkBcp4ar23OY8p5Msd5WuU5ZaWR80rGkzpx\nnjac1y9Y6eeR87J5zpPIedriPClv/HA354WJu9lru8BE2G5XeV/Oa/3bq7Y4r5rnPN2b8zborvQD\n59l74jzdgfPkHjlPIufpHOfJIXGePADnyR6cpy3Ok4fMeQfdFx3Gbw/au/hey7Ho+Hc/Y8ZBXuAH\n3tdy4mtpf5amgN04xtobr3P83Xc48d47ofOu6+mE12Q8N+EV/5at+A5NGutqGuOharm8OxfhyE+X\nrAHNINOe+PIRXKyNn9VBqUueWlG1CQpedKX30TvMZuEF4MrpSToXuom8Oz1eNQmQxwqc3oC/+Xfo\nL9/H//gVWFsFY5g4GFXKYODY3h6zeWcbV5UINaeOr3LqxAarvYJOkc3EKR0Pxmxev8nKpS/ofv45\n3iusdKHokl+7xupnX1B+c4XtqzeY/PJt+p0OmTWYOfftVEv3Gw9sHopmAKi1XRVqDS7vZenZujmk\n/vRL+v/0D6x+eZm161cxvRXqlR5UJdnWFsXlrxidOc3NS9+wYTOK1R4mE7pZxkovi6pgxe3NEVtb\nA4yxWJuTFx2KbkHRy6jOnmdw6iyDl35M/sFfcfw//C/0//7/Jvv0BqZ0kAOdAvICKauQxaecxPaR\nhbaQ54jzUDt0Mgzu83mOplqTPCwxKMfhrDMbYj0kBRkT25dHfd0SpSWog03A1JDIgLpG0chn8So1\nwK4Bcsx0SGk+h6BcpoxSgHjF5gXm2DGyfp9i4ziDLy/jJxO8uv1WMyxtaUtb2tIe3BbVk46kLTlv\nUc6r2d6e7MJ5eYvzQlO5P84TjEyJ7tFy3uAeOc9SrPYD59nIefUTy3mqqCw5b2lPqi0nvpb2Z2Ha\nmoA2vR79F19g5bXX2Hj7LfrPXEBUkSrCTTtz43wGxwRBbSiq2iBUT7M0ep2uoWsrd0pwFfbNsNwq\naPKsSQNXGtzi+5QVqD3bZLKoGLWWMqbt6a+rg0I5GMDaWsjW+NqrIYj9Gz/FPX+Rqten9oZqogxG\nFYPhhNFgiKtrVjuWznqfXidjpRe8vPJ86qmVXMqr699T//5jzM1b5ApOJKhGRpDcYHNL5/p3uN9B\nbQ3j0Yj8py/RWemRx9por5NY7NrOfl/mPk+A5eY+qzW4vFdOGUyU8e0h5pNPWfn4Y1aufksx2MYZ\ng6KIdwieTCATYfL9D5T/6beMuzmjc2foWIISBnhjkDyntwqSZVSlo649VTXB+xrnc7AWYwxmdQV5\n/iLDv/l3uAvn6P7mWTof/4n80heoVIhGkMAHCDJmeq3ranpCNp9e95gpVESZBkK1YSWFatifC9sM\noOkaEcRXhZACW30DRCneSHDBD1cprMiI2XoEcBIgPmaYUq9gFUmATwCjqT98DZOQ+Snv9eidO4/p\ndhnfuEG9vY2v65A1qLnST+zz2dKWtrSdbRHl+H7V5b2eug6rszmsJTf34m210xC52/CZPtttf/t5\nqx3Gue7qfXC/nCfxr+7DeVrX2uY88V4W5TxNrjDQ5jxJbKegc5zXhP4KnjY7cp4070HnOY99Oa+M\nnDfakfP6vZw8s81h0vLBe+e8YeC8fo9cRERV5xvcohf9njhPlRqovFA5IueNMJ9c2o/zJHKeznKe\nHATnaeQ8eQDOk104TyLn6YKcp3OcJ/fIebIH54V/PTrO26mf0Lm/D3Kw3fq3nfrQRWyncexePJQX\n8Rzete+8B7vfupuvr0cG9Edm4ms5L7y0+7PZlmO6XYozp1l7442Qxvq1V8nyLMR0mIxnA5w2k16t\nIKdlxWw2nzKqfm6qBKY00u2gpknpa4BIWqpgu7gRoFJqYiOEDD7J9T3qWCmTC1HVaWCJ2e4kZnNp\nXPO9R0+dgtdfh19/iL79c/TkcVy3G9S/sWM4dAwHI8aDEeVkQCcT1o+ts7HeZ221hzHSgFA6A1c7\nJlVN/c1V5PcfY2/dITMhQqeXGOw8t5hejr1zG7u1Re2VygjDp07hrYVOTgatFNj7Xc3de/70uU9V\nSnh5CfEmYu4cSqdMJp7hnTGTqzdZ/fRT+pc+ZeXGD1BX1HkWMkD7GtEw8ZVbg715C/3t75hceIrB\nj15B1jpINw8rHDBoZuhYS6dXMBqMGQ0njMc1VVXjvMNmGVmeYzs55uxpJqdPUl+8gJ4+CUUfc/0m\nUg6RchLOJYKQmpgBSH2TClvFILaI7SG0N/UewQc1LwuwpN4jcWlF2B5gSdL1SROqqqj6sLtw8lGB\nNIBpoCl8jwbuhZi1ygqqIS4EgBoBTQq2RxGQCO/OI0VB1ukgp05jV1ejyKjUg0EDRUtb2tKefHvE\nd3p7+NjvOfsgjnNQ+9mpvA9j3+1tO/3+oC7nAnX/+HCe3AP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X3n8BPvxF5LzjYLPIedW+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mhhYTBfLpEYKSYMQ8RmO+Dt1Ra/fljj/75d4/0QsQaJlqoJ7mKlLLKlNBK5I0xHRVpnhrxa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jOqYPKi0F+ftIy3u/RDe685ynt+A8eU+cl725XpHzintXXSPp2rfgPJUU9krT3K6K86Ti\nPP2QOa/oVEEke8pc4bzsOnMN51l+U5VSc96y7WY/nsh59bgHM6aLnGcaUA9wnoFE9SzzSqSM41TH\n3HnO04rz5A5wnhVSbrCJ80r5X+W8svEq52n+Z97S5jkojmAvy3maOE8OcJ60bas3cF6+61vmPBNb\n9sJVc56USngh52nFeXID512P1dcf+6pp3uXeczoOfN3BpCEwjSN+PxJ8DEsnFRCx/JgckQIxV65p\nH4lQ5CVaAUcRxhTodK/KXpVJS8DCADxV+E7guwbGzvFgaFg1jqGRJAgiFCEyUwo+bVu1bQSivmPo\nOzoLW2DCRJL7u0FMeaXLlqgCLwVyDI4KSC2thJiCLjez8ypgAgsuWO8vSSqRrBqyYStUsqCUebHK\nRTDx+Mnjt3sutxNfP1f+pBv+x/CI5vxTzh485vz0lPVmQ992NE0TV+Gx/NYalwqG1O5FGWwThRQ5\nQVFas0Sp4oNn8HvW3Z5xbJmmge+nDT/4ka/DBb+YnvJv+JEGb3fHSVNJq0pGZhBaAJHkf7JSSnQA\ntZDPz2RUkaDLrIQGUwhIZa2QZGfJjOaSq3WpA1vC2WKhWFaUqtyWbJQfrbJTXYGzUiWolEFOO7/6\nriG+DIT0YjH5CEHRChg/FvT0ydTx57HjH6cVT1zP0Pd0XceDvmOzWrNZr1lvTlit1/R9n0CopWma\nFDCzcIBlKKRVIS3GiIaA7zq89/kzjRPDNLHu9+zHjnHseTau+WnaczZd8GC84Lf9JQ97H+FYIKSY\nBLmn2HtEUuJZh8/KPvY/kwu2nHXIBSfZqqYal7cu8PEiXVn6bM1geZvJh/wpgYIRQVyDc222BNrU\n6GhfdAgNXdvRdW2EQtIzqFxZHlqJA3hOYGiEyUVD20+TogH6EINGO3sxCIoLgvOCbwIOh2ggqMNp\nat9Lc2N1r/z0RUjOyjv3y7TUeQSiEXd5id/uCOOYp+gc0zEd07tPR857v5y3VC2ipkmOnPe6nGdt\n43U5L47yuDRIUergBs4zfPuYOE/vEOeJ5EGvj4bz9Mh5f1npOPB1B1N0gR9T4FOfdI/koKfqHMEJ\nGswiWL/Up2tQ9KnppbTeBN7BJJI+yR1eYUww5FWzq+YO+LmF562w6h2bruGka+mbCEF5pZ4UcC+o\nKWllAnBC3zYMbcvQtvRtQ+dkLsqEEjcnD2zH3BvgxIPLoFR0Y037pEBODUdzZS0zeIL5vjpHiI2Q\nV4WJogQkOAwXJQedtgnrRflnF+T9xH478v0WvhsH/tyd8NP6IcP552zOH3L//JxVP9B3XYofUOWj\ngqzMDpWAzBbAqsxtW0jfnYt/G3W0ztG5hqnrmPzE5X5iP+55NrV8ExpaFT7zl3yql6xQREIqtqbI\nXLFPBUMLRTT/W2+vGugs0L0RTtoWJ8QXgM3Pn+bEVzA1M1QS25Na2dV61b4kYJrBTynQ6ne1V2Tm\nxl3+nQOxKsX6p2UVn8lHl/fRB6YpEHzg2eQiDIWBb8PARTPguoFhPbBZDWxWKzbrDev1mtV6Qz8M\ntF0CZudSrAeX82dtRNEDQKSEYG73nuA9bdPSThNt29C2Lfu2Y9eOhLFju3f8MDZ0wbHd73gQJtat\n0jUB51yM5SClpdpLTAmEav1xqXAl1++V/pWOF4ObF7FQff6Bv4VxEwgZFFEFiLXtzkEqz7yiGNGD\nc+bxlXndgpfMemuWQU2CVI8ytnDh4RnRIrimQJUPAReiVbAJAZEGp+ZpkMpCqjJalKfKrBQrOZkf\nPufRhYAbR9ju0HFCQ7hyvWM6pg8ovVA6XJPq94n32gFeg/OyZ8AtOE/eAOfJS3CevCLn6YLzEqq5\nsm/OeemgtMKczbuS2cn6Is7L31PZ2VDXK3KevibnlXG3t8d58iLOy8j9jjmvcG7hvJknmJYrX8N5\n5ajCeUZHJb1bztOPgPMyFAmiL+A8fQHn6S04byGXb+S81NVFD3CeJM7TO8h5ySdSswwjN/eDnKcl\nDzL/UjhPPwLOuymzr6rzr6TjwNcdTBoCYYou8N57XJMsgM6l4KcJiqRM75qlmZKIKkJLgl16xQAA\nIABJREFUf44QJGmpa4SRaAWMQARe476dwjPg507YrhruDx1D19A3TV4C2VWj7MFgKimECDSOrnV0\nTQrW2LR0mUM0Zzf+phJc1bb6IKpjpFYIBwa1qhe//JKYYmfMgKgSzwd7nZWxAi5EEAqSBXtQ5jCU\nrC8hBKbdyPbZln/erfgDJzx59CWrT7/gy8efc7JeM/TdzHqbwU5MyBalUWBIZxbBHFQ1FDiSEGNN\nmMu5qsNJQ9t29OrxYaJrduwax8XY8f3Y82Rac+l/YK17BE8rnuDiSkxl3fQEFU6qDAtzkVT9WLo0\nLxVd/m1QlB7K2c788KkBa9me4UarQN1VLeqyTu0edt51eS6wpXrwCuWYYoqtgpxqcX1PlsBx8tH9\n3Ucg+nls+eN+xVeseOIGzoaee+sVJ5sVm82Gk80J682G1WpD3/c0XUfjGlzj0upYxcJVp3j/kCzA\n6W/weN/gnMc7h3eOpokf17Q0TQyo2zV7+sZx6Rqeu44/bBueTM8hPEPUxyWeCZAC785eG+yfDEWl\n7ZrVui5ll5pGtqglwHCkILFCeeHIhV4PaNk9qJDAdhQeiH2pKiuTCc7FYMvJa06c0LYuyRTzPAgR\niFyTQSk/ivVP++TuUGSOc9HTYi/KU4UuKIM1VwEJEHzspz7lpZY/Oivc8gKRB/WrOq89HywfWZ5C\nBK5xRHZ78H4G88d0TMf07tMtOU+CiL4nzhOX3i3vMudluZ63vxbnxYJ8MeeJhqCJ8+SOcJ6oOv0Y\nOM+mmlo70MR5kuMVzaDgMKWZC9KLOU9UZyN0d5HzZo94RzhP3jLnlaqPVsCqgl6J8+TIebN0rSh8\nxXTT9eSa7e8lHQe+7mJSJUxTXPFnmgguxvLBVuBwpWPbJ672U6+0QvVN8oobASGIxX6ACWVCmBIQ\neTSCkirbBi47oVs33Fu1bPqWrmlokreVCRsTVAGhCUpwaR4z8YWyb6IFsE+WwCZBXApZWISLgY1d\nG83CZSZ1auDJwkHS/1W5ZEVtHmCk6y332w2u6Z1J4Ul8SPJU9SynkkI0ZaiKH0f8dsdXz5U/XK75\n6d7n6PnnPP7kc07OH3B2ckLbtnH+ud3XhFgug+qRK6Wids8ZDEVFQlLK8VGbxBdROdsKM9MUL9l0\nwuBamm7ETw3BNzzxwv/nO37FT7T+WVwGW3zKh5sX2azoqh0ic/F3E2nK4kCzYZspKENgVRDmLq/V\n3PW0yk29TRZiPzJdhqJcTnabWVELppXLdj38IAp5qoG1gZnrewIhDYFL7/huGvhGV/zQrWi6FQ/7\nZP1br9msT9hsNqw3G9brFevVkOKltEl5W+Dbqy3V4NhplZcQ0NDgGo/zLn0avPeIeJCpiBGX4iGk\npbNHgeej8AcvXE5bfilbVi30EgiBPGiUay/1rxlrWru1AeiKiGN9VG8CCYrMw89WuZHUHvKZV0mM\nuv3lGC/1i1IlM5y5uxtUJhhpmhTrS4t3Q9M0NI3LYGFiqFgMC3zUv20peicxxsXWeS5Gz+no6YE2\ngX1QokXQOxoJaBMtt2qBgOtkZViLvcV+hBwXyF5cHIqbJmQcY8DT4K+0nWM6pjuSlprjbaTbXHep\neJbbXz/dnvPs7eZdcJ4sOE9ek/O04jy5Leel4Y/qBfMK05WVByvOM4WtpoykbCMponpMpqpMyUY1\nTQVpVpzrOU/uIOfJ5IO+b86rHkdhPmbxIs7L2j5xXhr8i5d0rma/lKPZ1eUVOa9Uwd3jPKl/vGXO\n04rz5AWcp7k1vD7nJVGjms/MI5+lWqv2py/BefICzpOmafQA56kTyRXwmpynC86TGzhPUrPUW3Ke\nviLnvclBr3eRrtPJL52OA193KOXa1BT0dBzxY1xxQpvoAk9a5jqDkH2IciJLigOyWyHD0CTCRHR/\nn1QZKTEfJolLWu86Yb92DOuWk6Fj1Xa0zqClvH0lPRw/khzEVeMcZlesgH0X3W0bKe6XRciY2DLZ\nVkkYyJ09rzBT64MrA2C1tFh+Xx5/bS2QiCMJ61SYTiG4jJiaD6sC2XvPbjdx8WzPV7s1/yDndOe/\n5OQXv+aThw85Xa9pbC65mkA16071SFX5zLKEzqYaWD5DIFoVfEDNFdvHQJvjGNjtPZc7zzjFY+L1\n43z+oMIUWi7Cmm9DC2GkZ8sDCVFQO4e4Q3Ky6KOrFMe8HdbEuSxyrX5IVfb1Niswe2YUJFyBrXzJ\ndFo2EkYNTCGgCooA85S/EntAixqvQ6nmLKVshdwGSqDTGOQ0frxXfhod/7Tr+U56fnY9J23Hmg4f\nBvbTCjeuYLciSI/XhikIqwBDgK6FpiEOxCQlbop6ZhGu2oW5wbvQ4JoJ5xvcFHDOIzKVtie2THZD\n4xpa13DphK04vtk6NAircc8DlMbFqRWo0qT6XgZCteJTK8/F20ZkzUWDyNWS0ElLf8iLn+uy2+YD\n0s+5V9hyILx2h4/PHMvRNRUQEeNROIGmbXGNraZUrin23JDrgLzNIdKCs+spezdxKcI2BTttUj3Z\nsudNCHFKU1odarawVyH06sEWMizLR5l9xDlEoRknZB+BCF+i1sis1xzTMd259KYb6FJN1Ok6Bfem\nM5BHNhLnScV5uuA8OcB5uuC8JSLcFc7TazgvDWpBFmCzaTsg4vQtcV4FLPZ7xnlxx/Wcp9dwntwB\nztNX4DytOE/uKudJ4jxdXOEVOU/eEOfpAc6Tj5jzpPTfazlPrLtUnFdVdl0tM85LXcyGv2YoRz7/\n1ThPK86TazhPXNPoAc5TG4h6C5wnL+A8G7iHMpW7CFIpg/9vkfOqzjn7/bbSO7nPceDrDibNlsCR\naRxp+w7XJUtfY27wZQBMswBZ6J5aj6TtAcFD/owwd30PEYi2A7BpGNYtwxAtEZ1rsuutqWqvORIC\nNufea3SFJ1knu0boWodrO5qupclexUWx5A5eNlSStfq+hJ363Nm++twFEF2BofpeVWFFEUxWt7YE\nnYQih83i4tNgx+SZLnd8+9zz3572/HT/F6w/+x0PP/2M8wcPWQ0DbZvmkmchK1nGzR6TepspZrM2\nleCWTXJTD0HZbieePfd8//0l3317yZ+/3/H9DzuePBl5duF5tlUmn16uURpRVoMydJ623bNZK6en\nyncnjq9O7/Ef7z9j046EEPIy05WLeCWIq4zXfw0oZw3RICppxqoq51UnFRRBdmgPSvT3t202kGqf\nKiSlai7bWFeSsxLqgZdK3B5yD54xW3pgtT4XCogU13fzrosePGNwfLsV/vHC8Z+fTTzZX7Aft/ix\nZ/IDe/aojDRuR+OexZV82oZ+cNw7bXlwr+OTBx2fPFrx6cM1Dx+uuX9/zWqIHkneK94bKJePhBBX\nkvExbomTBEOTSxDucJNjch6ZCiCJs9VxhJ3Akz38v3vh9/KcQXb0neIk9u9FT8plaW7sGuaBactx\nJS6YneAk1YvUwVDzq0fZZteu7lkiSqS+tZAb4jI0YKv/iEiaDhBB0CWIiU1WaVyLSEPJQWok1VOX\nqSnWj+NKkUhLDrTadITG87yZkN2OZr+n0fgMEaQDXiWCUtWS5dAdMxfZ33nnqWWKk3jNdj/htnt0\ne/T4OqZjugvpY+Y817U4sdWbj5x35Lwj5x05r9TvK3CeDasdOa886pHzXjF9MANfb3uY8U4lVfA+\nenzt9/ihpw1tjN/gKgiS6OoYLYEyO73o9HnJeaIl0JMC3KOMRItg3AZTC9PK4dYN63XLqm3o2yZZ\nC2zed/z4UAFRutWkiviQV/tpncQgjW2LtB3O8jSXjvMyyL09byh/lkBUg5HUqquA29XrHICk+hC9\npsWZoCUpTtW8nHDwnv3e8/OF58/7jn/pz2kf/JIHX/6OB2ennK3XyWEt5jMadiuX/wRBJLtHYY1y\nP01xNUQVJ46QVpDZbgNPn4788OOeb7/b89WfL/jqz8/5+tst332/4+nTieeXgYsxKm3TNE5gs3Ks\nemXo9pycBE5PA/+yFv75rOXsS8f6MTy+p6ybBCPzaPIH6mxZP7OCvXnTlUOqDQZIEjB3fCwOxwyK\nwBA9w5ExWHWES4q1MJranyv51nyRcrl4uzkMFdf3kJey3o3Kj5fwX74L/Neflf9+oWz3go6O7c6x\nHVu2Gph0At3HPMsYA3F2wtnGcf+s5dMHHZ893PHZoz2PH488/mTkk4cD5/c6Vqs2x1gxi3SEDYdq\niIo5CN5FMDLLs0tgVDwpzVpWBwoVLhCeeOG7EDgZPQ+ccuIK017h2aqscwwIrGloLkSXy18y72bv\nT7WVza0dlHpMp9jy5hU41N5n1vcrK16WEUINf65x2Q2+eHhpBKQET1eahZZnKi+gApQyFdfmOBPa\nBC5bTwv0IdD7BKuhfGqY1brdU4CvKo3ZY9aewJLrUXA+0O72sNsRdnvUlxeGvyi9ekx3PunhV+O0\na5auO+4lb/ce01XO04+F81zkPMkZxcTYQo5e5bwySvb6nKf1NSS9IechrsOcp3awXewvjfNWjSI2\nDWtWnMs6ezHn5RoRK/RqFPQazlNbSrHiPL0d51k8KKTqEe+Y8zRxnnyEnDeLpmqFX7DtWs7Tl+Q8\nc72TdMr74rz8QG+A8+QNcl5+0AXnSeI8fUnOW76BveiY21znVdM70ckfzMDXX1pSlOA9026HXw0E\n35de56pPkKwYApRORBH2RVhE9ZDd4Imr/EyUpa9Hp0y9IBtHNziGztE1jrYRGleEpQGRunRuAiOv\nGuM/CDQS8xCDNLooaNqWAhzVaITlsEhNZmAzU7z5n/nf/PVQHCSprlvtqwBnfqzlR0FDdcj8+DrI\nZNiPXFzu+eMz4av+Pvr5X3H+i1/xi0cP6dqGtnFp2dliAbSgsZaVMqw2z6mt4oPElZEURZzgcXzz\nw5Y//PE5/+W/PuOP/7Llz9+O/LT1PJkC+ykwTQ7VAV2BGyRZ84pQ3Lu46tNlWPHTzzv48ZL2Ys8/\nyp6T/wnc3zb8X79XVn1AggfXMJtzliH1CniWY+qGWIZEFtvr0SlrH/VIR60gKnIyhW5mowQq0Vro\n8nHxjBLv4Uqb0li2Ft4DkcrKU/JYVlkqnxirCbyPltZxiqv9BIWnW+WP33v+7/+65b//KFyyRpsV\n2g6o2xCaDaE5QdwA0oJrQVpUhIDjGcr2GXx/4fmHf7mkZ8fZ5mcenjv++jcbfv+7E/729+d8+skJ\nZ2cd3ivjZO79gWBLX4qARF8AaclAkMsvV2FVr1VBicBP24lpDPy+2TI4jySr/tIiOCvWqjuLEZBW\nR2g9xWAur/ICUDCvL40b5s2qboMFwkr7MSgqF5QKHpwFg22b9OIH0tgS31ftzeUJkss8BnMJuCBd\nt02WVZhE2SlcqCLbHe0Yrew+CEqDTQ4qWH/1saV+Nut7VV2KSAxM66InfTN53OUW2e7w+zkQHdMx\nHdP7S0fOsxyzOAbmQv9Oc558bJzHB8x5do8j571VzpsV2JHz3innxemXb4fzlh3ho0wfzMDXUkl8\nnKmIAgXUe6bdnrDfE8aeWbwBqeI+SBXeUZkNy+fjSUAkECpLoKdyfxeYWoHB0Q0tq75laGMARFtW\n14RHjO8gSVjEUXMfUr4lLvXq0ui5Iy1j7VxUpo5K4aXnrvI/257/VA+1VLqzfbI4/rq/XA9DV5Is\n8pWyaiP2yQr47HLk+0v4urvPxf0vePDFr3jw6BGb1ZABSBZAVFzfpbpbrRK0um1U5l6j1enP3+74\n168v+W9/uOAf/3jJn76+5PsfJ548V7bi2LsWJEAHrklCUhyiAdQnvhCCuGh8DgH1K8K0Iugzdlvh\n//lTw/D/s/emz5LkxoHnz4E48njv1aujLza7KUoiWxJJaaVZ2c6O7bc12/94P6zZ2oytRmu7Y7pH\noihSbPbddderd2ZmRAA+HwBEIOJlXd3VdXWirfplRsYJB9x/AQfcS8t7hx1FoVzdc+H4bfqxB6NH\n1WPaPoUhfcIxW47vp8/7IeDpyJQkb2GAgvEVciubP4r0A2MqwVBL/jPaX2YcZyFNfQ8xNVyMrdF2\nyrpRPn/g+dVtz5enBUdNiRRzKJZgl1AuoFhAtQBbIZgARLZE4kuH9yEgcRvfWrSDh6ct91ZhKv2d\nh44vbzf8/ocrfvLjfQ4P5+wtq3AvIojXKLMBb0YwlNOh5Eguw8NHz9TGdRw7z33nqFrPNaPByxUB\nUrJ6GwFypJuw27CPT2CCZowtUUTxnBMq6Fcu5PvHex/+SljuEPWB9LQn/UtH7hnsp4tbQ1EUva4z\nZjr9Pa9FELLz5T+nXyUEkTW2xFiDFIITQwPUncN1Xc//afmCV8VE8J5Obx8uL1v+Db+l5ym8UrQt\ndrXBrzdo2/bBUL4fNnVXXqfy/WiTL5TzNOM8eUU4bwialdfHd8x5OUk9C+exnfPkO+C8Xrgvm/MO\n9xzm1eQ8SaNeQZ4D54UWOhLcd815knGevoGcp8/AedrX8TfjvJR1YnjbeX6cJ68A5+mE88Sr6oTz\n5PLlH8t58hScNzYTL69Mx/bY8v1J5bk9ym7g6xUtQgAit9ngNg1at720+qw+sWMkQBo0StRBk6YW\nvIAyTIMX6DRMWXeqeAOuMti6oKpK6sIyswGGbDSmiOkhJ4yOJ+USLuR9WA9uTYAiBSzJ42UiFMV7\nTN6ovM/n2i49Vyo6+T5tFek3k6unyfET5f/oc+eVl55Whn2iYewDnXYdR2ctt9Yl9669TfX2B/zg\nvR+wt5hT2PTsg+eBBERZ3V2+J42GKMQ5EAQxIbip6+A3vz3jr//bPf754w1f3u8wMwOlgYWFsqIo\nyqD8VCGmRw8eMw/eRy+LgNjwPF2HUY8VaOsZq7Nj/v72Cu82fPSe5cqy4cqyxeAmA00mA9H0DNO6\nHQkqttVcjpnJGXkZe84Z2nb6qwpqEpEM+4jEme8+ik6yK2isy6GuNV2TcavJjWDObSl7ZPo8QPEw\n6OU6T9sq52vl13cc/3RTOdEFslhgqiVUe2i1D8UMyhlUS7BluDdTQFHSR75M96sCBC9826xoNitO\nbnV8/MU55d8f8YufzPnf/9M1fvGzt7l+bUHTOuh8GPaLdeN7LhiWXvSPln7LxBYOE1CPqqOt5jTe\nc7fZYH3HXumo1KPYoR1Lj0Nj9o0eyQRFisb4DToclpnpcN3h+NRmRk0pvc1MYCjASHb/qWkwfn6R\n5MEjBrq3FMUwDd7EIKj5VftvqS9P2wkDKIX7sDG+YYh9g1gaoFtvcM0my84U9QnEwa9UZ4xLDkAE\ne5B0Si/DmA69dI6iaTGrFbrZoF0LjGPn78quvCrlMZyXVPJUNT/NKZ/ljftFlP5ZBPQxnCeDXr7E\neZJg6VXkPJ4v541l/nw4L7uRdP6tnCeq6PeJ8w6WDQcvkfPCGIhq+vu0nCde+ujBL4nz5J9uqp7o\nQt5QzhMlBIDP2/Azcl7cNOI8yY5PU9jGZPVkzpNeXb6ZnCcqolGnDON7z5fz8ibxqpXnartfm4Gv\ng5d9Ay+4KGC8x7Yt9aahaBo8NqZ+HfaZwNDo+HwKvDIAkY+KMWwTHIITxVnB1IaisswKQ2VT3IY4\nhV1MH2fCx8vZ+N1puioYPILB2wBPNmXDTWm6bTTK/WLvdNP5M2wBoke2/QxURC7vmw9WjX7L/l46\n9RaTGJ9RI8yl9fXdpmV1vuZrXXBzcZ29dz/g8O13WM7nVGURYDKDIRmUGSYqt2Q8hisGyXkNyFmU\nBuc8Z+eO3312wT//8ph/++SMf/+65aEW6EEFVfA0iBW0rpC6Ah+CbiIgxoK14btzQ71JEa7dhvzX\nYgxFbbB1RWfOuV2s+K9fblguzvnBoWcpUNo0vXwiFhH6OALT6s7ayCU5k8loFANOB/kkiErQPBKP\nAZPH80oqP/GsjxJNU+JjbAgFIzFFeHZfkryAkstlaBNK4tUU5DRMfe9cyqTkuX8Ov7svfHpacbcr\n6aq9CEMLmO0j9R7YCooaZsvgAfQuxAsoq9EzShAgoiZ0mY1ByxKtFNd0uHbDp3c7/vN/fsi9u8r9\nBxt+9OEh16/N6cSHwC4hNzWC4GPss/DCUjCY8MGqpmd2Aqo1AJ1X8B0XbsED77narjCiLMoYkFfM\nIPkoyzHApLav/SBX8FpFTp82m2jnfS7zTN1pXzdZG8ovn22dMnm/JX5OMRokvtAlKPL9WoehLUQ/\nMyRvXQZi8TWDPoaGiUBU1RR1jZQlUlYhqLXrKNbr4ImPMNQv9YA4wyPV03DPSe+nl6oB8pJzwWCB\nWetYrjcsV2tc21GzK7vy6pZHcN4UxLcY56fa71UC+qQF5RGc108/2MJ5vUp7Rs7TyHnyMjhvWAr1\nzJzXW8HnyHnDOUcu4hHnCerR7xnnLRbnvP8SOE9iBWlMwagage8pOU8NKt7HKn7hnCeR8+QN5zyJ\nnDcMXT0b5+mE83qsyThvGAh7es7r7+cbcJ6+IpzXY90jOE+Q4A+JnCeR8/Q5cd6Lto9Pc71ttv2S\ngXjW8toMfO2/7Bt4gaWXsFdM21E2DcVmQycVzkaISPv0vX0IGpifJyluGNYvOxk+99sMaAG2MlSl\nobZCaQQrRCBKU9qlV0IeekjCa4xDmV58Pd4kT2ByziVPoB1uLDd+IyO5TcnlDzhp+8kAJi2ZK7se\nGC9V2uRzLoBJn8zP2Y/ch6nvm3XD6VnDbXODBwfv8ftv/5C3rt9gPquiBzVNfU9KMk21zqfohmuk\nWANpMncKnmgLQ9PBnQcd//1XZ/xf/+U+d847TjzosqY4qEOCESEYoVkJsxrVkKVHPaHeiwK8op3r\nX5LVWPCgbYQkYylmBcxn+HLG0eqC/+/OBW8fCP/Lhy3GKmXpQs7lablkHC5V4kSW2/bJ24GyVT9O\nBnrDKc3Q2AOBh1N5+nZ2WYsmOQy/e6QHon6umOoAQQzNN01/dy5m93Ex5oNTbp8J/3LX8PlFzUMW\n2Nk+Zr5Eqzk634f5HmosYitkvgjy6VrEFkhV9W80IbdzCKCJCuJAygKtZqgXfOdxmw23751w+zdH\nHJ91nK5WFGXJwZV5P7UbQl14oxNPUO+zCpa1f75xvYsYZt6hruW0W3DcOB60LZW0zArFpArJB2O2\n2SiNCw1iuw/TvQUvGgLRRvkN6EofAyJR6nSsX0UYpkfQ96/8Xsiu2Qs7AbDQ91MjgrGGoggdKtVD\n36IjuITD88ySklGaZEBkMLagqCqq2RyYIbM52q7pmg1+0+JdhyNma/LhZeOSju8fP1u+0Htdpb//\nEPchDHzN25bFesN8tUa7Dtef51uzw67synMvWzjvRcP4k8qEsr552XHeq895XOY8eYM5TxPn/cfI\necXAeePxhCdwXj+G1A8QTGHtG3OeJs4LeCEoRqect+VEO8779pynWzgv/e9yxT8d5+ljOK+f1/oN\nOS8Ju29+T8l58oI4TyLnjV9OiCo+xfF6NOfpa8Z5j1DqL768NgNfL72mXmDpn1UVXIe2LbreBKNa\nxlTLZGZChs9Bgcjg2WDwtCkx9gNkqa41xoMArFBYwj8JEJNiOJgJjIiASfCjMSYNgBGUEBvCivSK\nIx2qEqfAa/QG9uCS683MZiKjTVsBpld2cvm4pHUHtwHjVc+T/XoYyiqVDKaidyqktvb4ruP+Svn0\nooAfvsON93/E4eEVFrM6ps7NvH9GMCm4ZPYG3qvwGFt1gCKlKELMjYu155MvNvzff3Wff/ntGffV\n0iwrTFnCvEKqgpA3F8BDaaEM02/Boi5kfpHCoE5xBsQaxIZsJl5D21INACY1mHnwYDTnM+6s5vzb\nquKvvyz4SznmZ3vnBNQt6M2WjM3sJfk9FnDTPtm2XlZpn0cEa0y75E0oxf1SDZ8htre0Y1xWIKGe\nkxM9OBtDMqjUbocWoqNmkLyAAYgU5xyu86w3ytnG8PFxyT8c1TywSzhcwHyJ1guoZjCbw3webt6W\n2FmJGINrFGsL7KwMznJNzdeAjds6F72HgBqkA7OZo6aGas5Nv6L7dQv1A05Xnl/88TUO9ku8jzYU\nHwErAclIYCgpsOu4v4sYSlfRVTUz5/Dacmu9oeyU674LcWAULGEZjMm4diLpsXNeezgesXCOxsHj\nNdmY9sv6vWTtbPQOlJ4hgcPkNOkewzKT0E9Fwjmcy66bdEn/PAmWB89pn+baxGn0hBdKay1lVWJL\nG14uu1XItHh6juu6ECza+xA4WiNgMgBbumauAROUSbqvCGCFQu0ci9WGerVG1uuwxIVd2ZVXt+w4\n783gPHac90Zw3p+8IZwXK3zHeYPAdpy347zvbdkNfL2SZaIV2hbdbJDCYKSg15QwjI4DKQjpsCqo\nz2HXmwElZiaRIcuPR9HooCutUMZZ6takLBYycrAlxdGbB80Vq/Qg5YwJHpXkOZNEWWZIndGXNDKm\nE7CB0bTZS0CU9s8BabqPbP06eqB8n5Fx1eGPRBjy4Z/vHO264UFX8JUsqA/f5vo777K3v0ddlQGG\nJlPf0/fc+PTuJYnySp4HI9iiQFX48taK//5vZ/zdv57y+YOWdVnh5xUyq5HaBlC2BGl6DzYuebLh\netoFo29Ki3eBvKSwmCJ6A1VwLmYb0bjkgRJblLRlxZnUfOxK/up2yY3Djp+6c6xRjEkw1Fc+PZxO\n5TK2WENDnbaBJISUpaYvuW8oQk3fMB9XzBC+o39liHU1Mpp5n5L4WBmE9ZcOMvOpHfiQztq54Bk+\nb4SbZwVfXMz4ol3A3h5msYR6HmComiGzGTKrUBXEFsHzKmEeuC0sZW3xPngZ+wxb1qKOkPq5Kgjq\n24ATTK1oVaPzGccPjzk7OsH+ZoWiXLs6o7BLqjLGbnGA8YhPsQ3ic5nxY/afNcnUU5QVpXPU3rN2\nLSebNce+Y+U6RJTS5L0098KNcGgs81E/zERPBoQJmEjnl/Fpsv7b75m87D3dyki19K1Tx2dJfTUF\nc/U+SzmdXzTzaIbP4WaNSAZeA4AZE7yL1WxGWZW07RpdrfH37uI3eXvyfcpr6WkkFiElAAAgAElE\nQVRShple8R7T7aRnTS9dYgylKnXnmK3XlOs1NC049z2zo7vyupVnaJ9P2jW39k+0EC+nPBXn9UM0\nSa1lnKcZ5yVVpq8R56V3yLjt1eI8dpzXc548I+eJaMwDSN9Qp5w15Tztf3g2zhtajQnRwjPOC8VP\n5iPpYPafzHmacZ5EztMJ58n3mPP0O+Q8fUGcJ68R50nGeTrhPImc920C2r8Ie/lNzv9c0fW1Gfj6\nPhfvHNK0If2qKFiJsKOZeQjtVaGP+TBSahAVbgCg3huohI5mQQrJ0lnHUA2SRtDHL7RED2FKyzr8\nL0yvtcZQGIv3fnIPkbxg0vz1EZ/JmrwZbxxB0jYg2nISzfbp73lquCfHjW5NSfG92qZldbbm2F7j\nwbX3+NHVt7l2cMCsrigKi40BXtNM215hJcWu2k/tTco0AZ6Jyw5sUXB64fn//+4Bf/U397i98jSz\nCj+rgwdwViBVACJjCffmXO9NtEWAMB+vX5SCj14yU4SpvmKD99aq4LzgVTAxXkcZ40V4EW42hv/n\npOZnZ+f8b+sTFmKorKOPAZGqOXokRnLI4faRJdO5U/u5bWljD0iZbHqXSW7lIq7HWF/Zjfb7DrcX\nW7jPelD8mKPYdPp7CHYajNrxpuA3D5fcdQtYLjCLBWY+g6qCsoIY/NKUFgje2KKKvvZOsGWQk3Mg\nXkLMjhiw1qHgowfXFqhY1FtcBRRFiBuB4MqCL05O4Ddrrh3cQd1V/uijt7BoBOIwQKJ+6hUUrILa\nPJtRAimPLWrK0uNcR1fWbMo5K9/yoNkgIoHTEgwnmx27WP6u0dfuVEyZZH0iK6X3BIb3hmHa+WUL\nKtnJxv26P54EPRm3o8N7Sf8IUaeqHy99uHTNdPY0DT/26Mw5oVFvIFCUBbP5HHtwQLe6QMsyZk/T\nPrNjCKQLahmNFUMGQwkVJ4NeRoTKe2ZNS7naYNYN3qVAxbuyK9+L8mgV8YqWN4nzdMd5bxTnlc/I\neaqXl77paKcXz3l5yIId531vOG+yWvCV4Dx5hTnvZdvLF2KzdwNfr0FR5/Fti2ktWBAsfeRTSUp8\nW1uRsQKSCFI6xHzo+SB6raxJmXmyUWxJU96DoRMx9Ilso6IzQq8Q0tRXH7VN0Dc6aB8TDdRII8oj\nPqfvl5+tvwHNvz9p/+x7sIDbryEw0F5UrIMVRF3HeuO4f+5YH+xTvvUBi8Nr7C3m1FURMobEulMM\nkoI/psq5BIOSzThWbGGpyoI79xt+9/kFv/rkjE/vNazrKgQ0nVUwK6AuMJXBlAYbF877TnvjHPgz\n3LsI2NjjxQq2CP/EBtmD0HmhcyHziRhBCoM3BvHCmSnYrGd8urnKJycXfGjPuF619GpkrKe3g2na\npLns4oZeHlNzl5lS3XaedLwMSj+/dj+zLIIaKeCujCAonDZBqm5tRel0yWh5T5z+7uk6z6YT7q1L\nfnW24I4uYDlH5jOkrqGskLKEIgCRLQJoiDUURagLYwRjg5z6PmYlLi3R8OLig7ylMKhYPBa6kK7c\nE6ZuYy0XD5WbJ6f8/T+eUmP4wXtX2FsWWGtxPsQEyadPGzExLoRSSBENefBOKibAVAGFF4rOU5SO\nsm5pmw132xWz0nNlar2TKFI/63lVh66aulcvT+3FmVyCPRRpfo4MunJR59ft5TroolzamRol1Xju\nuVdC5p3eHx33N2k/0kyJ2J5k+JzaeGqe6RmCR7BA5nPM3pKmqvDWxEEv34MofX1E3drfZabmkgKO\nn0WCdZh1HfNNQ7HaIE2bVcyjWvWu7MquPEX5zqB8wnn6HXCe7Dhvx3nPyHnyoT3j2jNyXt4ccs6T\nOIlH+yxdz855ktamxQv1ez+G88Kuvf3UyHnymnCeTjhPXkHOi+NH41mcL5nzhqNeDOf1Ezsj50nG\nefIYzsuyWn7vOe9R9vVJhuKZym7g63Uo6vGdom2LWMFIjO/4yDYyNf7boSjr32G9s82y0jDwS1IS\nJqa5lhjbwWvQXZI6OokxwmcnDk9Ywyw66cRGMqOUg4eOm/XjZghJ9pyafZ8+fKoAtuwzgiIGcEv7\nDq6CWGEx+HTXcdE4bp2De+eQ6+9/yP6VK8zqMmT4sUkxhunlQUemwZ1BcjpovP4mRYSisMznFZ9/\n9ZD/92/u8OndNWcimFmNLCqkLtDKQoShogxLFlCDN0Dr0DYCUbxuFF/vpDM2eJ1sEYywGLAd0Ibf\nxBicN+H4TvAUbArly+4av3zQcbBouL5cM4rJMFXS22SRb+orIgOgnl8yCJVsH8lSWpOW/Uq2nDH+\n5vPp65kl7eNvhB6QJUrNnoFLTTHYp2Gpq2rI0OJS0NNOuWgMtzYVv1zNOS7nsJhBVYcgpkWJlEX0\nAgYPrIhgbXSOKxirmBQbOHYRYwCTpsMHSLVl+Kdi8GoQgQ6LNxYxBpEwhf7sgefv//GUmTvhT39x\nRvH+HnvLCm2DoRdiRkVj8CjiPdbG2AT9MzuUENzYqkV9QVF6qs7h64bO1dxZVVzzLRLDavbjXDKp\nwwnrSpJbUkgMgz5j5k3wqr1c/KTJTEsWjnR0vnCejN6THkrXNfR6UBXcsLKchCam15HpBTJovzT9\nPVzdRIzJrxWeRQTKukIWC5rZDC0KfNeENNc6BNidlhEMJf3V6ymDFUMJzNuO+XqDWW+gbbefbFd2\n5c0qmUZ4ZjjOj3055bvhPMks6KvOedtl8N1z3jD9t6/El8J58undtb6qnBfXEG5/Gd8mi7hLemTp\n5fzUnBdHgZ4P511qWTvO23HejvMuV+fLtoMvoOwGvl7pkqkEVdR5pO0whOnLEr0Do84xOu5yia/6\n9IZABoXT/xfPF8M/ZgwRjvb9wFnoiCKCUUAGL2M/cp8BSAKyISDlWFH0NzQa7p/WR8a1g2bYUmeT\n71u7cqa1s8uHzzrR5nFKqw+BTjfrhjNnube4gT24wVtXr7K/DGmtjTXDs4sZFGKKHdGfNCXL1dFa\n/KIwNC0c3Vrzz/9+wt/86ph7rYX9MO2dyoZ01oVAIVirMS0vPUAYD8ZHo6ADBCWpGqMYq9iC8NkI\npgi/WmWYAViGv21racTgCvhXf8jizPDDzQN+z51hrCKmT/w71KnAEAh1S5vMjaPE9pD+ZoDYyzk1\nrnG+P0YkNJVfKtOwEUYI5KijGV+hO40jUqTrJC8NGl4GfPQAOheWRKxaw5erBTebJWtboVWJrUJg\nWikKKApMH29Dkg3rwSgwfe5N0oHzZbBpxgbvrSnC9GfSS4kXRAWqEC/EqOCdobnh+apZ89d/c4u/\nbN/iz37xdpC3Ct4bMOGBQry2UHdqwKqiljAVGw0xI6JcClfjXEvlG9bthlWxZoWyco7KQplkIXlN\nDvSSgCt52Ib3mWGGVJKn0QA/GmFi0FnptDJIaaQTsmtPdUXyRCauHrWdAXbSEgfRLEOSah/UebhV\njTIcWo+kmIBRf0ty70n0+BoLZYGpSpwtoGkGoNfh2HC+oe76bRnfYwxYS6HKvHXM1w31agObBt+l\nHD+7sitvfHk0/Dy+PAr2p+f7Dl4KnorzZAvnbXvW7FW/5zx50ZzH03He8DDDak5eMOfJlPO4zHkS\nOU/funpVI+fJd8R5wn6tbxLn9Tj/7JynWzhPRpeZcJ6YWBUD52nivFF3+XacJzvOu8R52bDyU3Ge\nREWlSZ47znslOO87sG+vXtkNfL1GRZ1DWzAoFospzQAZMOokSVGG7QwKVZNy738h4ZAhH8mO503a\nOO2tORAR1xqH6ZumD7o6rHtO00F7qhGJQU8162Ka3wpjxTWthLSfcFmTkTHTxACPzrMFklJ9jbbp\nYNhT3XmP7xybiw1nfsnR/ju8feUGbx0csJxXFFURjWp8diMZFESwTUpYQ1BYVY+PMGSNMKtLjk8b\nPv7sjH/57Sn//Mk59p1rFIdLpCL02kLCslejmOhJMpagUK1ifICiwahKvI1gPUXCcdYOt5igyPok\nJ6EqQhyITWNDVqhC+E1b83A14/9oPuc/uvuUHqz6sQyFON1/aD8jaO9haAq4kVpG7BgP8v5SuIfR\nhiFd0iPeCSZtIA1+STx23AgZ7r5vAKFtx0sMHkCHd8pFZ/n8YsnNdoGrSkxVYiMMiQ2VLdZiChum\ntUePfgrKmTzqKShBmpkZBBQCrYc4LUFOUoT+rp6Q+tqGPqwa3bqmwpgatYbb69v89d9+yeGVip//\n7C2QFPDUE8M/xGqL8Vp8ymIe+3PWjhAougrnatTP2bQbumrBipaLboMxAYiGLpoM+qBHjBADe4a6\nnaxeuCSqflwzbw+afxykNCZj+skE0+0DDOn4l9j2THx253zfp/u9o3c6gaxq9LgT36UgZkBLulDj\nXcZp9kYwEtLOSxnbR/8ikN27MiwXyO5v6BqxlcZg0qWHRdsxXzVUqw00Dep2A1+7sivfsDzLi8CI\nHr7xBQPnSeQ8jZzXp6R7gzhvqK8Xy3ky3jbiPB0cnE5eMOfp94XzBJXUuLRfb/h0nKfqNec8udQI\nJm3g23Ge7DjvmThPUr/OOE8i52nGeSN/c1/9Pv/tteM8+YacpxPO6xVxxnk64TxZtJ28Zpw37afb\n7OWz2NtvXHYDX69R8V7B+ej1EYwHn4JomsEhOAKjDIrSCLJiCMvF0tT02FljBzcyTPHs26HEKfBx\nPFzJUlj3+wS1YzQoAyMpIN+kVQdaYKCXpCu32K9pdxAZBjZy5TiQ0PjNOT9ueuLskP4UowGw9Dlu\n0JBJp+scZ2tPU86Zv/ND5odXqUtLURQYY1E/1GW6z6SMVSRAEEQY0kz5Er27hlt3G/7bPxxz6xjs\nwRIzL6EUfClIAbGSkR5OFYyJU94NxmsI3DmJ8dlDaU9RZnjueM+2BJemBceMT0UllIAawVHSYbjV\nXuOL9Snvlycspumbknz7at8CQ4+SQ15yaJ7KcAq0OcjD0PhH7SS7rib5xP4T3U1JJvmueSUmI+W9\nRm+gsumE067g66bmntb4qoAqeP6ktEhh0RhgNgFOqF6JcTfiJY1BrRnkEvssEr1hQgCZkI4r9Gcl\nxJ1NLjObZBriq0g7Y9PsceviCp/dgY8/fcg7by052Kto227wcMZ6k+itFx+8xNaGadl9FnUD1hqs\nLXBFRVnOqOs5rVvxsLPUhTJHg54heTbjA3od2noGTOF7/gY3biZDyBQZRJt3+Wz/be8+6XwpZkPa\nkM8MH3TT0HdVw1IBB/1CjxQM2oiM4uH0/+KJQ50NvsH+utJfJuhba/HWDPdAUjvRe+iDWE2C50hE\naV8vwStpEGZty3K1oVqvkU0Tluvsyq7symtTdpzHS+c8dpz3xnIeXkYyyXfNK3HHeTvO23Hem1l2\nA1+vRYlKwCuKD/DjJK4Pt3gzKFcvGQzBOI1vn+Z3UFJRxYeryDD9tu+wJIOeOnT0NmSn1aQos//n\nx2+FodyAPfqRswfJP08M7aUDthjOHHby/R5ljKe3FqeW410AokZoF0uWb73H4uAKZWnDmvm0hh56\nQ5crwnT/Ei1Cn1ElQqxXYbWBr+40/OO/nXL3AuzBHjIroTJQChotk5hhfYAneCJ6uXrBOIkxOkLm\noH6KbjYDRaFffqoI2LCvd4JP31WwpVIQjO0Gi3OWr5tDPl+dcG1xzmKYHz0ASQKRwfI9uowaSbbN\nT7ZLvjGXT3aNHJrTsdsCeUkgDIl/+0w/l0/OtJEkQ+l9yPKz7izHbcmtruJIS3zM5iMpq09yuaY0\nSjEzCzYEPQ111Hek9IqCSgharwhefAxEK+EYG1JWK8FDqOl9RSQQUswQJG5Ju27Y6AVf3Pf8+uNj\n5rOKa4cznDMBnFPfEPolKnEhKEYVa8K0eAHUgjEWYy3WVhRlTV3VdOuKk67gqjpUk+uOoR1onLSg\nxKfLpnFrmuqd9tdBdIBopteSaKd6gdCWhy7d083l9pJJtG9al/YYpul71YyfU7yHpMb6eRSXzxJP\nngIg93cl0jvxki4cJuXTQzde+2Ramq2YGd9meOZSlXnbsVxtQrDTTRteorPn2ZVdeUPKG9iwn5nz\noiaFFFMdeCU4T19tztPsp7EF2XHelPN0wnlD5T6G87bRVi4y1ZEMXgjnjf5yqfUk4ed3teO8Z+c8\n3cJ5/eyup+C8ft9vwXmjvVNrfUmcpxPOkx3nbS3b3sK/s7Ib+HpNSrITYRa2h06wEjyBRIUqMVOP\nj1pFUidOCshIDFKque4l4c+AQWl77PwJoLL90rRSH0ezEqMkr5QyWLb+rGnkK02N9lOIGWHWcC9b\nWWdb587g6NLFJ5/TinTNtk91R9DE9AMf6tGupe08JzKjmR2wf3iN+WIZ0lpbQcRgwtzzGCR2fH8S\n3SAajXY/9VegKISLlefmnYZPvt7w8e2G88UM3Z8jcwO1ENwxweumMeKsJ8jVq9I7a2N8gOCxlT7N\nj3gPvkO6BjpFC8GZCpEieqjACjgJ57Q2Bm01HlMaysrSIXQb5cv1Pr87u8JHV26DNpCBVl+J+aCX\nTOSp23RdDo8Cxk+aRQY6w8Yo87jdmHHA04RhGZCO7i9rC+FrHJTIGUjSlHQlZflJQOS957StudfU\n3OsKTtTgjWCLlJUnyN2L0NtMY4JMigq1ZZz97DC+A6e41uAp8bZApQjXtz7GfTAh1b0ENHQKPsIT\nNtYbBlIAVFPhHbBuuHP+kH/61Rnvv3eFD34wvAj1GcM0A3hjMKqoGWKFEPWBsRZjCozxFLaiqmq6\ntua8rWjYkGYdXOqnQky4JMTMSuMmkQg5QW0SQ6/HGMW2HYlo1BrGL23pGgE2dLryov8ik/tNMk/d\nKokveU6DavUZFIW676GJkD1ppB/TA6VrOI/3GqfMx22asnV5jJq+Owy0FN2DlhiMNsDQYtOwWG2w\nqw3atDFX+K7syq68DmXHeUw+7zhvx3n9Rp4X58VGv+O8WI87zttx3vep7Aa+XrOiqqhTRDzWgHPB\nWJFST0sEHAmKLawn1xAMMaook+J5SmbzJ1CURrhzA5IhCj1GRb2VFNCQ+UWw0bCk/cYewMxQXn7K\nYZ/Hlkf8PjJ6j9r9sqIen2DyNZAormlpO+WiPsAvr3Kwv898PsPaEAPDmBBIUqD3BF7yMPWKNE2F\nD/VdV5aj44ZffXzBb7/acOQMaiy2LGBmkJoBhvqp7Ro9FRKzt4CxBgqLd4K4DXazoXArbHOB2ZxD\nu4Zug61KTF1DvYfM95HlFXS+h9bLANnEwJoajboPRpYWuha+aOb8drXPcVvzjl9TwGCGMjHndXBJ\nLrkVG4TXP1tv9R9bZPx35AXMPzMB30lblNT2H2FEIuv5CJ/eKRqnwR9tCu5sKk6d0EarKQakCFm0\nsMFTpNbircUKGFrs6hzTrTDtGdJcoJt19PZZkAXGLvGLq/j6AKmWqJ2jdYUWwbOYpmargBrpvbpG\nCqAAZ9BCkGYBB1c4Wm347ddnfH235Q9OG+a1DdAL0XgOkOS9D7FdAGM8RoOeMSjWmOgNLLBFSeFq\nOluzNjWNdnTqw/TwXEpRnygaASfN4tJRs+hXLmRykyiu9PKQdFbanjPvVNT5XTxeo2h8Scm2xMxO\nETFHTSndS/CsZ0tA+ozek5fM+DI6uqIqvutQ14229eSkgnrtlzdo8ij2+j3Ip3Seg65jsdpQrdbQ\ntjHmww6IduV7XS5ZmBd0LZ1sy8u2d7XRvk/gPH1NOW8y5JRef2VbnWypni21+O05T0dfA+fJjvOe\nnvOG2T7hmSWri2m9DzO9tnJeftCWPpLqN1KDBmMdkKIfydHHcJ7Qz8jpIeJy2XHey+C8tPubznm9\nXcg4T3ac963KxK48uewGvl6Tkutxr4pxPijVONPVGRtiAcSUt2kQTJPiIS4t0hiXQdPIdlib3CuO\nrMNBHAnPrp/PUE5TNsP1skCoIhQxFoWPOq1fc53x0KUnGxmqbTUwXPmx7fuJqCmPuMRgnft7TGyk\nGuJ7bRo2ndIsr8GV6ywWC+qqjJ6R6AEwvaoOUJRr0WRw4vWSUjVGqOuC8/WGv/3nY3791Rq3nIVp\n1OIxVYHMIm7EoJRJB6uEQLReFSMWtYJXg/OW4uKE8uwW9dFnlCdfYs9uou0KdU1QqLZEFlfh6g/R\ndz+ifevHNPUSb6InqYipeisBFy29VTpRvmhn/Huzx71uzgf+gj1cj8UjWeSet23b+zqO2/oYAFEI\nRukDjUxO358sQf00hkiC+tHlJ+ePPwav7EjwfXNIbTsYwjT13aHe47xyd1Nyc12yCQIK/yTIX6wE\nIBKLmgJnywBD3Yry6AuKh59hHn6CWd0H3yLqA+gW+/jZddrDj2iu/wR/4w9guUTreZgOrx5vwMkQ\nFwJMCKJZlhgpwRncRQezEtk75GS1ojk+5cu7LXfunvP+u/vM5wXeg4teb4l1JDGujMFgLTEWQdAk\nxhqMNVhv8bbE2oq2qGltTcOa1reU8aVnHLs2W5OTidaYOKsgA4aRNZPAB73DMmPmtK+wpWmMBd+/\n9kWJ958TO4+KBhhyLoISQxvoj9IhVog34UU1+u2yeBoS06JnfT81LO/RtkW7dug7sR4kXj/jKUTA\npZdOCZ5ao1B3HYcXDYvzFXa1wXdupKt3ZVd25YWUb9Xp3hTOy8e9Lj2Zpv8/adTqO+G8wbP16nGe\nmKrQN4HzIonpaPsrwnnD7K/XhvPEGzRynrymnCcedMd5O857Fcpu4OuVLuMGPeqEHtQpVgqquqab\nlbgqpOcI3gmHuPByLCQoCh3KSJjmXCBYUsBN7Y1KPx0WhqmcItndBEWX25deXeS2MLNtvfdLhOAi\nGam78QNefvRJGRHVE/bLi/b3/siSDGt+AzoAUbNpaVyJuXEde+UadVXFYKemh8lhjbcMgBlPq+pH\nlwoyCXV5cu74+n7Db+9suH3mgvctxXvwDvFgK8I6f2/QzuO7qDBNmLYOIT1xcXaH6v5tioe3KE7u\nUFw8wKyPkeYcdQ2qIQOIaIdZC+YY0A3m7Bbm3ie4ax/gr70P9RWcrdAiVINzwRh5azgRw12/4F57\nyGm3ZsExZgpDvWwnMstFIBDTRMWKkQFKe5nFHcNcf0bTwDT7XVNA33jsyK1z6UMGbAGGkke7fycY\ntcN8CnyYBt85Yd1ZHnQV93xFKxHgBBSPVx/goSpQCmy7ojy/RbW6S726i7l4gLk4Qi7uQ7dC1JGW\nEog/C0sWVDGbBxSnn+He+jH6zh/SLa/R1Qc441GrkeNiNqGqwFQFeIM2hDqzgqkL/GxGUy759E7H\nv31ywfVrexwcWDrnyVQAEIIqYwjPYARrDXjBxKUbxpjgKbQFtqgxRY0raxotWWvLDOnb9wiEEsAG\nxRT26oFDxi8ibIGhSdMKH2WLeHO9lV700iyIRBkD6Y6apAz9NweStMpD+htIsx1SavsQbyO9aobk\nVRK9xz5mskw3qOAiELVd//I5tDEfzy3g022GbE5KWEFRi2GusN86DlZrqvUmpsveluHnCS+Su7Ir\nb175tm8F2zrM48756PeyR5ZvzXnyGM7TjPPkRXGePpnz0nv3Y8p3wnnhx4HzLo18qPcaOU9eAueJ\nePQV4Tzx1ug34by+hXwzztOM84YzBM4LpPAtOa9vhGPO6y39K8Z5mnGeIlZeQ87TF8B5MuE8GWaT\naQqUN9IKT+C8LLukyDfkPImcpzvOe2zpzd6LuNhrM/D12onxWxXN/r+lxI6tGExVUe8tMXszulkB\nzuOaBlZrVD2m1f5Fvg/LJ2GJeEn4a9I6Yw89j6jGzDHRyMcsPz1QJAOXjW4PY+VsH4UWQSW6LnXI\nFTTeh+39dgs/PbJVjC6dw5bkGn/7dR9V63Hga7PpaHyNvXKd6so1yrKgsBaxhjSFOn+xDtk/hj6t\nYgbU0jBgYaMX5MHDli/uNHx61PFg7ZgdFJhKoDKo69BWMfMiZP1Rg9+kelS8iaCkinQtswdfsPfZ\nP2CPbyKrh+G6se69mYUsIBEGDJ5idQ9zcZPiTk1VLOl+8r/iZhXdYkFX7qHW4Tuli05ItcKFsTxg\nzt3uGifujLd5yKV1iQk4+jbzKNlFeMllkC9TNJk1NICPEQDyfXrLlUg8hyHN2lbWJrYEPR2AO6Oh\ndHkNVR7gUGmcsOos933FA63D9PfoMPWqOO8orGDKAq+CvThhfv/XVEcfUx5/QngBMXgsmKrHhpDa\nWJFuRXnyO8rjj+FWRXv2Mxpp6d75E9rZISohKKZKmDZvygJbF0hl0Q1oG7zEGJDKIPMZfrHPZ3fu\nc7W+4M9/rhRFMuZDu1U0BGX1Q9VYK6h6BB/jt8YYEL7A2Apb1mhX07YlG7XhurEqI/sMMkm0aQTx\nOsoyPtR3uhfGMNR3JxmxVag3GfiZyfnyYx9bBhBLqepVhixPAZXyJRPRXyoWxCIS04yTICqDaOfo\n01YraPQE+s4Rk4qCD2nvHcPLkjLE+QDAWNRYZsawUGGv8yw3LUXTol2C6stlwMbvl0XdldejvAGt\nctrxpt+Ht+7nx3lqWpVXifPYznmjd+Jn4LztL0XbOW8wCtvWskm66R3nfUPO08h5Y8ld5rzc0Ge1\n/cycp5HzZGgGsc5D6szRUMtjOE92nPfSOU+2cJ7uOO+pOE++p5z3NANhz/yQr83A18nLvoFY8hp+\nIUOT6boiyKzGzGvsYhb+LeeU169Q3LhCtT9DZiXatbiLFd3JGe3JGc3JGc35Gl03+M6FNLMapqTb\n+M9An25XTK5NMkZD0SyYX+rwIEHxEafZ63C/CogxqOoAViPbJUPHvaTBHlcZ2U59/37MQZr9toXB\nht+iau3Pn64RLKGqsvGWRmbUewfUy0XM8JO8pcESyuhZJTMyAtEzq5nCMtbgWuXm7TVf3l7RqENm\nFrM/x5QKtKgJ9sF1XZBTGaalixjUhfTb0jXUx/dZPviU+sHnFKd3wHdQVMMgQgQ7TcEQRfFicGKH\ntMluTXXrl9CecLr6n+mu/4Suvk4nJR1hPbkBdF6xrgyf+WvcbI/4sUIZ/QnOJkcAACAASURBVBV9\nHadqnA5OTXvSiG1l4JHRYGQm8xQXYnpcv98ErhJsTbP+JFn3MJZLvqfk3hirah/sVNWzcYYTV3Ck\nBQ/F0oU1JqAhGGrnPaV6THfO4uEtiqPPqY5+h1mfhOl7PcTFNqGDcyrYWwNS9TdrH35O/W//J5zd\nhdU5m4P3aWeHCGBqi5lXwVgCznl8F8G3sFAbWCzxrXL/+JQv7jTcfdjw3ts1IgZrfMhqA33dBiiI\ncBSsdYAiAWNMWIJjLaYIMSC0qGhdyUYLVLrANVFUiWpG8JLJR7Lf+leTuEuSx/QVaARD2efc9F9S\n2olGHlV0LO9eJkLs46FVaPSIJo9hOrVMb3KiStKOYVq8j17CrH15xTtPp0PdeGPwxqJFgVnMsYsZ\nxd6CclZjjMXVMy6KinKxwlys0bbDty2+bdGuw7ttnsHvV3n3Zd/ArjyxvOGc90RAf1M4b2zmXyzn\nyfBx+0V2nPedc94wM+fFcp6IxMSBO857DTkvSmfHeS+R8zJhvZ7laTjvtRn4On/ZNxDLpKk/x7Ll\njCJh6VJZYOsKe7iPvbqPuXoAh/vI4R72+hXKaweUezOKyqJtgz8/p3t4SvPgIcW9h5gHx3B0Chcb\n/KbFdCGAoVG/NfPPYAwm2CDx90wJBG/WsG8+cp3Op3iMDJ6y3kD2S9TSDTyRC/v7GD5kAxtPPmBy\nmIy7ebJ8cvkQNEBEQ0VbLKiWe8zm8xDs1IR/YxgaADCllxaIU1kzL6gqxgjOeb68teGL22s6ATu3\n2HmJ2A5Mh7ca4KULxskUFmK6Y21A1i3l6oTZ/c9ZfPkvFKsjpL1AxaKmJEwxll4+Jl47WJvwzBoN\nuXiHffglcnEf7cCtO9x7f4orDnEECLOF4IqSdVnylV7ltt+LXoqsHeew0ctoRMRZvW8TVy7fdL+S\nwdAUgjK59rLNeuwlkM4BeHKvseX2MBSBRUnT3wMUrV3BSVdyrAXnxoRsTCaBlOJU0XaFaS6YHX1M\nefQZ5vw2quBN1T9TirMQghdHY5usp6bYrYq5eIB9+FlwuDrFfSC4egH1LMR3mJeoE7QJRtWrD5mf\nqlgn8xm0wun9OXdPVtx70HB61rC/rDHGBA9xXq3GBB3hPalnx5iy4UXASAh+agYg6tqKjStiO3cD\nACeRpRqW9Fyp2mXEq5JkLpnoYaQ38iU2eXMIh4Vn1kzGY7QeNzwll3O2tf8+tN9+Hxm2j1vj8MLR\n31BPQ2lTmBIfsjXFYLoJhiAE8xDB2wBDVBVUNeVyidlfUu4tKOczjLW0yyXniyXVakOxWsOmQVcb\n/MUFfr3GrTcxuGoCo0d2ul3ZlZdWXnPOe9Lu6dVwcrGe8zTjPNnCeVruzbCVhW/GeX3InO+a89LE\nqwnn5UGAnqxw+ilUmVV4Cs7LXoTTkkYd3ewTOA+vbALnaeQ8iZwn3zPO00dx3ihSUhJ2xnmX8/jx\nGJNzifMCKYw5b3y+x3BeQIEUxy39b8d5L5HzJHKeZpyXo1sCNp1wniS98S05b9TLn4Hz5Bk5TzPO\n668ZOU9eE8571AGv9WDYtLw2A1/Ny76Bl1BsXWH3l9i3rlK9fY3qveuUb1+junaF4nCPYn8RRoTn\nFbYwQWF0LWa9obxYYY9PqI9OmN0/YnPviIt7x5w/OKF7cIZpW2TTIHFOpfc2KGCJo9sjhQIRhUZj\nGCIhpbYkzwVgjUkD6bguBIUcAdQ2b2D2Zyg6/pgflx8w6pbxi8m3MYalsUYclL/m++VQFA/wwdvW\nlnO6+RXm8yV1XY0GvrQ3xBJTRg+T3yWvOBXyrEiisGmUj79e8cmdBjObMS+FwnRoDVpajG9RumDs\nOkU3HplbZFYgpsOuV+zf+TWLu59Srh+CuhgQLE6/VcFUM8q9A+r5gmpW47oO1zY0F6e4zQq6DSKC\nLQtUO7rNCv3X/4Lc/Az+YoZe+ymuuEpRV1hT4ilpBO7IHkcs8WoHqyJ5HaYKz/9lv0Ww7jf2YJpv\n00EeIpPljulkaR/JjonbR/JluL++DUzb12VN309/98GbivesneGkq2gJntSQ8ihBf/Ce2QdfUq5u\nUZ5+SdGcxAAew20hgqn3KOd7VIs9QEI8uYsz2tUZ2lyAdqEPFTPUFBTHn2Kae/iyQBYLNld/Ass5\nagTfONwm9D0MUFuwIS6MaYHKo8tDWnXcvr/h3l1hf1FjCosLCX0if4YaCLAP3sU2q0PsB2MIqZiN\nxZoCb0ucrWi1xNEO1Z7JehRFJsrLGMW7LSz8qL6rGRelZkF2sMhkFvgW/dHLKV1GSfEX+ne0uEPe\ncjVrTtr/LgQvqcN7wYgl+TbT+bK1LxChuvOe0LND0+mcp1VF1CJlgZYFWtfIfIadzTGzGeXegtli\nQbWcY2c1vq5Yi+FYlbrzVF1H1TQUqw327BxzfAoPj/EPT+hOzlB1o5kIu7Irr0rZcd7jOc9EziNy\nXvH6cl481avHeeo93Y7zdpy347wd5/GNOU8hBP3acd6rV16bgS//5F1e0zJumGIMUpXY5Yzy+iH1\nuzeoP3iH+ofvUL17nerGIcXBEruYYWYBhIIXQUOWkK5D2haaPezBEr16BXvtgPL6Ifb6A+ydI+ze\nEfrghPbohItVi7QOP/GkDdO2Bz1i+o6f7zcY9WCDeizIE79GfZS8Y9KDwXjaeV4nE+uUw9N442T7\nZcPWK9UcfraVZHS3OGXxDu0crl6iy6uUszlVWfYwJMZmCjsFQM0VvPRaVOOzGwm7th0cnzk+v9vw\n9ZHDLWvMDKTw/dR3CkKmFfXBKHeCqAWBoj1ndn6H+dHnVKe3Mb5BEXy8j7Isme8fcHjjbd567wcc\nHB6w3FvQNS3r9Yqj+/d4eO8ux/du06wucF1D0zia9QX+6CZm3VB+9nc4Ktp3DzDVHFPWGGfpPNzX\nOffdnLXOmNNuUSrJ4sggi20GSvXytm09v2+QGjxiIwiaXjP7PDICMsgkyWdkHbc3EtUQ00F98I6d\nd5b7Xc1GI0XYCEbGYMRRNCcUR19gz74AdwZ4JC5JMKqUsyXz/UNuvPtDrt54i4MrVxFj8M7x8MF9\nju7f48Gtrzg7vo9r1qgXsAXSPMCenFLf/iV+/xruvXdxsof3BueCN0kMUMVgFGJCu+4MZmFxewc0\nm5abd29z+47yow+hsgYXoSfVqfc+/iV8jjISI4gPf0N6d8VYi7UF3lR0psKzChkbe7TQXl8kGkwB\nUVFBkhuw7yuTik8fk/hGLzP0V9muF8bNZztI0b9AhCaRtF0KRko/82x0fhUGrSd9MzTxw5g9tCcq\nr8FT3Cg9EJmY4lyqElNXMJ9h5nPsYk4xn1POZtTzGfV8TjmrsbMarSu6omRV2LichbAspusoVxvM\nySnlg4dw5z7cvY9bXdCt12jTBmjua3V7fe3Krryo8gpx3vPqDElJZKPeyFNwntrIeXbCeUTOo22x\nzR4cLOHqFZ1wnjwj5/XUEzlvbImfkfN4MudtM/hTztNRFX5zzhvvmW7+6TlPt3CefN85z16qvOfK\neYPxHXNesMpP5jwZb8vk82jO67+oIjvOu8R5EjlPH8N58h1w3jC9VPIFzSO9cKkz7zjvleK8p71o\n2u87HbF7bQa+vg9ILhKCIxZX9pn96D1mv/9D5n/4Q+oP3qV+/22Kw33scoZYE3QcDL3Uu/CvsFAV\nUFcwq2B/gb2yh3nrGsV7N1g8OObg3hH1zbvI13e5uHPM8dE5Dh8BRgZ7kGkOgZGB71NkEMOhRnvj\n1PeGSHKtQ36CDIqAkVeG7GSjYy7V1vgGxxX56N2Ttkr/8mftb1VHtw3ag6bO3oaDa5SzWczyAyIG\n6TMYhedK6DgaKJRUv+F3Y6CwwvGp5+5Rx80HjrunSr1nMYVBa4/6Bm06zDx6mdoUcdPjOg+tsHd8\ni737n1Cd3cO0q5AGJD1yUTG/cpUPP/qIP/rjj/jzn3/E29eucLA3o207zs5XfPrlLf79tx/zy3/+\nJ+59/QWn9+7QrM9ZrS7QaobVDfOP/yuUM7of/glSGUxRhmCtrfKgKbjXzThxSxZ6QSF+eOBHwtCj\n5JoLLJiV3mr18JK5hISh3kmZfnKXUXa9JMsRiEe3jk7vMTWQcdsIA1+BnfGOs85wp61Z+QBvYkqo\nSrSqKFdHzM5uUZ18jpzfpCsqKGtKSW3Gcvjuh7z/+z/lP/zFn/OHP/493rl2BWsNzns+//oOv/3k\nC/7h7/6WT3/zK87ufYV363AvxT6gFHc/ZlZWtD/6CF/t07IIkTENSFFE4o5esdIgC4sWFtYdm+M1\nX9z1fH29DQFZbczgIyCS4k/E+sfjXMhII8aEbLEGjE/eQA1T4W1I4+20pMPgFWxSKAlKU7+QGHCZ\nmApbQp8IVxt3wwROml6+Min1Us5cjqISw++O5T7qk9n5NW9WEcqSB1Q1ppEnyN721wrnDypDIOoB\nwWYglcFV751Mn5VOlQalIXA0VYHO6wGAFnOK2ZxqVlPXNbO6pqorqqqirEpsUYCxOGNorUXKEsoS\nqYro/TVUqzXlyRnlnbvorTtsbt+luXuf5uFD3Ob7OMdmV17V8h1w3qW322+537cuT8t5WNPPynjd\nOC/N8HgM5+nTcl72qjytyG27DzM0dpz3nXLe4jGcF8hK9NFyzSprx3lP4jwp7n6skfPkFeY82XHe\na895z8sOjnryczjfcy27ga+XVsZty8xn2L0F5Y1DZu+/w/ynHzL78fvUP3qX8vohxdUDTF2GNf+S\nRpPTPx88ImrikjwL1kNRxA5SIfMaM59hlgvs/pLDvTlmf4kcHlHfO+beasWFOkxpEFycJgkRPIZR\n8Uy/eA3j/KMEtrGjp6czGftIfoKxm2ws4H7qMtvhJh0z+pv/lFFcXs0CQ7yJXA4y/j6lP8B3Hb7t\nkP0FZv8Ktqyw0XikfdNE2TSrTeO9mOzsohITzAjWCGVhODlr+epew0kDrTHBYFpCLnIbwtNqSt1s\nwrRvBcR1yMWa6t5N6ntfYbpNqHCT4jsIy6uHvPvBB/zZL/6Yn//JT/noRz/gyv6cWVXinWfdtBzs\nLZhXlouLc9YX59z56kvatg2BEo3FaIeefkn58GPqh5/gywK/OEA30KAcacF9rTn2e1z1HUtWl8XT\n26oMPHL2mLaFx03RnXp2JU6FT0Y3M4yji/cBBCIUjYiYy/vH3zRazATTmiy2wqkz3GwNK5XIbvFc\n1lB0p9RnX2Ob85jBRXGug0apFgcsDq7xBz/9KT//s7/gL37+Eb/3g3fYX84wEgIFXz/Y48bhPq7d\n4F3HJ5sLzh/eR7sGlWB0TbPCHt+m+vxf6XTB5sZHSGEwJsYbUQOlwRQFohbdBEgx84r2Ys5XD0u+\neuhYN449tWHJdHyGkKXG470Lns/IsUYkNbMsWHLIcmOMxdkS9SUOg09eyJiBM2iTcacUQlDk3uer\nPWaMJSQJhiJuTEQ56sUZ66bYK33bkaTVpkWHppC8vRGMTLxPyQJMhJTTuY4L8V80taFUP1EXKAOM\n5SmtPeALg6tL/KKGxRyzWPSev6quKauq/1eUAYQCvA7PEZa6gAM6QkwOiTZA5nOK2Qy7v099sI/d\n38PcmtEeHdNdrNCuC7ajb/+7sisvvnwHLe9xEP+8L7fFmIwN2RM4T4qrB2rqErnMeSKR81SNTjmP\njPPsmPPkBXGeTjgv1MVz4DyN2j0d3WvpR3CefgvO065D2w5eY8772R//lD/6vR9wZW/OrC5xzrPZ\nfCPOE784UN0oDTLlPEmcl5nkAbXSKOezcd74RXk754VWF+p2ImQJU/k0jmhkcBCs9xQq0/GBFF4q\n513ZxzWP5Tx5BThPn8B5+pw5L401SS7K58x5+hjOi/rmqTlPvkPOG6LYBc6Tp+Q8iZynT8l5z2PQ\nKz/Ps9rY53X9x5bXZuDrzSnb5Wr3F9Tvvc38Jx+w+OmPmP/Rj6nff5vy7UNMVSLWEheeZ00qtSkT\nhqfVhOVfqqFxWx+8QkUBVQlVHPzaW3Cwv2D/6gF7N65w7e4Rt+4fc/f0jIfthqbb5H4QUuaapM6S\nStNoGTRl/pFghJ1qP11WovK0IqSp4b2GGxFWVje5l3BaEtT0X6Yfp8fJ5a9TA9xfvrewl37TzuG7\nDpktsPtXMGUVA7mm282OGYx/hmvE+jBx5rZijaGwhqNTx1d3G1ZOoLJBhuJDENLSoLaAZgOdh1J6\nKDKupVifUd+7SXX/JmptmB4d4dUa4eqN6/zeH/6Y//Cnf8Qf/8GPuL5XU9oor8KwrAuu7c+prHDn\n4TlfffkVZ6sNvu2iKAziN0jzEHvyOfWdX9IcXMNVP8I3SqeetRQ80Jpjt8dGL4CL7XKTgGnxxMPf\nBEA56KS/vVs6DvCS7TeVZS++XIYkYdB7ovJjL93fALW5Z2mAovQeooiHk0641QorG/ZV9cFLJh7b\nHFOffoW4Jmb2ga7r6NqW2cEN9t96jz/+kz/hP/3l/8RH79/g+sFiVGU39mvevbpktWk5X6258/VX\nrM7P6dp19CSGezMXJ9S/+ydae4he+ylSF6ENrUFVMFWBsSUiJSoe3XSYqqAram5e1Hx9smG1blFf\nUBQFPsKbxsCuXefw6uK09+h/J4B+qKfYD4xFpEBsCa7AeYNTEyOaGlA3lm/sO4aQMawfvsxecyTB\nWd7vM1ztOTcDnKlYL8mV7DwTdE4nCCo0zGowxmYQOER7IN5v+K1XBAF2om4TE+A0tcH8ZSkAZ2xT\nRYHuzWG5QLLp7lVVB69fUVBGELK2CLBjzHDzGuKReO9wndCl5xQTgqUul7BcUl+/SnW4T3m4j5nV\nmPIWeusObrUaTYfP4W1XduUNLs8TtLP3t0dynmScp5HzpHz7UE1VCpc5L772EV69nsB5UpVCFWLF\nRM7T/asHsnfjil67eySvEOdJ/4bdnzWrtC2cpwzhftKcsqGi+/P0tfZNOM9HzjOvNed9yPVlTWHD\nCzrWsKy+Ked9GDlPH8d5w/iDxIb0fDivH1v4JpzXN6Hnw3l6qxVZWQk39q04by7xWgpwfe964Lym\n5Xy9lfMUEHNxopHzZMd535rzQu7Pb855mnGevGKcJ68Q533TAbDvtOwGvl5yMYs5xZU95n/wAbM/\njINeP36f6v23sAfLMJXRCJNICkMZAYYMGlvidGCxYCyYIv410aAKYi3zosDMZlSLObP7R+j9Bxxf\ndGwcUeklBSLja2ryohAvbpC+IwUAGrf4wUM2aKFMhV2Co/wZJx/Sc05OsfX46aYMTS//lUkVa//H\nOUfXdkg9o1jsYawNBkLoDcKgdMO5hlH/QTELxLgAxJTihpNzx52HHWsDzGM6YqPQdahRsIqZ29gM\nQvYUJ4Zqfc7s7A6lW2GsQJGm4Iar2sJy7dohP3j3bW4c7rE/K+Oys8tVNJ/VvPfuW9x46zr13h6b\n0wbXrUN8CVNAscSsTig/+zs213+f5r0/B/VYUZwIHRVrv0frjxhmX2UXGA1mTmDoUXLLqbyHo2FK\ndkxZFOdLp0idfmopJyeckJAQ+pfXyW1MgEqiAUugoNCpcOaFI2fYGMJ1RTC+o1ifUK5PMe3FaNqj\nOk/XtRRlxZWr13nnxjXevX6FWVVergOgLAvee/s6H/7wPX55/QYnD+7Snh+FZzMGihKjHeXRp5TH\nP6bQc1Qt4gqktogtMEUJTmDdgVOsAZ2VyHKGVkta4PTcs2kce3UZgnE6j+tcWGaRvSgkUDRG8Bo8\ncykGhETdIiboHOctLlQwIq6v00R9kAI2aA9FkrhYEiOHPpnCfKS2kLOyydReaFapH/8P9t6sSZIc\nSdD7FDAzd4+IjLzr7Opjpns507NDEY7IUoQU4Qt/PR+WlOUu5+ru6bOqq6ur8s443M0A5YMCMJiF\nR2REZkRWZpajyjPc7YThUHwGVajWbY7x5G0rMDKFluU4pquTfD0yCE8hKsvGYpovgoqzj/OQo5yJ\ns/LJZZAgynmh2++QsGSxv8diL2v/kpl729I0Bqq+8UXu1JA3aafJIW8MIfmBGAyOcv9bdLQP7tMs\nFizaDr9a4bqO9V+/ZfPoMRojO4eou/QDSTfR0LdeM3GezDmv/fwhzeG+StfIOZyXXv7fiPP0e+K8\ndOpWzpuXU2VQdj7nJXOfswV8VsyPGb8C58UPgPMOlm16CT+bXp/zSJzXbuW8atILzRNWb8Z5+qac\nV8fss7kBEY2q02xcifP0zTlv+yt327yS8/Q94jwhT1+925ynO847k2YvSB9m2k18vbU0a0tiE0/t\n3UMWX3zK3t/9jL1/+BtWf/sFi88f4m/tmbm7xrEnlnPLP+OGenCHUWBGBU0m8RmMfGNCq21Zdh2L\n1YK9ZYt4eHx8xMn6mDVFhk9uVURjFhKz51Od8ksWaqYEEiYas5zv7WP0rLzqZ74IfLZd7CxATR6m\n/D2/v8chMPQBWaxo9g/wTVMGBinPVWkaLhjorfxMK6oqPDsKfPu8Z3DQ7Dn8wuOagJm6m9mt7yyS\nEkMgqqBOWAxHHLz4hi6cmnav8Yxr9Q2Ibt++xcP797hzsMdq0ZzJUf697Do+uneXO3fu0u7t0x8/\nY0hLHBAPzQp3coT8+X/Az/53wmZNo4KIL0D0Mhyw0Y4JvCcYKoPTfLCS6nupE2VU1dV1Uk16Zb1R\nBrxIdf10brlmtb32iF76ikzzeE7K4adVlRCFTXScRM9L9eOzOsHFnu74Bc36BRLWIB14m9iKqoRh\nwDctB4d3uXfnNg8O9+nas65iARrveHjvNp9+9IDDO3folktekmLIiCC+QWLAvfia9sWf6TZPCe2S\n2CxsaXTbIOJho0Cw8uocGgVZLRgWe/Ta8/RFz8lpy+3bsFElhEgIITk9tYG/aPxE0/cEFtkBahr0\nxXnUeYKYE87cJ2RWtmdKugBN9TdVUyT5uE0naqq3fEx5McqvIVV9lm3zm+fmd6bb6+SvVP/Wzk1N\nF2of02Pmi6Y+I348VlzJRW1+77zQrVpcXLLYX9Ilk/dusaDrWpomA5HHe5/M3l0xqjib8wTsIRIk\n4MRYGAHaFm1bZHGI29+jXXT4RWdjgQjxdJ2coW7K1c6pqV3apbeV3rPGdyHn6d7f/Uwy53VTzlNU\nDQgYh8WpbysTWFILtFdwHonzFjfPeVJzXhJOelOct9Xn1zVwXnjvOW/F3pbJlZzDxWtznjnTvgbO\nyw1Kr8h5dl5EL+A8W/2ogm7hPNV8/2vjPK04Ty7Hea7MvdTJf1icN3k+mXeQN+M82zRyXqnP94Dz\nNHGevMOcd5lJrzL6XOLYdzLtJr6+p+QWHc2dQ1Z/+wX7//N/Yu/vf8bqF1/Q3L+NO1gl2R8MaC6V\nZuIlDzx5oHdaaQQbioZQzAePV+hOT1msOpqXOURrFgCQB6dk4DlqBJJfGVBbfZ/Wi+eOCeDE41w2\nEx2zO5moO++Zyr1nMDQ/r/bpUIqsOqgMjlNl1BSKjCoruioqhhCUIWKholf7ON8U/z1lpj9deISj\nLLinI50Vg0VQWQ/Ks5cbnr08ZdHCnUWD33PQgTYdQo/QQx9sCX0D6m0t+n7/gsOXf6GTnmbVouIr\nEa40raNrG7rWHJReVNTOCV3X0nUtvmnNxLauB+dhOEU2T2mefkn39Ct0+ZDY3AIVTqXh27DPS22x\n0XFGoG4CP1NpXtdNDZJJ41yWdNT5GdGcMaR2DV1VBdf1O1k+UQHWvILmKUNcovwhwung6NWDNPby\n4T24FhmOaV5+i9scj32srnsn+Kaxdfw+r+HfXi8iQtc4FouWxXJB0zVJmS/FtFoIIJFu/Zy9v/6R\n9U9v0T/4GBkUBkXXPR6hPWyQlaBrYXgJ/WmDrBacquevj0749L7j4YNlAqFQ7m+avBQ1NrNj8vHr\nFFzUqh/4pAlsEhB5xEWDUS0LIBij/JCgJVedjpuropdUiblJ5GZSJtZJc5+pnvQMblUwnvo0WZ9Q\ns7nm3Vq/tlVQZOUR8RbKWjwRl2SbS9va9DfBi1R9T6PJR1XQiABN2+IWi2LunjWApgX0NN6PQOQd\nzrvkd2ZLn9YR3GOMhChIGKDPz6fQKOo9cvcOTdOwXKQIwW3DyZ++Yv3dd+8x0uzSLr076SLO8wcr\nGxo+QM6b6Dd3nPcWOc9dWNT+tTjvQcV57Y7zboDznJgfuB3nfbCcpxXnadd1suO87y/tJr7edhKQ\npsEfHrD84lNW/+mn7P/jL1j+7FMWnz1EFi3iBXNYn1rlRUtP6gFkupECSWVsUgMhnwR4baKpEf/i\nBe2ipfGuCIRsBjpP9VL98RZavhmHpe9Cco6Y/BIVWEtnlgHxvGecw9AsR0V6zjK0dZS5AAsmsJTL\nzsohKgR1+G6BLJdmiiqSw/wWbYdlrxahWhxTaAJHEhgOfWRzqnz3dMPXj07YdB1NmvPM8OMAp2Jw\nqRHEoaI0oqzCEQfrJzgZoPUpCg0jlOrAut9wvN6wGSIhKj6DwyzFqKw3PZu+R0OwupPsuNUGJNEA\nmyP8y+/onv+ZoT0gdocwwGn0fNcvOYrNCENlNKnbY4ahWRs9t55y2OXcblJlML9mda9yj3ScVr/L\nQFzV7+R+FZzl+25JIQonwTMEGdVTrrGlh5uB5viRmb+LaVnqtiEixBjoNxvWfc9mCHTOj9ZrVVJV\nNkNgs+kJYUBQvE8vI1nZhZmeN/0xy2dfEocfMywb3HpAolqXF8EvzA+DBswHBKC+ZT0I3z454fkL\nb34ftDKDTn1BZMx7br91dVrEn6ylMigK0iRNoBbYKGWgaS1KVU0mJxLYpGcfrcTGSD/VpvJDSySf\nuhZnbl7GW50LoOWgfJ3Ux8eLGBAZ5DpyhB8KAPnklyFp7Vw2fx+XxtRtzAm0bYuGauKrrUzfvadp\nHC7DUPIlkf1NuAJFyWFFlsSaHdYKIQgigZDumZ/LLTrkzm26tqOzWiZuNsSTE9MIDmFWIBcV2i7t\n0o2lyevqFc+tpcVNp/Fe53Oedp89xBnnSc1525aeyCjKOPvor+Y8Ops/bQAAIABJREFUcU7Gia/C\neVpxXl7ws1UkXoHz9B3ivPOgIj+UnXljnDeywxU5T6+F89LEw7RYLFMV58nrcZ67FOcl8z+0PigP\nVPOqArZwnpzDeVLG0evjPNnShoCb4DyH99s5r+/P5Tz5gXLe2GKuj/N0y7c558klOE/eMuflEnhX\nOO+6x9O3OgW3m/h628k5/P4e3ScP2PuffsL+L3/G3t/9BH/7AFkmPz+xDtVbpyvy3wQMxKbti/QS\nm/jKKQRkuaJtWpyYk+6oAdUcMtmuUVsqxzh2LjMIdaiGPD6NPgqFUahItXGihuOs5JpsOweGynPO\ntp/3ZjsBxNm9ysYsANMBMRIRgvO4doHrFjgvo7CTYuJ7JsuTFao5n6KIgyEoxycDX317wn98eYy7\nE2kOGxYitFHxUWk6LAKTNECEmKJyiLIKJyziEerM1FhDiSHCMGyIm8h33z3h678+4tnxhocBVs32\nlnO66fnm0VMeP3nK6fFLYuinwjs/gGvw6yPa59/A3R8THYSgrKPwiJbj4CjWwJMiLeBSXbcC2MnB\ndUqm7nV9FjP/XJ75ujoFIp1XtpX9VvN6kfFeVT4suzLeP2kmB4WT4OmHCMMGdGnw03QgEbd+ggyn\nW8rQtHjr9SlPHn/Hk+cveHa8ZuGXdM2oMcxpCJFHz4/46+OnvHj2lH59YhohccmFiwAebTq89ixO\nvmSzfooMAw7Fd4JfNUh0xA0Mm0h/HBiOI8NpjwbYBOXJkxNeHnVlCctY3BkkU/HJOLBbsWgxgbc8\njYAQsyawIE5VsmK2BgVYNJVzdlmj9hKVrQwyBdnxeVL+bEsu4qVU7XiMnTv21UmTZC5yJUGWwzuP\nkqMR2Uku3UwxAPTOj+DjqqUxxR+GQ/y4f3QGDW3XISiLRY7mY34evHfpry+aX+eSUj3/TRDpyhMm\nGJLKKiMKMVTlLkIv6SW1aXCHh/gQWTrHcHxMPF0Tv/6GMBydKd9d2qW3nN4qEF9ben84T6MG2XEe\nECN6LZyXNiQ5/X1w3tJvbzkn656/XJnzvqg4z+047404rwouVaUhRB692HHeB8x50nadJs6T95zz\n3s8xuUq7ia+3kvKA73CLjvbhXZY//oTVL75g8eOPae4dIl2TGCH79Npy/uSrViAwB6VaTVcL5Eqw\nkwaARTo3RNxqD98t8N5Th6ItV5hBRsGZYpM6FVGWEy1DTxGmZ9JsYJxP378KiiZcJWePry+o2zdP\ntEQyv46aebnrkKa1kME56odI9bHzJpevfky0amJAdHQSeHkUOHo54Lueto0MTvFB8YPiOnCt4Bo1\n7aBgUX76Y06ePWXx7DHBLwiuIQY1LU40IAoh8PVXX7J/5z6/+fHnrFYrvnhwi0XriylyiMrxJvCX\nR8/41X/8jj9/9RXDyQuLYFmcuaZHEAeuxZ2+wD/9is3HvyQegDplLZ4nsuSYNo0seRCt4KcegSZt\ncl5fGVyq7TUEiWChkOL4XeIUuAoU5eNr+KnbC9PtVZ7ONNWcJYUhCsfR0YtPpu8NiENixA0bXH+E\n6AbTEI0XcyKo92xOjnn8zZ/5/R//xK+/+ILmJ5/SNgc0bnRncbwJfPf8hN/8/kt+/R+/5dE3X3Py\n8jnDEBCJOJehUCEq8eg5+vVv0Z8+IuY2qdji/0GJm8iwjgx9IAxKDKC+oR88z4/g5NRecrJZea0M\nzP03l7+FfJY0IGsZpLO2X8UVILKgy3VnSFCD1U9WypYA0VLdM+cgFXyZPGbUGp4JXa9zOSOV2fzo\n8LS0ttIeE0QUqZWPG/u2Jl8P4pJPjWTlVTS+Ulk7lLY0tvnyAuXMbN6Lo20ahEjTNbStL2bv3pmp\nu4GQGy28srNlNzbzUbRrgfbspyRqTN1DkBAI6dhe0jV9Q3ewh+cB3WefEF8eEU5PWYeBuN5UfWcu\nmHdpl9759LqQfiX1d3WKnsd5/t4h7tWcZ/9czHmjhMydXrKkyv9/v5y3vdS2cp7Mdut43PmcJ/kN\nOZ2YX5Gvl/N8hRRX5TwpKPL2OW/Jj+5nzrP6DdF44i+Pn/HrKeeJiJuY+6k42c55XJrztJj/vFec\np5M6NM6TCzhPXovz7hxI40aqOdlEvnux47wtnKdWlshb5jxNnCeJ87TiPHkDzpP3iPM+aNDbTXy9\nxSTe4ZdLFp88YPk3n7P8+Y9oP32A21sYCMXAKHXrdA67FYkhZzfO19HP3+R9tSZdBaIiqz38Yon3\nDajWAc5mo8NILMWrQV4rnW6d/T4Ulim/Zv3pvMGp3l9/ka07q4xu2Tc/Trcdo+fvV7XoHU2Haxp8\nY7FJRrP3Uaswz51Sj+22xQYRYRiUlyeR/lSJGyWuA/1JBKKFtN5EpFHwStMmE+ZVR7fuWTx7inz7\nHfGv3xK6FcF3yT9uMn0dBjOz/uPvib7j4ac/Yv/WIQ/urGiria8hKo+PNvzhm8f887/+O1/+4fcM\nx8+t/iufBQZzHvwCOXmBe/Qn4vERQ1S0gY14HrsVJ26LSClglP/OBoz890zdpgYYHbjKP0M9CiCm\nBgnVaTn8S76vztvftsY23VYG4oLzlIEGhUEdx8HTuw66hS0fRnD9gOtPkXBibcS3UxAQh/cNm5Mj\nHv/lS371699y68Fn3L99yO1bB+xXXeTF6cBXj4/453//Lf/yz//Kt19/yfGzpxB7pv5GAI3o8yfo\niyOGXz4ieoeooAPoaUQ3AYZI3ERCUGIUFA++ZRg6Xp54Tte28mZWCBQrRUb4Ly9AAiKaoMjylOzh\nzfcDjjqNbCjldw1FWYYIjCGuq/Ndyk5MOxxiUYC0uq6AbLEMGK8zythJn9WR5at30EnejUIaRBqy\nb4fsPycvHx+hiPJQeXmKaQANRBQDnKZpcBJpmwRDjaPxDu8zBLmiCTTZQfpemdUX+WefcRmMUKJU\niRIlIkGIBIIM9GIaSte1NPfv0Hz6McujY/rnLwina/r+KRoCu7RLu3SpJOKdJs6T5d98ru8458k1\nc54NFjfEefaOP5/d2HpcddTrcp6fcV6FHrO7TjlvzIODt895B7d4cHs1CZgzRJUnxxv9o3GezDiv\nlIbNKW7jPC7kvDTk6iU4T6iH4innCS6WuJ16g5xX6mzy6+1yXj78xfqd4zwRET2H88SJ6CU4L01H\n7jjvB8R5F0n4dyq9NxNf8dWHvINp2ga6wwO6zz9i+bPPWP70M9qP7+P3VxAjQvb1oNPzLvLvNRPf\nU2jIA0E2H64HonScc+CB1u4jyz2axTJFCMnCQko2pg4J0wuwUtaJa9qZs6x5AKnzVTQ0VT5GaZaO\n20IWky/n7K+fLadaMF7YLbNUle27fQut4JrGzN91FOhm3V8J+Mklq/XnCllXKsBmHXj2bM26V2ha\nWC1hz8GyB9+DqAkjjQSUmOxe/fER8uIxcX1Mn0PaMqSmM4KdOGE4ecGTr//Af/+v/xfro+c8+u7v\n+fjhfe7e2mfTDzx/ecTv/vRn/u1f/43f/ct/49m3f2aMQF7VDynv4nGnz/DP/4SPR7gGNAgng+Mb\nWl6EJoH2NvCdwZC42X0mtxuTwya/yO3ZVQVa1Vu+rta/pWpzhZLGTzabH7Fv2k7TBtMQZR6yaD+n\n2feDWvsQ3yJuwDHgErRkIB61aCA4NAz0x8/402/+BVCG4+f85qc/4aN7d/DeEULgj3/+K7/9wx/5\n7//P/81ffv9r+pMXoHGckNSpM13tT2DzGF4+QY+OiXRI8BCCLY8YBHUNshAkiC2l2BzSP3/JiyPH\n8akyDCG5skjtVDXBSnZMmpe72HZNc5LOaeKBERKieLSEH5/Wc5Iq6ZpWRvm+wugctYrrRL5Q7Xem\nvHCUDVKYOCYZU+Mw9UtMvoDWX7IMM62oiBiYkJ7bmZl/LoupQ9usqXMT8MnnOJ81igYrItA4R2wa\nnCiNT74eUlSf7OdBssNoZ86JMxhZtCVhVK6P/TUHVlKNRWZHBYkJKAXzCeEE1zt6MdnW3rlN+9mn\ndM+eM5ycEk5OCScnJerTLu3STaY3bGWTeYc3u9Tr3ha6wwO5DOfV1gakpUfb8eQM51Unvhb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e7HVnblLMxMgWP8Wg+kWmku574eUlsvx1dQhI6/lWpgyIN8NUoKozbx/MwXwI0qbAYxIHIdNEvT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jP0mNFb1Bi8PUVv7xj65SH49SHqN0cw4xm4qi7sZjvppJOPT9bhPHyAnLfzeBv/+IsJph3n\nvSvnRSV2UaWuz3luc0A/+NXKedRx3kfLeaS14vfAecT+i+iQ9o7zPlZ5MANfXRtYRwRcAHYtiHoB\nPjyFmS1g5qV9qKZzZOdTZIvHoFHfbo2tVVjwORGGBSKlAJoDZYlo3SEk2it6EKdLWnv5XCa7CUNe\nGlZACUWkFJABhSIURY6NQQ+8NUA9HWE2nuLsfIrj8ymOxzOcTRY4my0wnleYLirMyxpVbWDqGscV\nYb8CflgA09pAKQ0iZxEEg2Fg2Cka9h2iV4AM5hR4ZFEyAApQ5OCHmtmNMEMgGNBSWXBStK6sneIx\nIYhombLWCQtxk7LC4XiGTBHYGJzNKswWFZ5tD7DVz5GRA2M2qaprNfzxBcfxGjfu+fMAFtx2TQ6C\nIUmHt8RJd6vCSICrcS2dFRZjiUzYtATGk2UYirVIQvsSxI4+AoKsNUgFcKkN43wyx8vDM/z7i7f4\nw/ev8a/fvsTheIbz+QL9LEOudeQqH09Y9TTekBAgkbxtDQjfRlPjn4eBZRCy4XDIZdLIZXNxNwP1\nIn5eQWSnvvdzje1CYUNlMHWBsqqQaY08Uyi0RpFr9HONfqHRzzMMejkGvQxFbkFIqbhoaFhnJeQ/\nQmKYDi/ySLFwwrm/JsupWWRhLbPEb8w8gWxfpDXMYoGynDsroLUckluo1q9jobTNR64VcgKKqkJv\nMkfvdIr8533onw/Ab45hDs9Qn47BtUEz5k46+ZCkRZO8a0Nv6aHWifZeih83SIdVVnMeC86z3x69\nP86LSBCUY0NaOI8c57FSjAzIFSEvchpZziPjOO/0fIqTjvNui/MoRBqFLuA8FjxG1+A8auE8vigM\nwXnELFZjulnOY8F5dA85zyUhtMZkiOcGOS94cpxHV+Q8vgbn2SZObs/V63Me3TPOI8d57DiPHwjn\nXUdfNrvK9yoPZuCrk+uLWZQwVQ0zXViL4NkY5nQMns2hn+1CP96yg1+5Fr5YaALXSRoC+nZKvXjT\nbGWc9aQ5hNCQ5R7JKQEC2CvvxtfnzBaYsgy618dQKfT6Bba3h/i0qrBYVJgvKixKe1yWFUxVg02N\nH46m+NvhDG8PSxyUJnb+BNhdEOMCGd5yZ8g4BdeYZhvSHzUoE1kocn2AcsQSdwwSe/00YMCptTiY\nIBjEWyDljiJhGrezvihFyLUG5YzXJ1PsnU6xfzbD/tkE/+VXz/HLp9vYGuS2mmuXAL+HjWReQtzC\nu7XexHR0cGOxT9n7WSBpTj9P4hKQE0LmyOARbpDAEOC3q07vmabbhn/Z3GNJivQkaWvcg6x7of3d\nL8Fb56LiVKTBDEzLEj/uHeP3373C//Xn7/H9/gn2TsfItcYgz63yJBLliqDoIdLu4UYWuW+baWrb\n9U+oG28lXMttbAeStHydSvEllGnCINd4vFHg880cRWYTrZW2U8EVhT9v/QyLgRLF5YvDS0VqYW2u\n6eBBJzyfLdUU7pO40Uw8PBCmGQrPrq8ApVGzQVmWMFyD3FbdEoS0VtCZRqatRbS3qNAfz1DsnaB4\nfQTz0z6qV4cwZxPwbCGmvHfSSSedLMsD47yIAu/AeeQ4b9BxXsd5Hec1UnsrnCe+hL1TzuOO8zq5\nKXkwA1/j952AByWNDpAB1AYwC6gTQBlGXtXIZwsUx2Pkz3eR7WxAb/Sh+jmUVnEXVqclGAAWFdAb\nAFVpP5F0FrGL0yBvSs22iphkZ8QpFIXdJMl1vwrQgPto3Tuw3hC/tfZbxmaVRp5pFEWGsqpRljWq\nsoKpDbiuMWOFcU3YPK2QGQNOdoWU+fC9oyhidl8SJJJaB71zm1y7HKpfdDW6iXFFOOKg1MKuIUl6\nBAC5c8MMNiaxchHZDrmqDRZ1jR+PJjAMlDXjaDzHb57vYmdYYJDrYPVgNgmgSFJYrkVO3EXASUJI\nWicn7nzT8HGLYxFW9CMtftKCyDKZARSXQYobQNRMd1s+A86Fc27ck6a4dLp7XISzNsC8rrF/OsGL\n/RP8yzcv8Kef9vD92xOczRZQyk7t9jBkQ/KgQslzEdeCaCj8pbabpDJIXKMEiV9vAbfU4Ux97guG\nle9BtFxy9ukkaILdqSdX2OhlGPY9EDn4UcpZy+A+D3AWvbATo/i4RMCP8hY/cT1YXmPJJHAUrKbN\n8hFdTFImvrsTxUrCj1/LAUSoqhKz2RRsTOx/Mh3Wd9BKIWMgn1XozRbITqfA4RnKN8dYvDpEfXBq\nX14XZQsMtUNqJ508dLkC530sD0HzbVbeouTWzXIeoyrtlwBE7bOzQvKuxXmi/q7PeXCcpxznkVbI\nlzmPG5xH95Tz+BY4j9bgPLqA8yjGvJwW32AEm8kxkXU4jxqheDfMbvF7wXkkOI8Rj98n57HgPD8F\niz8yzvMlGFKsQPyOnEeO81hwHhERt3AeCc7ju+I8P/hV3j7nrej/70TiA7B87bphvau0pelK8mAG\nvs7edwIepDTaKjMwnQPTObLZHMXpBP3jc/SOz9F/vovi6TbynRHQy6ByFRdohLELpzKDBkOgLMGU\n+x2iIdTM5U07YSSOyWz688980yrhX2iV6LUkCDFsq5Z9BhHI2IUVVQZopWCUAisFaGWByDB2GPik\nZmy/HqOPGjMmGFJOYSir51oeOZ9EI5PKVpEQAaTcwBVHz+z/Z58l36GHJLsq4wQiECx+MctBRQco\nimsYMItiJrv9NZGCZoX98RxHkwUOxzMcnM9AUPjF0y083xoiUwQi7axnsUOOVrQkVbECG/XIwk96\nO3XIbHfeSYLyFsdkUCvGzqJ8Igo2QQhJGQS/8NPdOe7ekySNkvPlZk3CaTymcB4HXuylOPAFIiyq\nCqfTEn9/eYjff/cS//Xf/o4f9k9AZBXnMNnKOobhDlx+4mCQTFPQ2P4RQVpTCQSI58svrethU6IP\nMYVPGJvj3UuameNZfKWwg15a2QkHo0JhWCg7nV2LtSrko+zK3fczYQCLYnkQeVhysUQOiiUhIAiw\nbqwgAAAgAElEQVR+AM3dCyB1kQqlGIbveyJs+zh9WM4JGGVZYjadwLCBzrS1+uXW8pcTISNCMauQ\nj+fID06BvRPUb46x2DvG/O0JzHwBU1bLiemkkw9YOs67UFY8/Cs5j96F80C52NLtnTlveQBtDc7j\nyHlR0V7Aecow0HHebXKeAJ9U5YvkUprytAxbOI/cbwjiEs4jGXKD8wLN3DDnBUh4B87j//pvf6cf\n9k/4FjiPmjV1x5zHnoY02XkRuQavwXm0gvMI8IvlL3GeHY73n0HCdmNugOzWOU+5MqpunvMoeXze\nj4TmIaTZFNrS2FBEl4b5rtLSS68nD2bgq/kK0Mm7iZkuUB2copyXmB2cYfbmCMWTbfSebCHfHCAf\nFVC9HKqXgXLlrGIMNZshm83sG2yWI5njfN1mfVV/AYoapjduHGsAyAJoKEXIqxqqrqCcVUxrhbpm\nGGOwQYTHBvhiq4e9yQIvFkBl7PRxCxxW+XCkggBeTGyntDtlEBe+RNjgh12XxuL7ectYlgiJ2U6T\nd3HYezFeOKgSeCB0T5wCbwHANKyDFJS1AoGI0SP7+B9PK/z51TFOZhV+82wLv32+jc93N/B8e4gi\nU9Aqg+FaAMiSCrTVIstDwIh0yfKE0AAsd96YvZX4b8zy8vd8uCzP/TXpTqTHw9AS+HhFTNGtcNIQ\nqxSDghTQ4q1SiqI1bzwrcXA2xXdvjvDNqwN88+oA3+4d4Wi6QOYWNtUqzlByLQ8BlQMk+dgljKXo\n08CjBi4KPeSt1dSWv0bhyJcTUSbUdAtbvn6MOiO7c0+hCYOMsNnTGBUZSIltqf3aCOQHkXxbtdft\nUhnSihctha5oJL1EmBSFmcz/SihIzCiTt9xzm7Sp4IXhd/lR7mVDuYfamBq1qZBpArRGzox8ViKv\n5sgXFbJ5BTqbgo/HmL09QXV4hup4jHoyQzVf2HV7LqyLTjr5MKXjvJuVj43zgI7zOs6715zHHeet\nzXksOI8bnOe/OQSJmXf2uq+VB8l5DwH7HkIaV8qDGfjqvnx9V0mVmCkroKxQnk+hD04xPzpDsX+C\n3uNNFNsjFFtDZBs9ZKMeaFiACg0ogs4GoNMzqDwH9XtodoWJVlkR/7XTLhRO+FVy6nvDWxj9t+60\nKqFqgqoVdG2gjQWiujYoMg0mwle7A+yN59g7nGNWM4QpAoJFUo3DzhJIvKQcPI4Qi+/3Kc6E8UgU\nAEkB3nwV1VfUVik0xPJOZkhxBCRZOP5TMYZd55YZmJY1zo8neHkywd7JBIdnU/zTp7uYLio82Rpg\no19AR43aqF4W1UMR+EQaE2iTJRPSLa9Zv1YJ8RKk+KASMEpKO94LxZNAULzRXAsiNWbKcH14qcGa\n/P/CWgdSAYQIHqAJ85oxnS/w6ugcf3t9gD/9uIc//vAaLw7PcDCeopdlyLMswFCoK29BJhkngqVY\nWuUY4isRdyW0MQ9D5NZNSELjGKCA2iSohvOk1Gn5Uiwxu8ipVnZR4n5GGBUaG70Mg0JDqWjJozDt\nXU5/RwQiXx6NwS/vJg5rUeQgD1HhCYvl57MZASjAU1qtKTe5H3bs5YDIrU1BALiuwfM5MJ4gn5cW\ngBYVslmJbLqAHs+hz2cwx2NUR2MsDs9Qnk1QTedJ26W09Dvp5IOXO+S8tofrwQN9C+dxC+fRGpxH\nHwDncYPzyHEet3Ae3QPOI8d51vvdcB4LzqMrch5dwHkS6egCzuMVnOdpJcwCEzmU9+4b57HgPHKc\nxx8Y5yWbPF7AefwRcR4LziPHeSw4j1ZwHsPXWyrrgN9NwuFlnTWt4WbdcK7i/tbkwQx8dXI7wsyo\nqwp8OkY1nWP+9hjZsId8OLBAtNFHttlHNuxBFRrZaYXaFCiyHMXOlug1msMPwLXb9UWPWdIxSSDy\nNwlhDi0BqAkgEyxxpAisCVRp6NpaBHWtUCuDWhurwBXhN59s43hR468nC4zLKu6CxKLPJ881HDpb\nO+ATlWrQMcyhU7b/SOgedp2vV0DkvlQH7DoPPp8N0PQWSQlO3urHXiFFpRS7V44KxqcnA2pWMMx4\nfTbD4XSOv+2d4PPtIf6Xr57gN8938Hx7hGEvR6ZVsC5aayPcugCUps9JAjvuv2XQWL4XIMZDSxJo\n2sKkX27cS+JL4CrCKSUpkRJKLPkVt+DbndWnBCIVLH8MAhvG2WSOtydjfPPqAH96sYc//PgG+6dj\nnE7mqGpGP8vCGg9WybuQvQVbK5tGFhFzS1KWzgXBhAYZvUuCiV93UJq1xvHFrzWxrYW1JBzYaCJk\nCsgUsN3L8XjUw2iQIy+sFVB5IHKPmYchJaxy4fMBCIsd3NpeLmIJSWntiKwL2AnXw1+gQQGRjYzL\nYwrWSGvB1RpkDOrzCdTbI/R+2ANmC2CyAI1nwHgGcz5HPZ7BjBeoZ3PU8xJmUYKrOjT6mySaTjrp\nZEk+ukfsY+Y86jiv47zb5zz+SDgvpK6F8+hj4jzzYXJeW698zQ4+CfNeyYMZ+Lp3JfdgJS1JBoMN\no56XqOclKgDl6QSL4hx6UED3C2SjHrJBAV0o5Mdz5HMCtraRf/lpUCjL0oCilY+O7N2p3W3onFic\nw8KKgZgO7xJkxLHv+chaHaAUqFbgmqBqDVXX0LXd2pm0wudPNnEwLfHJzycYlxXO2MCu/dBIrZsO\nnu43FK16EUzkHYTfEBY3gIg8EFl1H6bdc7OcPAB5a1oEomYhkisDH74CQM7CSURQzKiMwayqcTIt\ncTKeY+90gllV42g8x6+e7eDp1hDbwx56mUam3bRuAphtWiiUiay01Crnf6SrJQgK151/ATW+BXAA\nT07K0d9rXk+OA2TJPp6Srzli/Lbcmk3S1yAJjUrOXVUzamMwmVc4n87x88Epvt87wp9e7OGvbsr7\noqpBROhpbXdhClYun0uE8KNyTstgOTXiSstDGfyFthId2rYm44nIRPE0Sdvysx3boU+7Irip7wqD\nXGFnmOPRqEC/sAuAknenSPiJ1kAJSfLTxRQeaRlyYg1F66FzFIEqPhe+gCQgkfhtOEsskKTILmya\na/CiQnk6Rv39G+AvL0BlDcxL8PkMZjJHPV3AzGxfa+oahps12SzjTjr5eOSCls+XO7lQ2mgivvlc\nOSkPRR4G5/GyKoPQVFGxWc7jFZznO2Rm+wYdOE/XBLRw3mdPNvHLds5LCYrtkI8SRfCRcB4LzqOO\n8zrOcwe0gvN4Tc6jNTiPHOex4DxffOEaxRvs2j47zqMVnBf2sxBoeJucl1QlpZXQlHfRd+8q6wxy\nLTfNy8O8yXy8c1gPZuCrk7sTU9UwxqCaL+x2vJl2O3EYZG/PkZ8uoD//FMP/UCJs0nyVx2CV8JoB\n+QEvID4Gym197YXkwJc7rn0PZ8EI2kDVNVRdg42BVgqPdxS+nJb4zU4PZ/MSZ+MarADScRvwaO1w\nsOK6AnJKRH6vH9eL8M9/nPYtrRZeBRFUhB3ATZ3nJKsSkDwUmRYYIsRYvcVCTi0GkfuE3YSuOFNA\nVRsczyv8y4/7+O7gDL9+e4xfP90JYPRoY4BeZq0ffrlSO+2eIJWpbRsCfyjmNQCQ83LRG0rzOrdc\nD/C06p6AoViSHqzahBq/zYGJCEIeLtkAs0WJ08kcLw5O8ePbI/zl57f45vUBXhye4Xy2gAK57asp\nUfSBh0JkEfr8NucyZk6drchHTHJTY63SIH7thtCSJeCv0NkeohTseg+ZJvRzjY1ejkejHh6Neigy\nFQzsdup7hD9SHtglEMoBLP/iIPLv7kcMauRb3ks8ukNhjQz9ha+TEK+b9u7ClVBkgShDfTbB9MUe\nZv/jJyz+9VtAazBgFzGtDNi4GQfmwprqpJNOonQPyi1Jx3kKX0xL/LrjvI7zkt+O8x4Y54Wkd5x3\nJ/LgM9UNfH3kktoQOP5vXMdWG5iqdg8+o+JTlBVh9PoQ9fkUum8XRY0aLtE6LXLdZ0aodqnlgbRz\ntpofgLLaCQ0LIbGbIUYgQ0CtHBhpwBiQMVAAdrcr/O7zRzguGT+fH2PBgN+3RL5VSxjyuQvQgiWb\nX7C4yKtRlbtlK7lO/Mvp7J53whl7FYQEomSCnMEOYEBxVC5yBg2goJ1JUykCowbXwLw2eHs+R1kb\n7J/P8e3+KT7dGeHT7RGebQ3waDTA5iBHL8+QawW3+EOAwmiR4yRZvuzS/Mh6RpoT4T1c51j5ofy8\n0pafKwDOOohQxjI2b32+WKIShlCCAMEwUNeM6WKB89kCh+dTvD46x4uDE7w8PMXLozO8OTnHwdkU\n5/MFasPI3XbOWsxyCqlpQpEon2D1ik4ToAasBU2id9MkGCx+Mej08REn0lVztYjokcVpRE27qLC1\nAmaKMCoyPN7oYXtYYNTPkGk3/d21Q7m2g7QAJms8BI4JlBN/ZRGSL9dYT0mZ+XySuChPG9dlvhMY\ncm7t5AKCyjXmkxkm37/C7M0hyrOpXRyaCFwbcMvLXhO1O+mkk1uXVCm3S3jfvOW03IncU85rcbTM\neb5vT2bqLHOeAB4knAfHedTOefTPnz/CSeQ8XsF5EmMoNqKPnvO4wXkCspY4T4yo+P0DRU6WOY/u\nMefRR8p5oUYUETnO4ytyHrdwHl3AebQG5/FdcB4pAuUaVTvn8RU5bx09dNfiO+Dr+vVyn/K0JN3A\nVyetkrR+5jCjvD6fojqfo9w7RHU2AWUjqEKnnq/a5ImAls5i6V4gIKRdhu/cDBC+31aAWz3U+eV4\n34ORcu4MOxjiMJC1uc34D18/xd54gX/94RDGAAvtpownSrVF+SbJYyi5HkW4GvVJdJ8O+ESXDfEW\nQGFhkzsABZ/iErlyYeWVa1QazL6Dd9sNMyNnVzRKYVHX+OF4jL8fnIKNwaebA3y5O8LvPn2Ef/hk\nF18+2cajjQG0ztw2v8bFLwe/TPIZnC+QdBnUZt79QUTG5Fj4DzwuAxNlw0kJi9ZNCNPpsXTPt71o\nZZJKnEEwNWNR1zg4m+HngxN883Iff/n5Lf78Yg/75xOczhbItUau7OehmY5BXyiUvqbIAiGKLwBN\nV+QGYOz23aIUyJdVe3TxDY+XwlzpgxvniPCtld3hxy54Stjs53i22cf20E5/t24FEIWdfOCsgBEW\nvdVPttn4G8gpgg5iWOFl4KJsqRi+BCMSgYXy8e8PMcpoxVQEZBnqyQyT737G/OgUFQCqzVol2kkn\nnbSKRJHrPkpNmG++B37ocuHLzHU4L/i5Wc6LynwF5xGB2MBNjSGCAl/AeW7qRivnEbP9Zm9rNefF\nJZE+bM4joxR3nNdxXquPu+M8t9npO3Fee1/XznmuetfnPKUIlGV24Ov2Oe996Cipa9uO284vknXd\n3bl0A1+dCCHxP0AXtFkDg8XJOWYv96AGnyPbHAB11WjiDQXdek+ernhGaNU9kWIPOeycM8GZ/aKG\nCN+4O61BJloF2Q5+gYGiX+DR80f41ekM/3nvBH8+nOK78xJZpu3WvPDWLgktNlxCVLIEgvFz9aWS\nAtxH6t59cqc9r0mZCRtboDMO59ZJDEcpy4g+TbJPDZzJUR0qb+JgQgZgmOeotLJrGlQ1fjg6x8Fk\njj++OsTzzQE+3Rnh890NPN4c4tGoj0GRo1dY66BSCkQKRBw/BeC07OKaFaFEfCFJfIxAwwKKRKl6\nK2AqMq/pvWDdla7Fmhh2ANW2qdowyrrGvKwxni1wNl3g1dEpXh6e4oeDE7w+PsfxeIaT8QxnsxJE\nCoOigCa5iGd8xppWvIvTnAI22PuNAONZMxjjpdtQng7uWqN04bAJDSLtDXjp/1Ri/WlFyJVCrgmD\nTGGrl+PxqMCzrT4GPW0HidC2uCkFgJFAFBc2devpRUtgeP7j4JYnlgbhuPshbPKQi+A2rr8R8yzB\nyj82AYbI5UHb6fyYl6gPTzH/4SXq47OWMmot+E466WS1vOsDcy/h+w6lMXD47pwXNfRanCecEloU\ntL27BucxxGBWALAlznNv/0RtnMdsAGOnBuWXcx59BJzHF3Ae/XB0zo7zuMF51HFewnn8kXIe3zDn\nseA8anAeX8J55Ae/VnAeC85zDe5anMdrct51dM99AES+4NjLis463Je/90q6ga9OVojolBtdYXl8\niumPr1B8+hg9tSu0qvR/Qce58v7laYl+SfQt8oXVu+e4HgQLMCIA7hvsQCNukMdaBRm5LrA96OHr\nT2f4z784xUn1Fj+ezGxnLuKxM8QoiTKqb3jdnaS+qd7jLjKxPNpym540gIgZfssWbyH0ljcigLwJ\n0KfJ6XpZwzJS5T4jYGZkIKgsQ8aE2hjMqwqn4zm+PzwHDGO7p/HZzhC/fLqFLx9t4/NHW9gd9bE9\n6mPUy9HPMxS53dJYOaXllXSad5lLCumXgBQxW/a7PixZckkttLi+oF+2ahB+aQ07xb1GWRlMyxLj\n2QLH4zn2z8Z4e3yOv78+wN9e7+PHg1McjGcAgFwp9LIMWiv088xXRLAmJpHJM/EMEcX3BK+4bXHI\nF40G3AFur/X0WqvmoXYXzhAOkPsco+2Fhi5+ySEAWinkipArwjDXeDQo8HjUw+ONHnSu7HRxSCDy\neY+Q0gZHEPf8ebyeuvG346ce0l9kqaV7id/oNtaDhym/lgrF3ZimM1SHJ1i82kc9mTVaXyeddHJN\nSRXDzclKNbtGWtZxc58e/sYAGJZO1+E8lg6i60a5yFGJpSK7UC29A+fRZZzHkfNYcB7lKedRC+fx\nR8J5fE3Oo2tyHjc4j2JZeQcrOW+pFm6B81hwHnWcdyXOI8d53MJ51OA8vibn0fvgvPpmOe9964ir\n6sFV96+r+5Y7x1uUbuCrk2tJuXeIyZ++w+i3XwP0GVz3Aj/9+VrCSwcrhAKA2F8BJhBJAfw3e3YZ\niLBYqFsbgh0keYIgjstFkMKjx1v4n373JV6OS/z89hQHDIyNgQq7C3kAIUjLKTutRuxVt+9VfXKj\nem6zuHLLWVo8wuIHZ2Fjf+zCdw4U2byEtHIanhwoiGl0O7HUNfwUYQ3ttgq3C1rmyloGGYy98zlO\nZgf45s0JNno5doY97I56eDwa4MnmAI83h9ge9bE16KOXZ+jldrcXaQlazjmHkmt+CrB8rVH3LYo8\n6kNKellf9czW2lfVBou6xmS+wHhe4uhsgoOzCd6ejPH2dIL9szEOz6c4msxwPl1gMltgUTOGRe4G\ndBS0V7SNtRZi+rwibtyiFJl8nbZbw9ntCkgSBJZKZdmXh5foI6xJslJVXfRc2uua7HpeuSLkmpBr\nha1BgS8fj/Bkq4+i0GFxUZ9XpSgUh4KfdeXLIvYqF/ErGvcD5MhzF5hDLgFPMR77CTQlAJREK9Mk\n/xTZ2QG1wfzwBPPjM9RG2q476aSTd5DbAOHbfDTvBNzvSm6R8/i2OI8UC/7pOK/jvI7zwpWO8zrO\nuzu5t8XTDXx1sqbEzpMAVAcnmH77E6qDU5hFBbG0YFDYqXdevnflR6LhL8AQljVLYn4jN5XZ/zla\nCjDkbrjtmiFgZbQxQK/Q+O2bE7zZP8Ef3o5xPi7BWRY6WYb1ty4GMryiTHKVFMfy3TRrMj5m437j\nSgseiAgAU9yI2yt/w2yRh3wxyk24nWImBaXYlZXnRQtWigiZVqhqg6quMa0qnM4WeGsYCoxhobHR\nz7Ez6OHRhoOijQEebQywOehjo99Dr8jQyzSKXCPXCplSyLQKu+AEUCKE1Pn69kouFkijbJwpzU8D\nj+BjlX9t7FbUVW1QGoOysn/zqsJ0UWI8X+B4PMPh2QT7p2O8PR1j/3SCw/MJTiYznM9LTMoSxARF\nCkWmUWjbnQblmVRs2j5l+pcgpDVfDURubWxxEdnEOu3atsdgDyTtGO62tpbPVpszcUAuOq2AQhMK\nTehphY1ejscbPXyyO8DWqIDOlINtWze+jn2WneVPWOls6HF6uywrimFRA+SEf28NjGUhHAkk9CWS\n4GhCZDGs8KvsuhZKK2BaYrZ3iPnhSXgeG2jbSSed3A9pU7k3JRf1mg9EVnIek8wZe3qQ3ls5b913\nde+MZFJY6qKLOc++0cqNEiPn0QrOY8d5VHScd1Ocx4LzqMF55DiPb4nzuMF59J44z5/xB8Z55DiP\n1+A8dpxH/jPGNTlPTMy6kPOC8xbOE7nxsYliuhnOI/c88g1wXrP07xocb1Mf3kvpBr46uZbUZ2PM\nX75FuX8EczqBGmS24zHS1cre9IqyZlhLL7fOnVOQAXqCJdBdCyqT7DRi503nGVSR4R9/+QxcV3jz\n/3yLl/tjYEiA1s5yI6ecp7Fb1HLWPrcTjg2aY7I4uk9zx0lYCHE41cbx11sDw58718qmUZFdfwEg\nuym1selJJmU3MmAtNcoqALfwvxGJJRO1BZG1/NS1gTEGs9pgej7Dm9Mp1N4JMk3Y6GXY6md4NBpi\nd2QHv7YGPWwNe9js9TDq5Rj2C/t5ZJGjyDJoLRfGlJ/CIRwriuVil+/wXGvLwRgLgH6wa1FVmJYV\nJrMFzud2l56z6RxnU/t7Mp3hZDLD/qm1/o0XC0zLCqY2oYgYwCDPgwK35Yul8rOrZ7q6at5HU+KV\n9P0i3vV58qBAKQysCK+h2xhpAsgDCAf3xGqFKowwpMgOennrX64VhkWG55sDfLI9wOOtHopc+5YS\n6i5Z8wERXGQdx/YVExotfcn7UbhESNdsWAbOximlcaUAG8vYWyrD1Hflto1XCmZRYvZyH4u3x/ZZ\nuajYOumkk6vIRa9lndyBvCfOa2intTmPo8K9GuepPENRZPiHjvM6zus4bylVgvM410QPiPNCCXSc\n9yDkTvT9gxn46hrY/RJT1+DpDIu3x5jvHaL32WOofgbU8DYX51IMOjWFlw4ul7bHwlMFwQLHhZ24\nEA2AlYMfByoeNvxePs6SRorw6Mk2fs3A//rmDPNFie/P5hhXFZDpBCokUzSVWposjv9z6l4CVgiX\npDub5qCc2aQw5MqfEcHBWzXsfXvXbwJui8xPEU7TaKuOQM6spYjAUFAGsAvGKqcUCYbsb20IdU2o\naqDi2raVkjErK5xNCYfjBQZ5hjzT6GUZ+rm1CPZzhWHPAtFGr8Cwl6PINTJlp8tnisK0ea3cubLb\nczMzjGFUDnrq2lgAYntcGUZZ1ZhXdmr7uZvePl2UmJYVpv54UWK6qDBz6zycTxeo2KB2llMft1Jy\n6n5U5F5SVZ3e8+dSySdtheP6C/68XS4z1LBrQxQem6X6FfEsP1wc3hGaQO4BQSk59d3C0EaR48lG\nH188HuHZ9gC9XIdPRvz6XkTk/AtrHInp8U0QIopl6so8KXvhxofTbNEBmAgxbFlnlP5FeEOo67gd\nN6C1gtYKxIz6bILpdy+xeH0Ark1SO5100sl60vLMrPMYLRHBTaTlHeS68ct8vO88AEg4jz3n6etz\nXturaXv9phsoumvrcR5FgruI82xA63MeOc4LGbglzuOPiPNo1Mv5HTmPWjiPyqrmSziPG5xHgvP4\nCpyXTk1M74XzNTiP/Hm7PCjOs1Ep+6x8wJzH94Dz7lLvybBvI8t3WozdwFcn1xKrDw1m+0eYvnyL\n/MkWMMjR+ny0WEFuXAJ0iR7c9+/B+iegCR6ivNnIu/VuWPwBw60RPh328b+fjpHXFY7/8ALj6cIC\ngbLKoRZ5Zp9vRMUXqCYpogYNed4TSaLkvnUh13iwfwYQYAQPQohKxlqRDNgosLIRGGKLcw3lkORD\nlLGFH6tKlT93UFS7OC00aGSZRmFqGGOCFbEyjMPxAmU9wbyqsagMytLAcA1whUJrDPIM24MCG/0C\nw16BItPIM+2mmdvtogvtzjN7bgyjNjUWtcGiqlFVNcq6wqKy6zgsKrtLz3RR4ng8tUBUVjAsy9tB\nHwi51nZ6swIK0kLBxuYWIEEUkaLVDb05LTrWzeV4k4QRQIFFTUWYkBDlFf96U7KlK9E2QzoNGHYL\na0VApggZufUelK2XnUGBT7YG+PrJBnY3C2fBtWF4q66HaMdJAFtramuhtkFRM62CcnxdLVsAvRXR\nu/Vl4/6UOA5gJC2W6Z/WCpnWQGVQnZxj9vefsXi5bxdUbmShk046uVy6Z+Z+ieM8lpynBzkBlBCJ\n/bkxzmu+mYs7KzmP3HeObvKVW4BjNeeRGCXCcGuET4Z9/G8d570L51FlmNfgPNoeFHzDnMeC8+gO\nOS9ps1fgvAufmHvKebQG57EiIiI7+PWeOY9WcF649wFz3sV96bK769x7kPJgBr4G7zsBnThxHSMY\nqA3o1T7q71/B/Ooz8ObAKuKwvbQQ2dtL89eqDjDx3+IwkkaLPw884rknv8NjMz6KMBSmwxPsFHl7\nTrBTvD/74in+ZwOMQfi37w/w1zenmCsFk2kokLfRuZTaNBiXjtadU5rFw+kVmdRwxStvz2tswEZY\nA+GmACtKwme4WmEGGxOAhhzGhvUomIMS5VD2vlzsnyLAhLaAYNmBUjZuZpCJlhk2BuTggzOvaBRy\nZVBqA2YN5ixYWma1QT1b4LysQzq1s/z5MIM1igiG2YKXz6uxs7QMGwtLtT2vajvrq2YgU0pgRCwn\nRQTtprRb8BEWKuFYqudQRyJAch6W9PIyciT8DnENCHZh4YdjzBdYvpuWxzSjRpxQeH5su4G4J+J0\nbux26XBWWHJAZK2y2/0cj0Y9fP1kA58/HmFjmFtLGQIQRctaKFMx3Z1EusVPc7cf8V+on+ghAE/i\nPngJx3EqfXTjp7u7dCoJvhT8KGeRzrIMWmuYwzOYvSNkkxl6VbVcGZ100slacsect66tQcqqF4nL\nKOY6chthriEXcx4s5xFuj/M4cbia86iF88gFkU6xSTmPBOfRmpxHHeetzXm0JufRFTmPV3AeNTiP\nawbdEee1TGe8lPMkyTwEziPHebwG57HgPH5HzuMWzouZWM15TM7SKl2Tvc6O8zwKcoPzSHAeX4Hz\n2nq7NrmLMbOrDHq1uV03L5fFf4e663LpBr46ub4YA/1qH+b7l+DTfwKe7sQ3ReO79xXgsyvXbyAA\nACAASURBVHR+MTCsFBm2hCSi+HsRrgY3Mrnuml/83p1rEJ48f4Ri0ENOjB4BR0fnOCwNJrUFEOMg\nwtsCoqJcTXz+e345xdhnKa7xIMtITHkPMOR/AaV1Eo+fcaVc2ogZcGspKPiFLiPwUNjNyEMPBQAL\nqpTiVshKkd1RCQwmG5bXTWTI7b7MdqcgRTDKoMg0jDEwPt2wazSwcetHVAamNHbdhroOitE42KmN\nsUYXD4XMdlqyg6ag7FwafX0romDF8daeWE4x177sU6Usa1GcBKiw/0UFv1TRQV8na3t4rQzZTAWE\nBaDhwKVLYSfUE4+jV1F5iIvcytcM/wi1PS5EgCZrBcy0tQBqsovZF5nGo2GBL3aG+PrpBp7vDsPn\nCj4rco2HOMjkgTONR16I0+ET0owgJYBVgJGnqQCPlKQl/rkUpHUrB8D8HzwQKWitofMMmVKYH57C\n7B0in8/D7l73TM930smDkBbOW+dBusrD1kYiq4D/LkXGeRcvReuJMSQ5jx3nkeU8bnBeVDpSLua8\nFg0ZJK2rds5z3+mxVyZtS2Z5zvNbBzqgWc15Pcd5ReQ8FpxHHwHnEQD+ADkvjP21cN6S+r5FzmOZ\n+o+A85JxOsF5LMv5ipznFrCj8NnpDXIeO86jK3LeZX33TemWu9BRyVDqHYTT1iRvXLqBr06uL4ah\n9o+gf3oNvDmEef4IarMPO7PKXOr95kR0lAE83PMjySJxHkGnoXGiPwbCTDNjwrVBv8BXv/gUNSn0\nMo3ff/sWf/zpEFOtUeq4la+B2/RbKEuvoljGFe7J9RxEgv1MqeRehB8LRBYc4hR4ClPTfRg+HF8G\nhgEygnACcDGIDQgKfh2JmI4GMTSqgWDLiBAHjBQRoC38+EwopWD8duGMAERkGMYvbspsl+hghtEq\n0gY0mAHjdjny/i1ISuUYMp/ApFfMnnWDe1k+PosuHc7jhd2yXxw2VNvSe0AcNBQ24cuFgMxZJZPP\nHkRbWZaWimnonfa3k+iMfPuHzbYit8OTX3PDwVBGhM0iw+NRD794uomvnm1iZ1QEGIrAEss5glAC\no6FeZIojFDahSNj/fH2GcJDAcLgGjz4UHQX3/jzQT7BKewugByM748tNja9q8JtD0Mt99OflnfZ6\nnXTyoUnHefdQGpzHzx+BPgLO6wvO63ecl5Rrx3khShcGUSi5jvNceGtxHt8A58Xm/jA476YGk+5K\nbn0g6q7jejADX/n7TkAny8IMjKfA2yPUL9+i/vypHfhSCm4hBOH2wpP2LoBX3JNulu77ns8Dj7ws\nrslzGVBT4flzpQJU5HmGR4+3oAjYyO0CmNVsgZfjEgeLCgtDqNxUcOXiEVhhgxGReCTyOpubZSWA\nKlnUNFgCvRUw3tcKKEDQzFDMAHl/MU/W+kcAmUZRO5hx3/nb4kxSKdItSpCcRREUQJLIlh25Kfcm\n+CG74ZItjGCpJEXhk0jFLYAoTGAsVKb9JCAptaR+o3/hJrGM2cSyJE/vXAJzCxMR4BZtt8uKSCYn\nStdhSKGkvXFLqxTBfpKplYc4tlZQZrurUch328OQ5iPCxnK8CVAgsp8/1mQ/8cvdp42agEIpjAqN\n51t9fPFohC+ejKwFUPk1HSIISXAJMCPvhThF6jwEIQWr5Dc4TcEqvRbbovOWxO/Doei0tQz8blMW\niDRUbYDJAvzzW+DnPWTzxZ0SQiedfGhyB5zXVFs3Kbf5MvP+XpRaOC+znMc3wHl84b2mGm8LbDXn\nuXEn/53Y1Tlv13HeqOM8Hxy9Z85LplC1cB4lifb+V3MeC84LkLcG53lA8ZxHqWtRBi3iOI99zA+I\n8+gSznN45he4TziPL+E8ukHOIxl/g/MC7t0y5/nEXUXXvC+MvE5a36e0v1itIQ9m4KuT+ykMwMzm\nKL97CfXZE2RfPhPTpxsOvVylibZCjwjv2o+o7x3l4Jc/bUBSWyKYsbE1Qj7oQfcKfLY7wv/9+x/w\nh+/38aZklEpBFRrsFSXgLINOVcvBFQ84PqpkjCYlJA890Qpmp4J73a7YTmkfQmETChXbKeOGGtsk\nOUsPO7BKVIezfpFYv9ZPgUdMabizZJEip0zYh8jBoqJcWAyGImdpNJ5RTdh0Ka4569InrXveaijy\nQsxh1yKbVLu+ARHBmFioaTACEEX5LonIt89SVNUNpvLxU7PFROtXctWl2cNuUxQRMq3Q0woFCIYJ\nCwO3A1Fc3yKxDsr8uP8lhi0/NBEY7IK1bitn8rvc2HpTRNAgKAYyJmwWGl/sDPDVsy18/ckWhoMC\nedjOWrQlkfcEXGDDDeAjytXfj4Y/WvLvXS5bCYPvlrAF5Mg6IABKHIuwwnIS5HY3yjTyIgfNStT7\nJ6h+eIX6pz3wvFyqv0466eTeSltn2EmLNDlPf0ScN9oa4cuO8+4T5xGB+APhPAI6zus4r5O7lG7g\nq5NrSlSwPJ2j+vYF9KePYX73S9D2wEKRmPKcmmDEwTro2WSTVgfrJNkp9ahJL/cTLE8uEeSugZDl\nGbIiw2efPsao0DAMbG8O8N3+OV6dTnE4KTGpDeZeqSgbjsSMoGp9UTQ1crD8+VOOKXd63k90VwwU\nAPoAnuSEp0OFuSJMiXBSM2YcLZIkwmGKip0dyNgcioJP0iVNbjHMkBsBGn5qe1LXjbr0FsSgCNnz\nGsHAeGta9BbqMSpOv95EvOZ3jiEoxUm5BUgQBrpgeG6wcbjo6pySO1J9Rz9Jq3IwtwRC8EUq7jhI\n9Ze0Uii0wkaRYSvX2Mo1yopxPq8xrgjTWoGIYciAYURZxwiW30MkNDjQEPXgd+DRDoi8ZU4xoAww\nzBVGvQy7wx6ebfXx2eMhnu0OsbPZh9Yq5gER9kI8vnzFteCi5b6EyGgBTNFOTolPyzJUf7yNCFVt\nU9tDmsM1Ettaq7iofa6R93rAqyOUf3uBeu8IZjqTxdpJJ53cjLS/va0vK8mhEd59GgS7J11JO+cV\nv/sl+N05j5fO2murDUIa9+K54wUxCnBtzmPBefTZp49p43Y5L+T+HnOeMzEmnMcrOE8su3VjnMfx\nWivncWxuTC2cxys4j1s4L6ESf+eGOI8d59FHwHlsn05X/suc56eK+YNQcnfEeXQLnHefdMpFstx8\nbl5uW59dqZy7ga9O3l0mM9R/+wn1012Y/3IElSu7BgScZk0sUA2/d/HIhbgu4iYBO8GtSE8CRYid\nvmGgZvQ3BuiN+vg/djfxH//hU/z5Ly/w3//9Ff7bN3t4M68xIwJlGqQVtLJa2e0vGf6MiNZbnCIg\ncUiGTHVU/RaJMmaMwNjWwFcDwle7hCkrHFeEv08Yi9qggldCUT36AaMwY8qnwtedAAcBFK3lGXcc\nimXHDcUccxjP2urFWqbUEiR6pRZ2oAEhllLUlomFKMyAd+vbevJzHhIuD0UQUyahxu2VHo6bafP5\ndh5b8hZzHv+PQOrPcq0wyDV2ezmeDTM8G2aYzSvsndRATShrAmmCUc4KCAtGfhApWDBdPj3oGg9p\nFLcq9+CmXJl7CyB56GaGrhnbQ41Pt/r49Wfb+OzpJna2+shyDcGiadmH8pMAE0EWjaMGwbT7S655\nt5QWe/jPrX0KST3Ci6O0AEhoABLsrlQehnSmkec58n4P89eHmP/xb6iPz1bUcSeddNLJByANzqP3\nz3mtIfHSqJJ3GjhPjoSsy3k82BhQv+M8X8R0TzmPfVw3yHmSboJXn2/nsSVvMefx/4+e8yShNTnP\n7sDYcd77lFUvyR+MdANfnVxbQt9tDHi2QL1/jPKbH0H9DGp3A2DTWPuUWx6fS4iIms6u+PwF/0sB\nrYpEcBJ5MknT50135BQyEXrDPh493cE/MjDaHOLT57v4Yf8MPx6c42i6wPGswqQymBlGRakC9oAU\n1kwQKWL2u/HEaAGCNtZfD4R+RtgqCLt9jSdDjSebOR5v5JjXQD43OKkrVDODUztWF3mvkfXE0ojl\nGvHA1gRdhhj0cmGYBgh5UHFz1V04bugolLN1mcBfAiMxnaGknL9UCVOSN79LugTBoET9ulwh0ug3\nJeR4LaRINGmvRINVV6Q/hJLAXfvbgd1mm9DTCqMiw5PNHp5v9vBolMEYxubWADtnFQ7OS5zOa4wX\nBrPaoIIKW4/7thnNraHhwE9SF4gRIQMAsZ3mniugpxUGhcZWL8f2IMfznQGe7QzwaHuArVEPRZ7Z\nXa7Yla0AuvA/hW2tl8ojxE1pzfgERm6hEBiFsqbwmzbmlkvivNm25a+F8GgR9dColf0UIc8yaMOg\nsynMz29R/+0F+Gyyok476aST9yirgOE+w3wzbbTmvRuXizgP/Ywu4TxOrrVoVREJpT6SLF5UT1Ip\ne2XuImjlvKi0o7+1Oc++LBP3hn3sOs4bbg7pA+K8kPGO8zrOe7YzoGtyHjfLQ3Cerz1R0Eucxw+U\n85rN9zJZx/1Fbhod1a3JXQHtVctO+rmqv27gq5N3FwaAukZ9eILyL99DP99F9tuvgEoJxcHpLzVD\nkCoELYxySbte5/F0yjiJt/nohMfP95zJRQSm8pcUAOM+s1Ma/Y0hvhr18cnzHfzul8/w3Q97+Pe/\nv8G3b87w48EYe5MSp6XBlBkl3G5ALii7MxDAosz8MQtlTbBTknOyU943MmCnp/BsK8fTrQLPdnro\n9zLkhUZV1tDTCiczg2llMFmwBaI4z9zlzFNSCmQJjVJMS/IlQbgWIScBpAY4SfDxQCkJKEBPS3sg\nCVkkUieaD1GSaslMSd68m8C7abAAKCl3H7OSbTOAVkMExLVJM80yvYqsFbCvFTYdED3dHWBjmCPT\nCk8Z2D2ZYfdkitfHCwdGykERw1AEwDTkaAEMLdrVowrwYddjyAgYaoWtfoZHGwU+2R3is0cjPH00\nxM7WAEQqTbNK201yZIEnfKohrXwBnFoGvjwoN+/IAbTYhGWY9jxaJj0dCpiKl0L6wrlCLCdl/5RW\n0JlCXmRQZQ1zcor657eofnoToK2TTjp5MHIZKN/Vi8Vdyjvlp8l56nLOi1OjQwiJ0qPb4DwigJlk\nvKkybuc8XroYx08C50Fw3pejPp4/38E/r8d5fiLOfee8MKXHz+pag/PoGpwX3gSuyHlxVQoiGQ5A\n8hPHlZxn3wRSzqNw7BLd4DxfY2mwkaVaW+YNch47zqMLOE8MzdmJU7fEeSFLjWy7yVTEa3AeL4VA\nfg5WK+fZiX+Q4UTfa3AeC84Lk8IanMcNzqNrcN5t6Ix19BRuId67lisNXLXIlfLfDXx1cmPCR6co\nf/9XZF89R/Gf/glEfmtoxlK7XhoAY9FtJAqy4e7SVFzrVoxkhSOS6RIJIpEXl1Wd5xhuj/DlL55j\na2cDvzo6x8HRGHvHE+ydzLB3NsPRpMTJtMK0MphVBqVhVAzUHHWYIkCBobW1DhWK0MuAfkYYFRob\nPYXtQYbNgcZokGHQ0+gVWuxMorBBCp9sM2YGOJqXqJlRkd29yC0NGpVHo9xDbkXe/YRr6dQDkfcR\nFuH05y7o6CbqCA4AFcvZckZaH0GtM7drGA9X7oSkLxIUzj4XwisRmrGRu56gaIiDI0ReQZJ1LIiS\nMHwaMiLkRNjqZXgyLLC70cPmsIcsc7v+EGFne4DhsMCjnRrjWYXTWYXzaYXzWYlpaTAtDRaVQVkb\n1MYutsswaGKLgt22OtcK/VxhkCts9DNs9nPsjgpsjwpsjQpsDguMBgV6hXZAwuFZ9Y9s+KKgCdUU\nnDZLo2G9RXIc14twtU2iDgSBNl6l0vAEg8V0UACeQEgRjNx9e0t5GNKELNPI8hx0dILy29eoD09t\nftuy1kknnTx0uejRvgQUPmyRnIf/9E/AxZzH74Pzlr64W5Kb5byB47zN1ZzHD4Dz+D1wHrvFvJpp\nuYzzQmVcg/MSM6vjvDDyeQnnraXub5jzWHAen08ruoTzmBpjdDfEeXwDnNdENcl57EetGpy3PH54\ns5zHLZzHdHRC1+C8u9QL3DjuMHRN6Qa+OnkH8Z2ZU3rnU1STV6h/eoN67wh6ewAaFEBtIDTRMuzI\n4C4CoTYv8jG/sLu5Qj8ke9ZmvEE32755yWOIxioKnSlo3cOTIsfj3Q18/nQLk/MJjo7GeHs0xquj\nCd6ezrF/VuJsXuJ8XmNeGSxqRuV27wHgdllh5Foh14SeBvo5YZgTNgY6gFC/p5FlGiBCDTv93O5y\nozFQCk+2GJPS4PVZhbpiTBwM+aRLe+eqgglIFCy7AlQ86MQL9scDT/DXUiec/MAqS9uXs2g3kj+W\nZ2nJoRD/k1ZkW3Ni79/VsVycP5YJiXiAaBT2A3TNtIh4ZNtPg3OQZgvfr7uQa4V+prA7KPBsq4/t\nYQ+DXm7duJ14ekUGRcDjbaAsDSbzEuNphbOJbUuTWYVZaVybgt0RKqkpCwSagEIr9HKNYU9jo6ex\nPbQgtLvZw8Ywx6CXI8s0lCYHtbH+fbm7CV/inUZMXxdQ0iwC6TYB1ADpFOshhNOgnoZBH9EbPOl4\nIAreyN+ixrGHsDgNXmm7rbXWGhkpmKMzLP70Ler942asnXTSybvLOko77S7Wl1VEsa7cxcvNqjw1\n7123LK6UlIs4T13Mee1piZzHyTV7ZdnPepzXMlK1IjurOY8bnOc1h1zUiiTnPS5yPNrdoM+fbmF6\nPuEG55HjPL5lziPHedxxXjOetTgvVHrHeQBbsVm9O84LPUD8KtI3xxvhPG5wHnWct1Kuouvk4Ns6\n/m5CF19LuoGvTm5M7JbFBtWbIyz+9B2Kf/4KauOpBSKrabDUxsNgkjtmIGz3shSB+70FtLtQlnpq\nahm4I5tm5Y6NcxOcKeS9AhsE9PoZHj0a4hfzEtN5hfGswnReYbooMStrzMvaWW8YxpEKAVDK/mly\nvwpQmuBmIsPb3JgBYnaz8i0UKSJsbyg8rxhfnS+A8wrTuYFfgDL6ZXgrSVrUEj4EkohiaC5MGi2B\nggeS+udgGWSgObmrWejNm4kLf5fglxrxC1023Le9HggQSzGXGgDWAjoAllV5iwgnEiQlRyv4XRw1\nhpnd2ef5tt1NZzTIoTMVLFXaK2oiUEboFcDGqLB1bhi1YVS1QV0zqppRuWvGRDAlD0R+TYNcoZcp\nFLlGrhWyTCHT0fIIokZZiDYiONRPzmoCyVKREAnoEcXuwpPHIRgsuxXAFNMn4qaQlpaImqKo9TNH\nu8NPhowU1LxE9dMeFv/vn2A+jsVOO+nkfcoaHeydy31M051Jx3nuuIXzil4B3XGe8N9xXsd5Hed9\nYPLg9V838NXJzQoz6pd7WPx/f4LeHkA/3QG5Dvhihde45rVj0HTer1QmzfAa0tbpLT2uLQPO1LjW\ntii+DIdCIuO9MN/WAwagMw1NBYpcYdjLwHWBqqqxqAwWiwqLqsairLGoa5SVQW0Mak4XEQWiQmNY\na1/NBlXN1r1hGGZo4yyB3hrIDJVrPNpi/HJRo1YzjA/nmDBjJorUGzoTOFghcvq2rR5OUynqTAKT\nv+xdx/IkeSOWuJgiH0CLRZgEUFirw9dLmotlho0T2alRx2HyPDmPbXAeI47u2xpkMy+NewSAFCFT\nhEJr9LTG7qDA840CT3cG2N20u+lopUAKbvtpB0TK7kSjlQUbpQhaqVDAvowMuwVoTaxnP5U8bGut\nrV//6UQsN4YxSNq8h6EUQig2HN/8xTMhgYTggSh6TSoveKMQ35LxXRx777G7kNejX59nOQU+WdvL\nl0coYwXt/vIsg15UqPdOUP+0h/rNIbiuG7nqpJNObkHu28N1l9Df9hr/rmG9e3lazmPBeeQHIhqc\n15L+W+U8FoqJUkcrOU+mLfpen/PsOkKZhqICeTvn0aKquYXzyHFeyLjT0yQ4j1dwHhljA7gnnCf/\nows4j5IwXbj3iPNSICHm98h5tAbn0S1yXizWyHl+rSxfLnZyWXxQLuK8lqfzQs5jcXwdzmPBeTG9\ny5xHa3CeD/2qfX9L57O2Hylt/m9DLyZYf013d6kfrxVnN/DVyY1L/eNrmMMT5L/8FNmvv4Ae9awJ\ny6/k3tZ3JHgUFW275Q2XDEhcIO/8OEoguyDM0DsTrFnB2BVK2Z8rMBQM15bQiKDzDIVW0LlGYQwq\nY1BzBBzjrDjJOTNganANZJrdAIi9zsyo3R8bDmsNbG/0MMwz1Kxwfr7A6wVjURmwUgABNVLIWerZ\nAhvKQax0GY90kVMW7tOCkgAV/IYIhf8AQQh5QxKHbApxval1JShJF4fkbPIX4LbRTubcC/9Iy23t\nuJ0C9hbAnlL2s9RRgV8/38Sz7QEGg9y5deDi3JOSihthW2rAWbBi0pcGluLKGcLC624bINQL+b+W\n8vT1RdKz/xGw0/TchKG0PJavShhqu5+ELJh46XNIn5k4Khf+fNl6SEqnvitkWiPPMqjjCRb/43uU\nP74Gs0EnnXTSyccqTc5Ty5y3rBE7zmvjPHaDV9flPO44b7V0nJfm55Y4LzwxKziPO8671/Kuvef7\nGPS6snQDX53cgPhu0bX5qrbrQPzwGtVffwL9w+fQ2yMEFZsoPSlNqxCQaNsQ3bJibiTlio+f8LTW\nWLdPpPwV8YaO17klsvPWAQAKpBSUVmAGtB8IFJ0zEUGxglImzNYxhqGZYbzFzzCosvEpMFh5Kw/C\nLJ8w8AVXNwygx/iysoqw2J+Aj+c4N4w5x622jSuEC6mV5RVOLsf1HdC4jlhfEqi4PS6JVAFGHFh5\n6JLrAyhSUEolW2uHfXo4KkheisGJgIYlEJTXOPHgsQLU0vBac+ZgQLm6zpRCrhQysjv7fLLZw5eP\nR/jk8QaGA7u7j80foJylTisV/IddepqKHQImljjCL/5KYBKpbG371ApESetvgFGATKQw5XAtpC2N\ng5I6SDw1A1q6T80LqZcE0GJZBYO9EmVJFihtWdup73mmocYz8Mt91H/+DvXPb8U73RVfyjrppJPL\nZJ2Hat0Hr9kxt/Vy16KGeyR31Amt5rzs6pwXFUXkPDFKcu84T44qXJfz3MwTOxtFcB63cB4LzqMG\n53HHec5v5Dwiu2DYdTkvTkda5jxq4TyKTj86zuMHxnm8gvO4wXl0g5z3Ljrlqn5vQ3+1ZfyieNpe\nna4ax53o4G7gq5MblKgU2DCqH19j8efvoJ/vQG0NowvTYNDQ7FnwBcdeVziVM2WX4iVgybTRfGy5\n5Zq8d1G2JPcEdy6wZn8sdqGLQAQACmAN0gy74kINDcDPMWZjA2Jld+IBExScdU/5ATCCMva7fmYD\nu35sGhcDYOPXf4gwYrmM8LnW2B32UDMwm1aoZzXK2hKRQQQjlsWJlrKVhRF+loEocRmmszdvsoAj\nTqOT7hlg2LncRAQFBSIFTcrBgUENTqqIwUEB0wUABoE0wWvS1GwYlwxrtV8TFwgQFj1CrjRyUigA\n7PRy/OLJCF882cDjnWGABL/WAzkroNYqVnfS1OJgTlDuYcCPk/IkxAE7yfCtsvS4xRUvKL0srvhE\nySsUoCimYvlw6XQFDF02GUACYUieT5coo8Sy6q2uWiEvchRagw5OUP/4BtVffoDZO1odaSeddHJX\nInuna7+dXCDXCU++K143jOukYRXZvIu0lG8r57F6vkMNzvPAZNMmP26zPEWC81KFmnKeO0k4Lw53\nyCTJVF+f8yh155JPjRS1KV8FkOM8pJznvyUjx3nkOI9vkfPoFjjPjnq0c14cV2E71HJPOS95q3Cc\nF146wuiZCOGWOI8eKOf5FIlEifGn1ZyXlP0FnJemkOKHyBfJO3Aed5wXJOl0W+SiXrX5Buzdris3\nMdh1Jd33YAa+5u87AZ2sLVabGJQ/vcGil4N+8Qn6wz6ynRFAyg30oNHcOfZYCXAg7a0TDGsw5mXo\n5/02lXFrr3qN51dCG3MCKSFI5aAIDGKGVgYMArN90QYZv3InADtGSLDrqPqZS8xkd2+pDUxduyng\nZC0WikBKgXWEmpBnsnqn0BqjQYH/CMLGqIc/fH+EH45mOKoMjFtDgOG3RvbFJQs3oaTwk05Ll1DE\nyaDXUrE1YMjHZ41/7GawMfzUd4K1jGkPQqygoEGkYKi2UMTRYrh+9aVu09w6oCJeaj6gFsADlgDE\nW/MyB0QKCtoA/Uzhy90Bvno6wlfPN7Gz2Ueu3TR3FS1U/lcrCrxR14y6NqhrY5ubsu0g1wqktVsU\n1wEMxzpgUZ0UM9leLvK1QzyeElNc6STX/A8lrpfenZbUpnSZruOQpmvpsaV4PYKXrzd/xVOafS6b\n8OgtyloraK2Raw1dGsz++hNmf/kRi+kcBktJ6aSTTm5Arsh5zV7rpgabZHhXDeuyl4fbEBa/d9Y1\ntXAeY9inCzgvAoHtzIUV6VLO4/vAec3kXoHz+IFynhyS8j+e4iw23C/OW+u5u4DzxId/YCJe/lC3\n47zwr+O8D1IuG/S6zM2DkQcz8DV93wno5GrCAO8fIyOC+uYnYHcTw40BVK5iBywBhdj2vJeuHciN\nTlRqHdFb+x58nce0bSp9DLTlPq8I10Oc750ZwXrptSIrABqkDEgpOw1eAQwLR+BUYTEs+xlys8KY\nwcagqmrM5hWq2u7os9EHMiLkGQFKWTMJLescP42618uxPSxQzmtb7GcLnNcGpXEbFpGNN3DrUrY5\n/fEQ6MrUQ5BwkoBDWMfBuxGzwfzU+rhrEIc0KCKbT0XoK4W+ygClUJPCgoEFA4D7TPSialpO/tL1\n9ESq6Vgmcp5YO1DZOrAwRMjILaJJ9nigFB4NC/zy2Qa+fraJZ49HKHK7XblfyDRYq+AVeCy/uq4x\nXVSYzkoAQJYpDIsc1M+QKwJBB5rwnL5k0XX1lrxruHsWQGXOY3NOiiX4cGhES1fDWWJQbxKUP6Mm\nXLVPxRdRpsF4ACJ5HNMW/xwIwcOQfTHRWYa8yKBqhjkeY/y3nzH59iXq+SKNuJNOOrkxuUXOW3+E\n493kg3gxWFMIDG5yntoYQOdhG8KLOO+S4auHy3lM4BviPHKcx7N5xY7z6Aqcx1oplJTJhAAAIABJ\nREFUcpzHDc6j98x5LDiPrsB5VJPid+Q8bl5YwXncwnlJu/3AOM/jHQvOS2DMDzE5lnIYRx7n6B5x\nXtiosoXzyHEeC86jNTjvLvTIEl2vGd9103YbebouHDf93Uo5P5iBr/H7TkAnVxYGoMZTlH/4BnW/\nQO+zJ1DbI9hJ1t4ayKkPqb0JaN1VMQilh03N781ooUu/SuKv8byFuLipXex/yqXJQ5HSgGI35Zyh\nWIEJYDJ2qQhXRCZobjvgVdcGVVnhfFricDzDdF5BAXi+UWBn1EdfESgnIMusAlCyQOOOJvlQo1/k\n6BcFPnt6gt9/d4C/v53gx+MZoAnsdCnBr1ebkloy3d3n3Lg7ATYaCCXgJ+Sp8fmjD1eGrQAQqaC8\nCiJsaoXHgwyfjHLMDWFcAgdzxmnFACkYZdxaED58c8VedNm1TVOk+aX8CJHrMyhy1j9SICb0lMJm\nrvD10w18/XQDXzzbwO5mH4Xf2UdMy/YoQeJRYGNgqhqTyQIHZzO8OZkAIGwMCuxu9PCIexiSX7xT\nOQuyTxkhTObn+Ij5umaRdRLPTvIkNh49aexuLeN1nj2KaZCP7Wo/tHQsp/5D/oqBrjDo5cua4ueN\nWttt6QejAeoX+5j89QVOfnyN6cEJUNZrJKqTTjq5jnwAnLfuMMwHI22cpy/nvPRLKF7SLnKkKz3s\nOK/jvI7zOs7rOO99yYPXcQ9m4Kt83wno5AoinotFCfPja2SPtjD6p68x/OoTFLsbdmsZ4zsXTnvh\ntnBWxZGci+vhNjecN3t3f3nZNHTtxzskR3TY/pwAKAKxArSdDq+YoTlOh/eLMLCJhBiWwTAGZVXj\nbLrA/ukcx9OFNaBWNTIGtjI7zZoyOxPKmz0QrElWIWilMOjl2OjnGPYzZEpho3+KXnaGw9kCJ4sK\ncwZKMBQogpkDHg9ISXExx+qEgBBZLRKGpD8BUr7K/KRlEEGxBYSBVtgsNJ4NczzbyPHJZo5FBZwv\nGPn5/8/em3VLjhx3nn9zLBFxl8zKzNq5FEtsklKrW6c1Z2b6aeZxPvecozM6vWg0IkVSZFG1V2ZW\nVm437xoBwN3mwTdzByJuRNw9C1YVeRGAwzcAbr8wc5h3KM86HLcGC0No2cAjkbHhYaO7z5VvY3el\nEBu/h9TiIg5fbri8/HZYklp4miaFwk5Z4NFujff3Jvj0w3389L093N+fYlKX7vZIp2j7vgIAGwXX\nAvF83uLlm1M8OTjFd69PUCiFR3szKAKmlUJZKVSlikDsIce9rul/b0hvJvmuyYAnTSX6ghEBI6T0\nRZF41DJPXvJbJZ6Xe/vCEervTfYFGOpVMj5z4TmQYBRfL/BAVFb2WSg14/Sr73H4L3/B2dOXaOaL\njX5TjXI7ZLxmd0eWcF7PvDCwvUwuosG3leu+5dYtb5t6rThnkPN4968/weznH9IKzhsY5fN9/TK8\nNuGb47z+fbge55HjPFbM7DiPHOfRlpxHjvP4HM7jJZzHV8B5PiiUs2858rs7nNeL7ZXLJXMek7tI\nt4jz+rtcB14S57FDsZzoeIDzQvZxF/nbpH+B+pznZ3rRCs6jLTlvaGTZVM471w90Vy2+HqvalNfj\nMuq1TK8PlXNuP29SoTtj+Brh9Y5Kp6HfHOPs6+9x8M9/BpRC/f4DO8Bru7DycvvVkufCj9pynzCm\nJMn9WrxDZeTC2d9lcm5e4oe5nG5M4m8IYA+ADZQzehWgoI3Y2FcYiQFN7JYztvDQaI2jeYvnx3M8\nP26gtQG3GjMAH06UnT5dFACVABURiFxwUOV1gyIUqsQHj/Zwb3eCDx/t4uMHM/zrNwf48/dHeNVq\ntGydlnZus11Cm2Hs9HhhzArT1H03sd8vjF1J/y3p6ABDNraDv9yKgQqER3WJD+/V+Pm7O3i4X2Nv\nWsIYRtMyZtMGszcNnr5pcLBgGCYYshdJge3UftiXDViZhIFNKJ6Tv8ja5S8tuWvJzIkBxcamEB9H\nhGQIe5MKP3lnil98sIdPP9zHO/sT7O/UUKroLbXsyzbGAag2YK2BTmN+tsDByQKPXx7h61cneHa0\nwLQqUIKwOylwb1ZiNi2hjQYZu3pNPooOPgLZ4yMbHO7ksBEhh+Q5REsfsQi4iMaqgXRpTy//ev75\noUriQ3Ef7DNBbhXHoixQTWsU2oAPTnD6h69w+N//CL1o0jaOMsooly4Dz9dFfliMcl2ScR6PnDdy\n3sh563Ceu8FHzlv19fzzQ5VGzhvlXBkNX6NcqTAAaIP25QEOf/sXlPs7mP3sA5TTCkVVAJrh5k3L\nMwTvuJF4KPbXEC9JEOolWiG9EXxZeUPUlA/a4hhl9fCABnZ0QyAuwLBT4a1GcsFPSYGIYYij56Rk\n1FWJHRef673dGsoYnM1btIsGL94wHheM9zuDhwyU0xpUV+CyBMNOubdKQbmqWaVWVSWqosDPPiDs\nTCrs7U7w/js7+Pz5MZ68meNg3mJuDDrXJGXdKZAYlDBP1leyh2JcgbwHKUAIsQIxUIJQK8KkIOzv\nlrg/K/He/gTv3avx6N4EO9MSVWmXDJ8YBpUlZtMKe7MKL48bvD5tcdJonHYGHQPawZDlDRWWeTYw\nonTvOxTXzt2P0tslPYhevyuIOA1sYXZaKOzWJR7s1vjonRl+9t4uPniwg/femaGuC1RlATkTz98n\nbIz9aA3TddCtxnzR4HTe4vXxHC9P5jg9azBVwEf7NXYmFR7uVtifFKhL5eA5gqm8JYdhxd+vLPek\nm/65FH2R5kG++oPHfQUo+96vx0DdBrbD+dnvDbvb/whw1wMJcMK/ZlCEuF4FyrJArRT001c4+8sT\nnH3zDN3JWVLu0naNMsooF5Ilv3dum/HrNtbpJiQ1bWSct/OzD1BYziPHeQKeXCjtyHmUcV7s4z4o\nCNV8+ziPABriPB/sfgvOo0vgPHKcxzfEeRx7kODjZG3BeXzFnMcbcB4LzqMlnEeO83gF5zEbQ7eQ\n85K+SPO4Mc7jLTiPBedRxnksOC+pUlb7vCvXhcC3GRa37ZNVeV1Lf90Zw9cod02iigMAfXCM44N/\nx+TBPvZ/8wmmH78L9egeSIt3qb1nbGi8CT+4/ShPFjz8fmko83+YbXrPW7kRKmcmRm9wTqsxBEND\nzRZ5hBgOnOVvB2sUNvIDoQCxXemR3Px3IoYmYxWtUTBuUJ9N7KkfdgY1GzwsgNeHBs8Pz/DqqAV1\nHZTWeKiAkg1K2CWvNQNcUuxDUvCeGQKBCsK77+zg/Ye7+Pi9Pfz64xP8zz/9gN999RqfLQw6zejA\nUIoAtyIQkwci95+fyg7b7Qy2YAAED0yEz0wzM4Up9MSAMkDJjJ2C8E5d4icPpvj40Qzv3p9hf7dG\nUaoQAdS3Y2cGPLyv8f79Fs9fz/H4+SmeHbV40WrMDaNhBikOl8LW1oCYwM4X6NsULz0HRRtXnxGg\n64/5e97fA8Zy7awu8P7eBL/++B4+/XAfP/lgH5O6RFEUoRwLUi5HBowxgDbgroNpWujGAu/hyRzP\nj87w/dEcL08XmFUlHk4rvLM3xe60wmRSYjatsTNRKJW9vjaOhvwdMQAgfjvcv5Qe83UT36m3M6aj\nPI04SGnSQZHlhh5PVnGlLAMKj1RSfvJJjV6KlFtCXLmp73Z1nwoK88cv8PIffov5kxfC1vc2M8wo\no9xKkUC8jbHpqh7auzIYXHE9l3Ie7//mE1rBeZkaSjnPxipiOo/zyCo4gMjb1ZiI3Bl9zvNYuCXn\nUbq1GefZxtwY57EzD3jOowHO44zzCMquQrAh50VFP8x5dEWcRy9azWtwnr0NUs6jC3Iez+qC3t+b\n8K8/vkcZ5wVzlOM8W/7d47yY+cU5T14aivi7MecRrG2WiNw6D+dzHldFQRfkvN4v1S3S0UC6dfPd\ntB5DZS/Tp9vUY6hO2+RxkXPXFhoK1ncb5b+TuhsVHSUTDv8ygNnPP8Deb36Bh//nf8G9//IrKOex\ngNaOSHj5iGk10vJj8Mcz0vEJwrkbGJdFVpn2Tjd93eVUfP8xnH63Gi9ua2O/Gw3W2s300TDaBu3U\nbKdBG2OXe9ZaQ2vrFTo7azA/OcPJyRyHJwu0TQcYg2lZYlaVmE0qzCYVppMS02mFyaRGWVco6gqs\nFLhQFoycMcsHf2w6g7NFhx9en+Lpq1N89+IUT17bOAPPD+d4edzgpDNYGB9Y1PVrUKg+mKnvl/hS\nPoWuYnuKsXESSiJMSsKsIuxNS+xPS+xNC9zfqXB/p8T+bo29nQqTukBZqmhIE4rWO7K6zmC+6HBy\n2uHwrMWb0w5HZx0OzzoczTucLDROFhoLbdAYAw0Ld97xzOHe8fiQW3c4Xm6XVIFQKsKsUtiflni4\nN8GjvQneuz/Fe/enePfeFPd3J9jZqUJg03CDGQbYLl2uu87FdrDX9+RsgZOzBU4XLRZth4U2LlCC\nws6kws60wu6sxnRaoa4rVHWJuipRlgWKsoBSsq8ACf05GpF4XgZ4Q/wKHXp2stySHwfUS0Hyn4y8\nSNTTb/ufM0kbyNebQnlxmrvzdofZXSosG66IUKgiLGdd1xVmuzOo4wW6b37A4T/9Ga//8V9hjs/A\nZ4sVbR7lrsh/ZTNewFsuA5yXf7/Kazhgwh9lhXC+mXEeec4jy3ksOI/gkSBXDW785nRvUBXsrRAI\nL6GJWEXEgvOWwOKAXCLn0fVyHm/JeTTAebwB5wUiWJPzOOM82pLzfIyodTmPBjiPl3AexQvryxvk\nPHKcxys4jxzn8ZqcR7eE84TpbCvOI3+NktTnc174V3AeXyHnkeM8Fpzncx5o81ZOFymrBqF1dNw6\n5Sej2BYydP6m9d5EX2/bppUD+jqcN874GuWKJd63BGDx5AXaF29Qf/AQk4/fw+Td+6BpBbgpyAl4\nSPvU0CMli/CU5KM59tL0GM3to7hvo+FGnHfeM5nvkkRn502HhNYD2IFhYx1osFvimmGUBaJCEbhU\nmJQF7k1rdDs1mnszLM4anJ41ODptcLTQeNEaTHWLWaOxtyiwt2hgpi3qSY1qUoPKAigLGxhVKVBh\nPWtaKVSFQr1T48FejU8+2MerN3N89/wYXz49xBfPjvH18xO8nnc4ajo0xqBz8GPY8yHD+tAsNbCY\nd21X7AGI7bT/koFaKUxKhf2Jwr1ZgYf3ajzcr/HOfm1BaLdEUSioQkEHxuRevyqnGGeTEvd2a9AD\nRtNqzBcdDo4aHBy1eHXU4PVxi1dHHY5bjbNOowUseALuEz2cYO/q4vQ2IX9r2iWrq4IwKRTe2anw\n7r0aP320g5+9u4v3Huzg/t4EVVWAiMIERBaArDuNru3QNR3apsFi0eDkZIHj0wUOzxY4XjQ4aTSI\ngLIqcH+nxv5sgtm0dqBboa5LVHWFolCgogieL3mHpkBCA1ol/RVCGTwljLM2FA2loDTNEg+897IO\nPlbS7ZeAEblrQ+FHFCl7X4R4HB6OlJ3pVZZ2pld3cILj33+Jk8++RfP8AArWoDnKKKOM8hbLBQ2M\nKzmP63fvQznOi9YR7iun9TjPTm/xRrAkzUrOkwo8bd81cR4L84mijs7hPBacR4LzeAPOI8d5vITz\nWHAeOc7jHzHnUcZ53iJ2GZxHjvP4FnNeNNMFG+ZanNcrYkvO6z2JV8x5dAs57zY6YIbqlF/zTQ1u\n6xjq1slz4/4aDV+jXKuwNtCLFseffYNifxcP/re/xuyjd5FQT7iNM4jxU7cBMVL6Y25DghMBg0FP\nwzGkiZcZv/K6LN2V118eygxs4Ts7KFIiwzJkp4wNLkpkV340hu20cya7eo1S1nBVVSgmE1S7GrPW\n4H6jsWg1tDbQnUajDZ63Bs+aBqVqUaszzOoCs0phWto4U1VVQJUFqCzAZQEuCphCQYFwf6dE9dE+\n3n9nhl/97B28OlrgxeEcr44WODhd4Gje4mTe4qzRmDcGnTYwzkvoe8Yu9Qz7fn1BmJQKk4owqwrs\nTEvsTEvMJoX9TEtM6gJVpVAWCkURvT3Kacc01oTFFz9nOihvKEyUsvEyZjXee8hoGoOzhcbJvMWJ\nm/11uujsp9GYtwYLzei0nfavjfNqOmeyIlufUilMa4VZXWBvWmF/VmF/p8b93Rrv7NXYm1XYnZaY\nVCXKQsG/ykkMsJvi3nUajVu553RuAeh43uDkrEHbddDaQClCNanwcDZBXZWoqwLTSYVpXaKqSxuf\nrSpQVIWDWm9Jjb2w6q6O8DNwOy9JuyJZfKJoeZrlck4JA1aw5NUDSW2EGMsLIq4XKShSUKqwXsDJ\nBLUqoJ8dYP7nb3H8239H8/g5+mFiRxlllFFGOU+GOK8cOS/hPEZpU23GeXzNnEcvDud8yZxHV8h5\ntDOreQvOo5Hz+mlXJBs57+ZlaMQbZQ0ZDV+jXIuEYIjMYK1x9u0zUF2hfngPqqpQ398FFQ5KElBZ\n8VyvBBhggI5Wp1t7GOl7oJZm6zfkMioe7oaq6aZ/EUoHfQbEBmSMW1XGKWcGlGIwK6iiQFkyqonB\nRFto6jqDrjNYNB0WbYezRYezpsWi6dBqg4W2kHS2AGpFdoZXoVCW0TNSlAVUWUKVBapS4X6lcH8y\nwaO9CvOHM7w5acLn8KzB0bzByVzjdN45mDDQxoMKu2nHhLJUqEvr+ZvUFiqmkxLTiYWysrLv4pOi\nuCDSyith+5c9ELkj5FxEfmUjH3gezNCa0XYai0bbvll0OJ1rnC40zloLRW1n0Lr+1B7sCG7atJ0V\nN5sU2J1aINqb1dibldiZ2tcOCud9sq8vGGjN0MYCatdptK3GvGkxX7T2dYZ5i9NFg3mrsehsTJRC\nKdSTEtNJhYmDocp9yrJ0AdntdVPOo+vmgMPTQfKYCNecvEWDJy25b8U2y939LYqZZMeWU9Hwo8vp\ndH1fuIA7ObU+8fqFq2/bL4OeJqsvKR/vobBLWhOBjuc4+/M3OP79F1h8+wP0yVmciDnKKKPclCzT\n8FftFR+aPHGd5d+0bNW+8zivur8LlXKenxfjC5Jlcfi3p0zSUu8C53GMqo44zb9kq6MMERtewXm8\nBueR4zwe4DzagPMo4zx6c9LwJXIeV1VBW3IencN5vAbnkeM8FpzHxjCdw3m0gvNoDc7jO8B54jXF\n3pZ/HTEZG66Z8xztubgqt5Pzhpo8ZJJfln5dY9Z5OmpbWSe/8+p4GXlsI2vlORq+Rrl2IQDdiwOc\nao03+zsAAe/8/a+h6qmNgQAAbupxcpL0AoaVICkkj6O58655L1tiSIPYh/TxXDU05TL0eHnt3csk\nY8gQCDUTJTYIgLGGLxgDgrYGMDYugCUCGHHhtuECXBpAu1gRk85Aa4227dC0HRZNi3nTYdFqzBs7\nBbxtNFh3KIzBVAGzkrBbKuxUBaZ1gbouUZQlqCqA0gZl3ykKTO5N8HB/glYzWq3RdAaLzv7ttHZA\nZMCGA/ExnHfGxSv1U5MpKHHATzRnwZGcd+OSPg5q0e0O06DhynNlVBUwRYm9EFfDGqg6Y2HJw5zW\nHKbD+5J8DAELdwWqQqEsyMXtcu2D83obA6Ntf7Suz8/mDU4WrfNENjhdtNDGQmNZKFRVgZ3ZBFWp\nHACVqMoigFihCEVpFbp/LaAoYlwDEPVsq7mROLm1aWDfwJ4he+3QeYlGF1/W+SVl0y+3aAfIEtcy\nxNBQsSE08IkxHxzQ1gXqSYXirIV+8hKH/+0POPnjV9Bn8yW1G2WUUUYZvezryMh52IrzwAbFyHkD\nMnLeyHkj590CufP6bzR8jXKt4lUXtx26N8c4/uwbUFWiureL2U/fQ/3OHshoGwgUSKFIZpDMCFtF\nLkM1yOVcV57bXOGPGjw0oMX9LhI75HeCBSN2qz16LUQAs3LGL3cKR8gIf9kCRekUfFVZL5TRpfU+\ndRUWrZ0eP287zFuNprHAZDobcPXMGCw6g4OuRXHWolR2undVWmVd1TbOQFnaeAylIkwUYGqFriJo\nZmgubSwFhguKauHNyLo60DCILGnYB0t198lAv/Z9u3YrwFDQi5mCFHCEkC5euwiUNsYGG8SArg4+\n5XVUQPAyAYBhA93ZvmtbjabVWHQdmtZOc287g7bTaLsOnXHQxYyqKjBRdqp/XSnUpQ1aWjnPrFfg\nStkYBkpZD6EqlIMzd0zQR4SFfv8N7fQomgf2jLcppd/lcQFUIsjwiqcyQynK86VeuXGTMigjt49C\nIhKfsLKP66fCeQCrukJFCtVCY/Fv3+LsX7/A/Ovv0R2dDrV+lFFGebtk3RlNQ2aSlT/P76jkfbBO\n+5bClOsgzjiPJOdhgPNyu5akG04CeG1VtSXX+Wo4j5OVK1dznm2e4xNWgOU8EpzHsSfiKRnn8Rac\nR4LzOOM8EpxHF+Q8uiTO4wHOi3aSzTiP1uA82oDzWHAevcWcRwOcN3AFt+I8EpzHaUoC+dlm63Ee\nOc5jwXm0Jef5ROuMQevqlnVkk/IG74IN8hkqM7uIg3kNlbGJe+GisrE+Hg1fo1y7BOPXosXpF49h\n2g7l3g6gFOpH79ijGplFR0rmRht87Dgb+Aeeux5cIT1HTlv34DIkYni2XsiBuiTlUpY3IpN5rDaI\nYMQMkHKrBRkf1zXk3ddvbBW4cR9WYC7tNGxjsOs8VFobdEaj7QyaRuOs6XC8aHE0b/HmrMHxWYNm\n3oC7DhMwZoqwWxD2pxX23TTvSV2iKG2QTSg/dd0aw4wqbABR13UWeJyhC877xgwFa2gKTRYQnBub\n+n3uPVT2oJJQIPub4mUMp7sCEq1BdlUZ5bvRXz9mt0CTNST65bjBBtoA2tgp/03jYkjMWxwtGhvH\nYWFjYrTGgAAbHLUqMJvYVXpmdWkDlpb21QOvvKMHy67WQ8p/hw1S61bxiSvdwIU6GTA6CehYpoGG\nYGdVPjJdDqnDv6JSo9WwzXr4mbXOviwx5/UhB8Tpp1AKRYj3oFBWJappjaoxKI/nePU//4yDf/gd\ndNfmlRlllFFup2wD9NuUMZQ/rzj2tsiF20ewPro1OM+WxdHs5EwMIY9QI5/Ay4acF/RTmHVkDUvB\nWuc4b7DxF+A8ItcOpvidwJLziJmYFBMzDXBeWOgyqdBqzqMfIefZeX5uMtY1ch6PnLcW54mHJ2yn\n9q+U8/w7nMkrmLeI8zYdJ69Lbywzgl1m2UP5bdsfl1G3tfO4M4avt5kwflyS3pvEjO71IQ7++U9g\nY6CqAtP37qO+vwt0HcD2HfhUM2YG7l5cBZnU/zoWz5YMhJowkzRICcqSmlnu7wkPH1IqrX8P1gZE\nAWEOePhYFRXocMVTYf2jDBj7l5mh2KBwU+OZbUBSC0kMra1BbK/VeOBigs0XLRaLFk3TQnd22W02\nBocADluAuhbqpAUBKIlQKkJV2NgOZVHYKeGKnJfK9QNRWI+aYJW7ByF/MRjiErFoZVDGJNrpvnkD\noFSpITUnXeW7zq+246GHOe5nF+jUx20wDnhaF9Oi7aynr3X7Wm1jRXRaQzsvn79tpnWF6aSy068L\nsgFmy8JOb3dxNcowjV1lCh0RePx3ILwyEGMfwINfaHW4xSjcNUvMSvF4SJvdnjGsg4StzDCV7Q2Q\nllwTsUEyXbpvZd7BK+noELEvUi+gfV3Ar+5TlAXKukKlFKpGo/n3J3jz+69w+vX36Nq2R9+jzhll\nlOuVLZ65cxTp1rIq3/N+W74tskn7cqsP5IA6xHmTyHnsOI/9+33OJOKnd7gSLs551sBFwegSZ88k\nnGenFYkzBpu6AefxwK1NALFyxjDHIsRMzMSywuI3P/XzuTOcxxnn0SVynrtIfrXQgMi0Ieex4Dy6\nY5zHGecNkNSlcl68KTfjPPlI+zOkTTMejnO/nKWT+AY5b1mydVXWMvS+TBn6kZyXSQPHlp2zTJb/\njDi/jGV1WnZeMhBvkNdKuTOGr1HeJon3MgHQR6c4+dNXIALK3Snwn/4K5e4OrF3fBUrwt72Enx4G\nZM9NAI8B30Syi9KdKbP1q72RLBkDkpll7p/cGOZPDWCUZ8dZYvnN6Rc20XBm3F/4aeYxgKqfdq61\ni1XQaXRdh0Vjg+KfLDqcNvZzMrferqbt0LUa6DQKZlQETAtg6oKoVoVCqRTsAjQE5RW+IrtCkY9G\n6iApwCZF1AsONyAo/xyIfPO9Rk2SBK0c9/n4GBGIHCS6fcwc+kGH4K0GTaftRxvXLx3mnbZT3rVB\nZwwMbHDXUilM6hLTqsSkLjGpCtSlD1ZauKnYFABIKQE4vqoZ8CTw4xKRbCMFuAgdk7wCOHCXpF0l\nygp3ULzHIplQ75am/D4XlZSXoFd6Vqk84GnMJsMwcv5OAYaOGR1oWw9gCHJaxCCnRauhDk8w/+w7\nHPzj79EeHMGwCe0eZZRRbkyWK7XNzj3v/HXzHgeE7URoqmHO21/CeRa/VnIeJYBmOY+8kk9+Ka3B\neT1j0vIrvuLejNDYq6rfZKc4U11G8Hhnl3FcyXl2nlLPjRc4jwTnFWBmZgq8l3IeC86jRdPxEs5j\nwXm0BefxEs5jEJE3Vblm0xqc59o/yHksrzcz0xLO44zz6BI4j34EnMeUP3sC0LwB7pI5jy/AeawO\nT2j+2Xd0BZyXDDN5M64g7+tOf9G2rHP+UN9tct7Wdbwzhq9xha23V7xho338HAf/9z+DWg1Vlpi8\n/w6qnQmgWwSf2cpHNnsePItkY3WiMQ0jjKQMB1mUZDNYxLLjS4UG6uPoRnoXfT1CWYQQ2oJ8mrzw\ngU4J/hj35JDHCo9N7NdFgUc/BqMoGGwMqspAmxL1RGPa1djtIhAsGhu8c95Y41fTaAdQGp3WOOw0\nTGdgms5Ch2HAGLvENQFF4QKGKuepUXIJYrftl2LkGAfCe8V6lyI3GIYuiQDl8/AAGGZysZ2Gr13A\n0855/OKH0bFB5+N+uan6fio8FGE6KbEjFG/pPlVZhG3p6fOxGpRU5t7Lp8T1pBTupNKP7exf+wga\n0RxFyLpEnD54G4f91Ovi3u0XMMJBlAA0WbWE5ZM8Vuu85B0PihAka+iBMVkMUEZFAAAgAElEQVTG\n2kNR4aa9lyUmTDDPDvDmt5/j9I9foXt9CGo6FCtrMMooo1yHXCLnjY/yLZOc89BqFI7zSsF5AY38\nSYM5ISa6AOext8otK0JksZ4Mcx55m4HPfEvO86QW63k+5/ms4tkM5TivrAyMKXlNzuOu03SJnMdX\nzHlsDNPIeZfKeXyNnMeXwHl8+sevaOS8UaTcGcNXfdMVGOUKJI6YDACHJ+gOT7C4v4f5pELxt5+i\n+PhdUGUHOTslx6bfSiRQydE5n0I/WFWKZQ9C2aCfIxZKsnBxjl/GJkyLYSxX8LKYPM2yoTwr20/x\nF22OIWPZux7BzCiZURmNiVui2RgNLaZ/++Cpi6azAVQbjbOmxdmiw6LtoNsOXWvQmc6uZmMMFBsQ\nWUxT4a+bDo845dvHPmD2hjnb736Z6tAV3s2EgUsnFLMNaGq/GhbBWB3gGIYN1mrchy0AacePBnCx\nF+JyySXZqf5VgB47nb0oCrfsNLl4A05RK4sOcpq2J484zR0RhLLLSqDereE9f727z3sUhWeR8mN+\nX0pYAZKI0l8FOQjlk+FlyFROs/QXYaBd8YFMAExQVLh7g8FLwB1R7xMCnZKCKgobzL4oULUaxes3\n0H95jPZ3X8A8fo7qbNFryyijjHIzcomcN6RwL1O2No1cgayqyxDRXLS+Q+XlhJJboJBxHjWO89Tf\nfgo1zHmbgF643onh7JZwnn19cTvOc5SYWRkGTjyH86JKTTkPd4/z0it4eZxHBmDHebyC8+gWcl7A\nsx8J55HjPL5FnLfueDU4sgAbVy5Pvyzfdc/3In9OXZduu0h5G+viO2P42ruEPOSgMKCdR7kloj77\nBos3x6g6jYII5U/fBdWFXQJbXrAe4LiDg1Ahv7OAIDfIhinmTqcSpfkjPb23YwWPhA3pBex5/zJo\nCfXw5WUFyH09LcRZv8j65Jya90tsIDGjMIWNGcEGrAuwMai1waQ2mHmvWRchqemcd9CtZNg0Goum\nxcJ5DRetdktg26njnbZBV7UxMG6Kvu0JoVwdnEQO5lhVJ2FVHtk9vr1Sk/t+lSAilq1WzjtZFISq\nKIMHLy4xrQK8ELmp1uRjXEQPH3kIcmUS4FYvt6NQ7tX0HkE/SHmMibgn0vvb3CEIAfE1UdH+GAjW\nX2nqXXWZZd/TmHoSpdDQPhL9PqTSe89ITncrtJb0mg6c3Td6uU+hUNYFaiZUByfQ/+3fYP7ta1Tf\nPENxGpezHvXAKKPcvNwRzhuHi4sLq8++Ic95JRGKi3NetmPkvPM4j1w9lClAd4fz+pgxct7IeXeH\n85aNOKNck9wZw9fkEvLI77Rb8hCMkmPq8SnQdWh/9xcoo8HtL1F+8ABqbwqCsW6bqBUypS9gQgKO\nlOhaSMsdTiSAZck5A0Wsbm4GW7INkQJyqDt/uKQskTdiDXkW05PiNnkciXUgt7IkKxv0VBUGhTGo\n2MBoDivd2I+2wUFbjbbTEZQcDC3aLuxvO+3SdSG+gnFBWX3xgHs1UXoogRCrIsSuQLoMdbzEIn5C\n9I5Z/eqXh1ZuRRhFKHy8iiIuLx3/klthx/cWRXChCDkDUTrT202cF7o91E3udoTgZgXmcRHkdc2w\nNpQX/1JyK8S+SesU/HF9VolbElw41jO5jcUjI8sYvn9p+JCvT+659P2XeAAR4zwQoSgKFGWJGoTq\nrEXx+CX48ycwf/wa+O4HlMdngFt9adQDo4xyO2RysR8E6TDrZED7J+mXSK7wR7mwJOxEjvPIc17d\n/hLFBw9Q7E2BYc6jaCNaynkRgNbjvKh5zue88yJhpydR0Lkep8Sykj7lepyX2H8uh/MoZMSWLTzn\n4XI5jxznccZ5dAWcRyDwGpxHl8R5GYBdGedF+574cwOc57P376kMcR7BRbPry8ac5ybLEQ9wHt8w\n510FNl4Hiq6r/25Khkyp66ZdW+6M4evOVHSUy5F5g/a3f4F58QbcGeDvfon6Nz+zg6MP0s7LPHBD\nzCq9ahBaY4k1iYH+tPVs+MzZiPpJYn7LnlGK59CStMHFIo1xoqIrH/+sDzYR8mW7PIwCkQEpO5Wd\njQErA8MKVZg6HlcP0g6WtIiloLV2xyw46U67fTqeZ0yYpu4dtN4baC87w8AFbHUxGXpd4eEEFF5R\njJ4u5y0qMq8dxSCkiSL2XehoRS6MPXSnSZgQZsTBRIJHotMyqv0kHyRp+/esrxetKCcBJNEuEvUc\ngqHINOgfHNjFoF4+y9LauucHKPkbrolsi/MOWq+fQkH+lQOFsrRLWU/mLSZvztD9jz+h+5d/h3l9\nBFo0KEdr1yij3Dq5Yc67gt9Hb6Ws6qfN+jDjvMnf/RLFCs5LVcjlc57XXRfhPPvL31v3pNUjq8YW\nnCfzzk7OGrWBrOA8Ymv8cpxHt4TzYh+s5jwaOW/kvDssoz66IhntSaPcIkmHR+40zMsDLP6/P4MX\nDbjtUH74EOXDfRt0KaxYmJ3usyBEaArbQjkFOHKJE0Xijvt8SJwwNIrL9D05x+gl58p4N4qvp4Sj\n3r6EokRFskNDHbR0OJX5pRrM8oAKi2yGWAZsQGAQMRQDihgFEbRiGKOgC4PSwY4xRVha2zj40UaH\nYPNaBJ2Xq05KGAoeQEQvIFh0S7jE1Ff+vntBgIqGME8Kcoq1J6u4whS57g/osax3l3ZzfzZYwiX9\nNDIx5WXFmvbBa5hqloJPACefxwrHkE+bc7sExOyU82yzNLQdqy3ypBR23TVT8MtaFyiqEjUzJkdn\nKL74Hvov30F//gTm4AjctP0bZZRRRnlbZZMfDnf1R8ZNDGR5mRv0nYcea2lxnMcZ51H5cJ+um/P4\nYpzHefJorkoquzXnxflOV8d5EJxHALlX95jYsOA8umHOC7ghOC+ZaUR+RYE+5/ESznN4NMh5K6kh\nN0b2Z4O9FZzn+zsadvuct3wcuBzOI8d5/BZw3tAIs02at0Euq51rnT8avka5hWLhgACY4zOYP34J\nPlvYgew//xJqdwYqCETKxoNI3HsIw3NYtnCpCADpF58kCyNzfqyXPtNyyRfu75IZ+Nnncv7wsjrl\nwMLZRgKHWfm9oYEHN3s7fR2ILA2BAGVABiDFUGxnfykwNBkQEVgxCkMWdAqCMcrCjfPgG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hOw2zaDDXBmdEmL46xPTpS8w+eITZu++g2qmsEcwIb1X8vW+VqHF/iYMDL+gKpUBkHBQB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\noYVZTQSjFDQIHQE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vuTsbsze2jE1DIQ1oWA3SGBNjTniMjzHANHm/awSjqCyia7z0wDDt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\nLX163YQO33Igyi2DRjXEE2zTSD8eTPI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8DFdsTHem9sa6fiE2XWQYlbNhIA2PKV02qOcj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\nAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy import signal\n",
"\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"kernel = np.outer(signal.gaussian(3, 2), signal.gaussian(3, 2))\n",
"blurred = signal.fftconvolve(img[:,:,0], kernel)\n",
"\n",
"# image in frequency domain\n",
"fft_original = np.fft.rfft2(img)\n",
"\n",
"# reverse from frequency to spatial\n",
"i_fft_original = np.fft.irfft2(fft_original)\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2, figsize=(15,15))\n",
"\n",
"#plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(kernel);\n",
"plt1.axis('off');plt1.set_title('Original');plt1.imshow(img, cmap='gray');\n",
"\n",
"\n",
"plt2.axis('off');plt2.set_title('Reversed');plt2.imshow(i_fft_original);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Discarding coefficients from frequency domain"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oFX6p9led66oUefYHBSf7j/fpOj+XfF/v6mckcP2fkzcBCfPtYTweX/cpiB3G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\n7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wNkWNW9npR48TL6GOV9/V+xoY9HP4xIcZ7OJxA4jvOcsPTvwUmfbFxn91wy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\nStUvRV7Z0Rio4YTDtD9zF0fm6sG/vRcO5AxfUXiuXO+Zzufc/M7dQ+bl7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2Pz+bx6EYca\n13QsPB7htUFJjXQal3cs+QNwV6IgrmhF90m5TSuluwbTJQaSknTKxzzjFK9RVl55MwrxuBxPgfGw\nR0qJKVFkwEfZQwsbavDygvKF6xgi45d6eaXkZlQ/j3em6h+1vSRNqq+iOeLxXhgE+ZoP/q1GQfYu\n5rKh7CI+ZI8np5GO8ZxnAOO5FMlvbl8KX3nYpTTtfVBdjOFhVLOb7S7JMyWHMfZYQ9AJ4CTAeoCn\ni2EOYBMT88L7VmNYHUxdgrWXodumY2wazUldVxqHNyPm8/nCnV98bQR7WXHe+I6eM2l8r/38hm4Q\ne4vioxhdy+C2R5vrEe/16n4XYXXj3RVufTSGrz8SMQgCk3oI93t5C8jbPQBTgIEHn/F4bIPBwHq9\nXuXlxbtbkSKokz1nhEkZFVKgJYoXgSC9o8K7rwhgBoJsNBpZt9u1s7Mzu7i4sG63WxkDYcDgfmVm\nq/croZzBYGDD4dBOT09tf3/fnj17Zt9++62tra3Zzs5Olc5TxJVKjF5eHqlnKWGqxq8cpQSEF5fL\nSdXfA3/6PGqDJ+RK66fGDBbaLKRms5kNBgP78OGD/fOf/7QPHz7YyclJFQc7v1w37VsYzQCc1tfX\nF4xfOzs7tru7WxlNcaH75ubmwl0HavgyW7w83jOKsTFIj0Frv0bzIAKd3H96Txr3DwNErb/yCR0L\nrj/A6OHhoe3t7dnOzo41Gg07OzurPDe4jvr2M2078o/6RQ1z3ryOFC/O+77lx4pWlCOVy3XTLFPO\nbcMifOBhB/7PXj26TlXOR+WpArOM4uKlQd563I6PN5rZwlsa1eCF39gwyCl9+gyUC0u1WdPl5koq\nboQJzT4ZNhXvAedOp1PXqwttUJmpm2Pz+XzhZAC8UTqdTnXHKxsjWd6obOa5FmGQiHQsPhdFinsK\nA9QxxkR5RrpGaV3qGG6itT2bzap7j4E1MGei+rJH+Ww2s/F4vBAGPRJYzvMKY49Tr32pfuHnJRgu\n6ueSsU7lExnwuG5I560/4Ec2RG9tbVUYUq+N4Dy0nry2Suab6jZeH2rb2JjONgLOI+JVKf6mdJfy\nctm4d8lzVoavB0yNRmPBcl+Hqd4VeUA0BcjYEq2eXv1+vzLSsOu8gsKSsrRu3v8ofSk41WcKNPEf\nzAht54vocX8Z2j0cDivvG36zES4xhYGBmXFkqMEH4VdXVzYYDCow2u127eXLl/b06VM7ODiwdrvt\n9odnBIiMUykjFv+PGH2J8UwBBAuQlMDl+FEZuTSpOFF4CfD28mFwy3UAgMZ3v9+309NTe//+vR0f\nH9vJyYkNh0O3nV7/8i4w/jNAgQF6MplYt9u1o6Mj29raslarZZ1Ox3Z3d6ujkewhxkDbO8KHeuiH\n561Xz6j/c+Ovc46NVUifOu6RMjxxXggfDofVywQ2NjZsZ2enWtu6+6ZAi8sGqTea1xeqqJTwK8+L\nZEUreogU3UPn8brbAu46PDtSXkowkacIRcZp9cDQMrhOKQ/50jbqb+Yb6ukFhZnv8+K7W9nYxdgG\nnhFR2V75LD9UIYvmQm58SvrHw4JeXupxwX3AXlxs5PLGW+ubU3pRJ8aN0+m0kst8ZYH2jec1gny1\nvyPMxX2fkscq33Ny3Wtn1H6PvDB+FoXn6ubVUY0KHuby4qewMTY3cZ8v9AbMKc/AnfrWNsCYc319\nbZeXlwub6WwQw299wUapEcxrqxeuVBeTRPzEW1ucxkunRxbNPmFRGP3n83llWOK1xMeRU+0q4Tmp\nNYW1ytdpAHfzJ/L4Uj7GeJTjM9+L8vHSeO2K2hj1gxe/LpWmezSGL95F/70TBo8Z0n1cbJ8CRiAF\nYnpn1WQysdFoVHmb4K6qSGHj315ZEbjk3ynw4JWjYam6MHgB+EE87PDBe6vb7VZeXefn59br9ard\nGzNbcEVmz76UoYvJM8JB8R4MBnZycmKj0chms1nFqJvN5g1DiBrYUt8lhq8cQIlIGb9nzEBbo7I9\nwVECbFKgJcdMG41GqLCVpPMEIYDQeDy209NTe/v2rb1+/drOz89tNBq5Y8HtULCB/7yrx0rYZDK5\ncaxlbW3N2u32wt1g+/v7tr+/bzs7O9bpdKzVai2Ao9S9cVou4jP4S80Pr50M0PGf24z24ZvBixq7\nFKjzM29ujcdjm06ntre3Z61Wy7a3t20+n9toNKoMTmqsYrd1BTzo84h/qTKivMnjVciD+csyc3VF\nK/oSFMm+SHmKZEGOIgVD+Qc/i+qqdYgUAuXZ/BxGL72zSeNGmMkLS9XdC2O+4Xl6AYvynV78wiLv\niCN4kHcVRlQn5YtRe6N0pc+jforwZ+TVxRud8OyCkQH9F8nAqC0sE9EXLM/42CO/KfPq6sparZaZ\nxTpTpEeojPPkTRQW1b8ulaQrwWS5PCOMWacePHei+BFO077ExuNgMKju9B2PxzeOSCN+CSbVcvlk\nDesxa2tr1drm47Ose6rnIJeR07m8vo7il84bxWvaL2hXaRrFevD8gnEZ/QX8iDjcl1FbS+dqbj6h\nLMasfKSa6+AdX/XWstcnSqn1rfwgalcUP9LvcvPAk/t1+M6jMXwdHh7edxW+KM3n8+pV0e122wVD\nX7IuqkipSz6/wQff8PKCpxeMPjnw5t1vEE1wDyTxtxdX25Jqp7erCo8qGPZg5Do6OrKLi4sFMMgX\nmppZJVRQ59Kz9aqQm1m188oAi9swHo/t3bt31d1fr169sm+++aY6moU0rKR75SrT0jrl6l9n3mpZ\nHvjVvD3myWE5hlyibOEZl8Fj4R0b4Hy0jrrzy4AEc+b09NR+/fVXe/PmjR0dHVVGTBbQXp9FfalH\nHphYmG9ubi70DbwUP3z4sACI2u22HRwcVJ9Op1PtOuN10DzHuexU/XOCmMdC2+qF8/FHNgLzmDUa\njdAzwZsL3AbwNRw5efLkSXUnhyqyyl+4rRzm8TsFPtxOVdb4TkEA2s3Nzeo+sjqKw4pW9KUISnsd\nSs3luvNccYf3nMNyyg3LZ37GxhOsbz5y5HlaeIYH8AWPH0Zy0WzRs075DxS9RqOxcFk2vIgiLyf+\njQ3BlJGF28XPPbzHYbn8NM9Uv3hplKcCz+LIIo4vop28sQL+zJdhR/VOyRqW2Z5yzjINRhP2QIMc\n1jkSze9U3UqwRhQvVw7amKNI/npKdSodPyvFT6k6Re3Ueax4hOU5jKa9Xq/CWmx05vSMN0FqMPHm\nFfcV407uv6urq+rOYZQBT08cbwYfgAG8ZH1rPVL9uUwYh6uOF/UH0qgTgPIivpoC2Bz5sWFZr71B\n+hROTfEo/e99WNdjHZBfara5uVltQHB9lB+kZJ32XSQf+TsKK+XTpeHLxn80hq9Op3PfVfjixPcp\n3Nf9LJHA5MWnLu8AB71erzJ84WJGZTLRf7WgM9NOCW6e+N6dWxymz7hsVSAZqGJnBp/z83M7Ozuz\no6Mj63a7C4qtvnoaHl0RAMsBVq9PvHECYGMvu8vLS1tfX6+OPsL7i8vMGSFSwj3F6L12aZkeANax\ny/WHVy6I84gAU0rAeulZcHjtUWGj/aECDCB2NBrZ+/fv7c2bN/bLL79Yr9dbMFypMSmqfw6gajzv\njgDsYM9ms4V+2Nrasv39fXvy5MnCfVc4Eqm7hqi37phpO7xjeR6IjNqmAA8f7+4vBjcAB9wH0Rrl\nb1xqD5C4s7NTeeyBhyhYiUCxF87khXlrXwER6sb9sKIVPTTiuwSVUqA6l6YkLzz35EhKGYjKUXzD\nzzzPTjZ6Res/Ik/x82R6qh18vNHsE8/A8blGo7HwwqLU8b5I1kf9FsnjFBbyxkr7Ooqfwp34eG9f\n5LeQY7NK5ZrZzZc9RXXi5xEOU0yM5+DlfCeRHqk0s0oGc/pooy7qa61TRLk5W4JPSstI4ZhcupLn\ny+bn1c3TnbCBjhMx/X6/epGVtx4ijI6wOn3vrZXIG2x9fb3SQ7Fxzy9UYCO9frRuis1KcEgOk3s6\nhuIoxZOcd6Rn6HO+h5rv+wJfVJ6Swm/8P9VWHSMN99Z7tKmr+XryiflNJG8j+aj1rRPmUS6sDn9V\nejSGrz8q1QE/n4N0EaiA1zfU8Jvn4OWFnTFdxGohZ4t6pBiaxWC0BNRwWSmFEb/RJrxl8eTkxM7O\nzqq36fX7/cowYGaVNxco8nbBf48Z6C4Et81jjLorqH1wdXVVXWo+Ho/thx9+qN72yCCbhQXXl8st\neRYxpRRIAXhUgaXxU4pIXcEfxYnKiNJHbcZcygkzdleezWZ2cXFhb9++tZ9++snevn1r0+n0hrdW\nJNBT6ybaPcz129raWmXE0rS4fPX9+/fVnWA4FnlwcFBdks+X77LhC/XVHUx9hrjanzxvo3HR9pvZ\nArBDWZw30qphGPXTvv348aP1+/3qTrROp2ONRsMGg0HlpRfdLxYBgmgd8LzyQAnnycDIy2tFK3pI\nVAJceVMsF98D3XXK0/SekhDl4ykWHtZAXswjorz5mXfERstQJVON3uCpMNRDgdN7f8zsxuYmH28E\nDkx50kf9w3Xx6u8pUFF+Xn9FY6h4D32AzU14v6CtqmCWnsIoUe4ivMeksg7PGDdNp9MbhrB2u11h\nPS4L8k3vtlRcF2GgqD2evIrSpcIeMqXqnMKQGCtscOJaFHUOYIo2qlJXFkQ8o6Rd3pzAGhgMBtVm\nvnc0Uo1gyJPz12f4780Fb857dY76gHlICit76bh8xlA48oiNRBjA5vP5gtFfy4yOQ6awq7ZBn/Fa\nZ8O3JxtTcshre0623SYs+p+LXydtilaGrwdMqhx+KYpAHteHwRLcwHGZO+61mkwmFWCIFpgu5pJ6\ncXoN9xY0gxsFnfybDRDT6bS6kB7n7mHQ4zP4uK+s0fh0VEEZmMdsPWaXokhgcLi2m59dXl5ar9er\njj5tbW3Zixcv7OnTpzfu/dIyvCNyCo68+tRpp5cvKAeC75JSoMWjHBjnu5w4PsrheTeZTOz8/Ly6\nz+v4+NhGo9GNeuF/dHRR+y41RqnxSaXFmmFvSFzMOhgM7OLiwk5PT6s3RcITDN5g7EXgeYFFRiIF\nSCXAOYqDNkRzKqdYK39EH+A+rXa7vXDcWUEY0rNRKjffIiOcx9f0CM59Hpdf0YpKKJr/JSC6Tp7L\nyI1ItqbKiOSCt0uPfD3ZF+WbCsN/xQbKP1l5Au+FtxeMJXrcT729+E4rtCOqf9Sf3vNUv3rPNU2E\nB7nd7LGmhj2+r4vHivl41B4PG3hGoVQ7PYrkNtoEzK1lw0sHmzyapyrkHJarT1RvldN1ce/nIJ7/\nqTansDZ/e2EoR/Obz2/e5zUYDGw8HrvOAZqP/i+dNyV8NTVHGUfo/Xb8pkgYhPj+Ym+t6DOv7Gge\nptoQ1T1ar55OE/EP/g/y7rfVcdR0Hk+O6sRh3iYHjwlfcJ9qgz6rw2M5/C7CSuTwMrI6RyvD1wMm\nNjTdN6miDTddfm1zv9+38/PzipnzhXvewuPn/KreSNB6AC56DkYNYqDJTBy/+U4cvBHx7du31Vv0\nut2u9fv9hbsv4AnjjZMahlRhT1EkgEqNR1F+s9nMer1e5UU0n8+t3W4vnAv38uS2qEEiJUxSBhnv\nmSegFKynQFiqf0uZZx3DF5eX2n3D2OvRW3xjLg6HQ3v79q29efPGXr9+vXCfgLYrOurorZncmuJv\nVYq8duA36qD3IAwGA+v1epUQxr1Xh4eH9uzZM/vqq6+qt4zyfVuYg7yeUXf0BbtxewA2NV4eqGL+\npB5xOaCOOjBfnM1m1u12qzdhtlotu76+ro7GcP21DB0zLsdrQwrQqKF/dbxxRY+Rcjz4c5UJimRB\nifLp4RQOZ2O42c0j3inFV9dzKr7WR5+ztwD4OW/iYYOT77fS+7wUv0WKYtTP/D+nHEVKZeo5H/VB\n/0Fxhzf/aDSqjF2sZKbuMorkg8pRbluJrIooJeu5bfxWaE3PmAV103sovTZpuSmDVyr8NvrMsmnr\nGKmWLT+S24yRsDmG+4+73W61nrw6RbI/qvOy/DLHz1A2XwsBAx63ER5g/OZv6Eqan4dhtb2efpCj\nlAErwtNKKTwPfnh9fb3AJ8En0DfcTq5H1BYN8/gH+DGHsf7KciHVxohnanleWCrP0rBovpXwxmh+\n1pn3K8PXA6XIePA5ScGKN5GwyPiOBxi94LKLyxk9MKbKpjKkXL34m+vEeXug07uElI1dcDG+vLy0\nd+/e2fv37+3Dhw82HA6rXU4zu3FXUerCcKWcEUjbG6XlZx7zVMbIyu/19bWNRiM7PT21N2/e2NXV\nlX3//fdVO/iOFZ2D0XxUoeaNZ0rgpAx8nvGhVABqnaIyIkFRamSbz+cLxgyOE+2C43s+/+047enp\nqf3yyy/2008/2fHxcegyDfIM4tovJX2ta4rLKTGKRQKa48DgCoPyycmJ/fnPf7YnT54s7A4ySGIv\nMHw8w5T2jdYHzyIAyUoAp+ejjwxEIgOY5stAttlsVkCJeVXULgU+XlncrpI1rx5ny4DjFa3oc1PO\nyHGbvKKwSNlMGXA8Xh6V47VJn7HXgFfH3O9c+VEdgH/0Xi9W1D9+/FgZvNQTir2hvH6sM365+Ckl\nLWojK4QgtAlvwMYdqDgmaBZjmhJjiafc6lwqaVsUFmEX1Bn8HhhCy+Y3MeOZh8O9cnVtRM9KKIqX\nwy13ka4UJ5VgKDzz8COPix5vHAwG1UsJNF/lQ/rcw8T8zVR3TUXPGSt5dUP7YCRvNpvVC3/4mgtu\nh4ertL0l471MGzl/nbsl65U3pr0rbTxslqpXjldqXlqGx+s8PJyrx10/LwlXXlISv275TCvD1wMm\nMM26Sn5d0snm3QOBMM/oBUbe7XZtOBzecHvX/L3/qQXqLQbE12M9+ttTAMEk+KJWXNw9Ho/t9evX\n9vr1a7u4uLD5fF69HYffksP9khOiJWCgZIxL54GCZ2aYOFIHsDsej6vjZ/o6dW0XC3EvzCOMl5dO\nxzJqrzfmUR4RQ/TGiAWHlqcC0cszUpBA19fXCwYxPmLAisdkMrEPHz7Ymzdv7J///KeNx2PXmOMZ\neqI2pojbGbUPY52L4z1DWna5xoX9s9nMtre3q7vwOp1OdT/E1dVVBZB0HjJg0n7Q+Y5yvT7RtnA4\nHznktjAo4jf56Hxhgx34487OTtUmKCJcTmQw1ctJEQdzStvjKX/q0VoHGKxoRfdBdeZoSdxIeSzJ\npxR467rLlaP4RI/KeLw9wk2cb1TfaPMFR5agLMHghc2H+Xye9PQChlAcxJiDn2s/eGMS8WavD/Cc\n+5MxAh/hZEwLr/7hcFjdUYmNT76wmtvAdYr6O5pjOcWzhLTPvDxZFjLG1bGADOM8EM7yLddGhNfR\nT0px8G3z9OR+SRklYd5z3bzCc3wwFrgKAg4CvEGG/FNeO9E64DBOl+MNHnn5R3OY283HbNfX1ys9\nAy+m4/sCtX84P51TJTiO43r1jsI5/5IyFF/xhoFu1Ho8OlWPknD2ykU8PeoYefdHfDfSf7w0ufmU\nm7dRWJRnHdlcZ46vDF8PlCIF977qYmYL57rxu9vt3ti94ImcAmuRcorwnNFBQQ8/U4MYv+oZBi/e\n6VxbW7Ner2cnJyd2cnJiw+FwwftE+yL6rtunpYJpGSDAfas7mHyn1OvXr63RaNgPP/ywcC4feXne\nN1yOggpv3Lz6e/3qkdZdz7BH5SBOdEwhKpvrr6CGn/PYeS8YQD31PhcWVr1ez96/f2//+Mc/7O3b\nt/bx48cbRkLPEFOHdK7VAZ8psMeUA8B6j9f5+bm1223b2dkxM7PpdFopJHx5KhvBuE56CTTPiVQ9\nvLXBbWC+o3Mf46ZrQHkaj/V0Oq3u+1pbW7PBYOACF+1r3SVMeYKoAqP8T5+taEW/B4rwgUcpPKHx\n6j6vC8495dXj7SmlScNKFQHmUewdAIMPZD8fZWKjF/BTdI9Nrmz+jhQwhKm8VGIex89g8FJ8h/L4\njiJ44oLXp/BLzrD1JUjlltYV9VxfX6/G6OPHj9Xdk4xn+di9ZxiLqNSIFKUrxRTL5IGwZcrIpfPm\nRTRnOK/5fF4ZWnENDIxCmndKL/L4RmrNLSPrU2m8spSvclvgJAE8B2Jex+vOK4PzU54QzQXFRHXb\nmSIdZ88wzviU+WRk8PHK4HAPs3q6NPP0iMd6dY36NIUzc5TSB3K6ghef65yLU0Irw9cDIlbUVRB/\n7jLxOzJY8J0I+EwmE+t2u9VF9noJqLc4+XfEqD0wp89YkdMPMwB8s7ELO5YM+hqNhnW7XXv//n11\nCTxf+q594gl/Jc+gkopTapjIMVIWFPofoBbCuN/v2y+//GIbGxsLR8/0CKc3Hz3BE4GAXP+UGCu8\ntt6WcoJRAUxO+OeACc/fy8tLOzs7s7dv39q7d+/s/Pz8xvFZUNTnOuYp8uZjSgiljERMkVDkZwy2\nzcwuLi6s0+nYy5cvFy5Q5p2rq6urCjRtbGxU5Xi7glwmvxk2ql80Pxl0enMZHlfR3NU5AU+Cdrtt\n8/lvBjrwrpxB1uvjFLhlD0PmhSvD14oeO92FEpdSEkvLq5vO+68yQmWrx4+isuq2kfERsBFkDgxf\nrLTi7YZ6nxcf29byIqXYq58nN1UZQ50RhueedwN78kPp1CNWfD+t3sUT4bESHB5h55K5q22uE+7h\nLTZoAWugDcB3fKcm8mX5x2FeffT3bYxKuby98FR+dfGkjrH2pxcWeWgiDYyPMCCPRiMbDofVeuIN\nr2h8U9iSaRk+UEJRPOgRXjzUFwZmNbpynzHWTZ0w8OZ4FJfjlLQjCk/NLx4r9eBHu3TsSkn1O8W6\nOhdyOK/EfpDj1d7/0naUhEW883PRyvD1QCnFVD8H8WJixg4Gx2/0mc1mC+fU9U4vb/F4+XOcqJ2a\nDmn1aKN6OXgeXnzPDgDRxsZGBYSOjo7sl19+sevra2u1Wgv1B5UAII+8dHWZk1efVHn8rf0OoYX7\npTY3N+3w8NDW19erOyC8+kfG2JwxTOuSS1NKOZC4TF4p4BcZSbhcBTDeuGMNvXv3zn7++WebTqeh\n8pMDcZ6gLGlrVL8U5QxcqfLMPh337PV61m63bTAY2NbWlm1vby94EsBIjTW6tbVl19fXlQFsPv90\ntFDvr1LvKG6fd98CK1UeoPP6l41reM6AQz0ncLF9u92uvCcY8HL9vTFRgxu3CzzPu5eG+aMHHFe0\nohXdHZWsKzUweLI1l09OmYqUXb7qgTf+4OltZgtGLz7eyAYvzwPLa5sqVB4vTeFCT8nDx7u3le8d\nM7MF72F+eRFwbOrYucrhnHKm46ntuAtKyQruW/Q5jC9s/GLDV7PZXMB6On5e+1JUNzyF/aL25cJS\n5XrjmcL0iic4Pm9Sal6MJfgFCrgDC33pnRTw+p3DSvFdybPS8ChM5zZjIcy5tbW16q2PuOKCsRGO\nRqZ0pGguRnNyGWy7bBpd9zpn7mLTMbUePR3Zq1NUd6+M+8KGOR5zV/VaGb4eKOUm7V0TK0laHruF\ng4njjSQweikQ0oWOvFVoKyDi+JqOF2p0nEcvrFcvErNPwgwKNY44np+f22AwqI5YecpiziARheeM\nFymGk0tb8t8TmOjPy8tLu7i4sJ9//tmazaYdHBxUworrp0ceozbknucAxzLCKpW+FCikBCnyVSAf\ngZRo3mC+DgYD+/Dhgx0dHdnZ2ZnNZrMbXnZRe7Q+qXqmKOr7qL9ydfIAkIax0jIej+3k5MTa7XY1\n51i5Ql9BQfn48WPlkRjdA8aX0rMCwGDEG2uvbSjfLH1cVo9dIi27u8PlH295vLy8TAJaD6B7hivU\n0buomD/MQ1e0osdCyyp5y8StG5Z6llMUdS1HctVbs4qrUnVQA1PJEUfvXi/ly57807aprExhPE2X\neq4ea1yv6O2Ua2trlVEM9y9ikyVlqFLZnZL7y1BKfnvPNW2qHurlBjk0Ho8X7nJjoyfi45O6jqIU\nu6ke48la7mN9lsI/0e9U3TzZn8LNUX2jNNzveDEEPCcx76J26Zh6/ILDtOxlZXwujTfXcvMU8w76\n4+bmpnuVh+Isr2+96yC07GVxv0elGJrrB+K1FHl+5eoa4UGPt6oOzHo2p9U6eOXchm4rWz058bnw\n6srw9UBJhcWXIF4U/Npa7P7hrgBcZN/v92+8RYLz0Un78ePHCpCYLe5MRQAvOtLoeXixcFdXdgA8\nlMueJKPRyH7++Wfr9/s3ygeVjEMk3EuZq5cmEv4M2EqYqMfg0H9ra2s2mUzszZs31ul07Ouvv7at\nra3qUkrOg71OSgCKJ+Q4z7rtzT3zyLt4NPqvBpNIuEJJiBQVrAltF8DAxcWFvXnzxk5OTmw0Gi3U\nVcG2Rwp0FDjm+kXzT4HRFOUAugpnxN/a2rKrqyv79ddfbW9vz7755puF4xcwdLHn5nQ6rXYNcSG+\nmf+GS8xV/GZQqmPLvA7pEA7+xsqBgjT0ud6XwoJ8MplYs9m03d3dCgwjf+9oqwIcrjO3jQGhpzB6\noCjiByta0YpuR7oOS+J7R4CitLq2tVx9rrKB+QCXC94F/ATDFzYPmX9HSriWn5MfGp/z9cpinsx3\neDG/RX/yEXluG9rD3sWRp6ynaOfwWorqKOY5o0LqOfc/bwZBpk4mkwWj4ObmZnjMM1Vmqu76X/OO\nsHIKU5b8juqby9dre4QrPHzl4QsYWfH2UOhQEc7MravPSan5plikND+zTziOsRsbXc0+3YPF6zF1\n7Fgpwui5dB55PDXVPqRh3Ge2iOO9I5yRPuPlr2GaVnVgnjMlWD7SJb4kRszpZndNK8PXAyJVouoK\nnDoUKUmoR6PRqBg3dismk4kNBoPwInvOW5/P53P3bTJRHcwWLy9V4xfvXHoX1oMJYSdTvZU2Njbs\n+vraRqORnZ+f2/v376uLqLluEePIGQo8plMynjwH8PXgjQAAIABJREFUonI9UJYT/vjPHjIseHEP\nwenpqb1+/drW19dtZ2fH9ahBfhF40TbXFUSpMAXyJRTFj+asx/i9snSXlEk9jxBnOp3a+fm5/frr\nr/bPf/6zuuw8B6i0HBWSKqTq9lEK1JSADTNbMBjl8mk0frv/6uzszM7OzqzX69n29ra1Wq1qfeOY\nxsbGxsL9gewBpgoO34/ISk0KDEAJRL0jQz4ficRzNpZpvlwW+BWOm7Tb7RsX3HoKmM4fxPXWkfJd\nVlZZ4eU1/aWAzYpW9Eeg3HrycI7H/1Nyp265ipeYZ/EbDFlRZ6OX53GPeqfalKqXYj5tLx+nxDP2\n5ocBgctSfMd3QwIfTiaT6s1zKo9KFE8PyyzDQyMjQ0lYzkihcXnTF32Bkxs4gsZef1rPCOelcG1d\nXJiKmytj2bjeM25vVEcP0yoGQB+PRiP3iCPL39Qa4e+7oGiOR+vXS6/zIZqLrFvAmM7GVuhajL28\nNVCC+b11WTfMi+O1L2o7Yy6d56U6EK/viAdFG+56jF3ncZ0+/L3TyvD1wMibrJ9rQqpCh/JBs9ms\n2q0Yj8cLbyWZTqc3vAeiBcWvfOVylaFxXdTIpcYvPdIILy+cKWcXbggl/uB1u71ez05PT+34+Nia\nzaZtbW0t7MqgT+oIXu7fFDjg556yrHlFabw8PcVWL5LEPUW8o3txcWH/+Mc/bH9/3168eHHDq4Y/\nqUu+tZ45Jbu0zZHQSIFFL46OTXRnCeKmgCYDFG88OY/xeGzHx8f2/v17e/funTUajYXLd6O26DOv\nH3LzojRPbpuSGpKYvHGOeMva2prNZrPK8Hx2dlatPzZcY83DoI03i81mswWvzfn80yXGavTiOjG4\n57jqNaBAgb3VeFeX28l5a1wAkslkUr3lEXyL+wbrKmfk4jHjNngKpfLPKK8Vrei+KacE3jYfDsvJ\n7LssS5/hw4b6FNZTOcL8VzGUVy4rRShXDV8wDOGIY8ropXWL+LxXT34ebTLoSQJsdID/w1MWBhvF\nJ7wZwnd7wQChXrtcfkqOepiCeb2GRX2iz1P9q2lKCePqHXkEtocXjr7USOukfeJh41IsktJrStJF\nuKNkvDQNx/Ewv6aP5guvYcw33Os1HA4rRwHk7W0SlqyPyHiR+18aliKtK+sRCPcwP3gPDF7w2Gdj\nK+uD3prgPovW0m3bxG1TTO/hMfznfHjcdDNU4/IVFV6donbqb+5j74UfqquV6DIl/ZQKKxmX3Nz/\nnPRoDF8pxfT3RjkQdFfEE0yP+bC7OwADdi7YMOQBMjOrBICeN/YAmdaHFzP/5h1Lvq+B3fFh7MK3\neoHge3193abTqf3888+V0o03GjGVCNOcwaHOOHqM1TPMKMjw6uQBEe8/32c2Go3s+PjYjo6O7Pnz\n59Vl92bm9qXX/qjcCLh49YoUkJJ1EeXvCTA2gtRlttxeT/BxGABnv9+v5hz6UYVSVE8NS9UpEpRM\nEYhI9a+nbGkary0eQFhbW7NWq2XT6dTev39v+/v7dnh4WPWL2eIdCVjPAJa4j4a9vxjAs3Kn44Q6\nKMCJ+kP5ozcOMCR7/Y7nuOOC7ylTsOT1N+ej+TLIUh6q/NTzol3Rih4KLQui66SJlJpc+ZHyE6Xx\nAHwE6j25dleKSG69QzbpRfZ6bIbz5j5I8RGVi1E6fabHs9XTS3ko+g7/4cUE2cvXX7BsZrmUMt55\nXhYp+tzKW4QDIoMKb/KgHyeTSWWEgPEruusLfZ6qj/c7Fa9OHM9Q5YXVpVRZUTke9mEMxy+HUA9D\nxsARj0Dens6r68Vb61Ga0uclaSJM6ukuIH6bKvCaN6ciPqjr1sNXJZTCuTkMXLKmvbHjsfcMa17+\nqb5lnqQYj78jwxzn4/F3DU+1NfW/NN1dhNWhR2P4ms1m912FL0qfS2jqAtCJv7a2trA7xscc9Zx6\nVFcGMQwYogXMz9XYBQbCHl7YsYLHBL+2OnX0SZnPeDy2t2/fWrfbrTybIlDKv1PGsFRYbkw9hp8z\nquXqwYYds5u7DFwngN/ZbGZHR0f266+/2v7+vu3v7y/kr8Ya/k4Z2CIGn2oHx6/TF6WCm8vz5jQL\nFi8sSs99AeA+nU6t2+1Wc477MmprlHeujVFYanyiZyWKmgfE1TCo+a6trdnW1pZdXl7a8fGxffvt\nt5XxSI2BODqK44/wGGOewIZxvvwe6fn4KdePAZWOBdeBeST3C9qGj64vPFPPKyhv/HIQLidSbryx\n8eaox0vRdzAWfk7lbEUrqkvwHgAtMz9L10wUljJAeTIsUjbx4XXMPIrLi8r0sJV6bnqYDmFqPNLy\nwAf0rd3s7aVtzSlTWt8orubPfIrrzB/gF/X2QnvZIAYsi2Nn/CZHbYfXBpX9OgdSSl9OYeQyvPSp\nZylsFKVlGWT2aR5OJhPb3Ny0VqtVGb9U9kaYKSoXYSwHS3DHlyRdczy2XlgK9/MHm3E4SgpjMmMM\n5OmtVw2LyMOjJTyqBNvl4mtfpfLiNaQGaJ5rzCu1L7RPvGuAIu+pVNu4jto2rX/UvlTenoE9ZeSK\n9AxdfxrXw3iqO3vPvXIjnlbKm+r20TLhpXFS9GgMX3rx+O+VGo2GtVota7fbn1Up4cWiYARAAcau\n4XBY3evFwIHz4Q8rd7zoIxDE9dDjjPjPLtpwycdl+QyCeBfQM3ph12symVi327XT01MbjUbWarVu\nuNdHDEspEopevNRzNVJ5DK8OeXWP7o5D34NOTk7s9evX9urVK3v27NlCnpGS7xlDtHyvXI0TMdQ6\n4Ckn0L3/3m/P8OXVOydMZ7NZNd9OT0+rNyuxq7d37IN/pwBYJKB4/qfcnVNrU+NFxkyv7frfC8Nb\nRXu9ng2HQ+t0Ora5ublgMOO2q+LDl+CDT8D7Cy72AEZ8v5X2N3tfeWsffMgDaGaLRye9fmZF9Orq\nqvJMUA9aGKeisYr6NDVuXH/wVBxXX9GKHgrh6K9ZufGgDpXKhRKFJ/U84p3srcrHoJXvevkzVkqV\nybyEvaWwqcC8DZucMAyVHHFUuaRKvbYjShMpb7rJyXfJNhqNBY8uxR+8KQe5yycX2NjP37pJmxtL\nTR+NR0pWe2Elfenll8MFyFOP3k+nU1tfX7dms1nd9+Ud52PDgsplzt+rV6rOqbBUP92WFN/l4qWe\nM57g+9NGo1G1nhAPaVkWaz14bdSR7VxGTl/hNKk8c3NK66bp8I2+MTObTqcLRlb1MmTe6NXV88xM\nzZeU3qXjz+1QDFgyH7ntfGpB8+fNz8jwpvzI47X8rd6xeM7XAXn3cqf4fB3ZF/H7UipJc1d44NEY\nvqbT6X1X4bMSBhQ7VnelkESgxQNKZotACMcb+XLGlLUYz9izIBLgnFZ3+Vgw8IcXsLq848NGr+he\nqo8fP1ZvpuTdGO4LT/GNGGqpMaIkfcTsU6T9nzM+KWPm8tAX3W7X3r9/b6enp/by5cuFt+ipAcJr\nQ6p8r96l7WOqA6pSQquE4XsGm1JGPZ//ZmQ4Pj62k5MTm0wmdn19bVtbW6GBxBt7L66CDe+YNI+V\nekF68VWAYt3rXQ6RccjrN+0TJqzlwWBg/X7fWq1WZYTyjF9Y9/xhLwUIeDZ6gS+gTshHQQ63x7tT\ngnfRPGAUgQVVGmD4MjObTCbueHpz1lMKlJdrPZTPov7T6fTG8cwVreg+CYDd7MsavjRM+QNTKszM\nFvCEPo+OEEYGF+9ZpHR4Rw4VVzGfQ32A9/j+rFQ9UvVB33h14XpG3l6eBy9jY/bkVa9zDkfb+aVH\n7FmrmEflaK6/uT1Rv3B+pXiBKcIw3jPF8VF+6COMAe7EnUwm1mq1rNVqLbzBWNtTt87LxrnrvKI0\nHr6qm4aNO2xoZQ9D3syOdK9IdjO/8Qwfigm8uvF/LU/1LjVK16EUbgZfgfPCbDarTuZEOg/rKh4m\n9vCbpk2NcUpX4DDPAJbrA483Rvqwl0dKd2ajGM8Z9exvND7dOcdvstV8I3nD315Yqv1R2G1l+l1g\ngkdj+GJA9Hunu9yFV5DmCXqzT4yFX2MNjy/eBUSenBakAEbdWDWOPuOFyyAI4IWNXnyfDxu89H4f\nZfZra2t2eXlpp6endn5+bpubmyGA84QG99dtFEaulxq9PCMLvrlcNRh6OyEpMNRoNG7ca4ZxHg6H\nNp/P7cOHD/by5cvqjXtcD89w4gmflIHqNhSBEfwvBTU5BUP7MvUccxh3UZn9ZmQYj8f2/v17Ozk5\nsUajsfCadc3PqxcrKzzOrNCsra0t7IRzfZAeu+V8ySjWEM8r3bnE5ax8rBh5qgFV24F6e/3GbRwM\nBnZ+fm6Hh4cLxzJ03SGdenrChR6Gr48fP1btxNu9WBHRt8wymNC1z7t2GAO9X0b72wNrHBf139jY\ncI94eevDM3ohvgdYvf8cX8td0YrukyLsUyor6sqUlOJREhatRU+mRHJa5X5disrjMlGWevzoEccc\n9lTsEfH0qP/UEMd9w1hPDXWQaYzxFNfxEUfGkNGFz9xXJXTbMfLKVtKNRJadkTFRy/HyZ7mG6wLM\nbEHON5tNa7fbFT7wFPWUMSWFM6M65upaGidFWt/SuPgftY/XEwwMbESO1hPGkvPnMB1j9b7kOa/6\nlVd39ZBnjM5rjzc6U7g0Mv54OofOR+Y73gkbj1Sn8HgMY+DSNappPZ6Qm6t1SOdSNK+X1Yu0X1Iy\nIUqj9Yjmgco/LSdXx7ug2+TzaAxffxS6KyAUPecw3ZmEFwDf54UjhbpL6TEh3VmM4nvAR88hMxPG\njh0YMivvbPRSby9lggAR0+nUTk5O7Pz8vGKWnou591+fMYP3vlHPZrNZuZPzW3T0rZPaR2r848sh\nQZouYnxRvVG+HvX6+PGjHR0d2fv37+3Zs2e2vb1dpVXPIZ6vmnduHi/LwLx8o3EoLd9TZKK89Zmm\nwXjg7aEnJyfW6/UW+k/BbE5ocFmNRqMyWPGc94xbW1tb1VsT+TJbnYe8ltnzE5/hcFgZwvjtXwyo\nQGqk8Qx93JbBYGCnp6f23XffuXlpP3ugBGHsuXB9fb1w7JGNVcxruS6ar7fDhjYxsZFxPo8vBOY1\nijHjtz5xW/W3N1ciUMhKkyqTUb4rWtF9UUr5KeHp9zWX6yjgrLhGG0i3LctLo+seCihfGl/Stx6u\nUOXUI8UnXA/Ugfk24xI2fKkCb/bJ8AX+Dn7H+BFlRsfII/4aPVfDH8tflcfqnYZvDxcz9lPerWvA\nM4apnPLGSfPESY/Ly8sbG5xqQIna4uktOV3Gm5deeg3L5Rk9i+ZpNIdZxkdheM53WPGxMj1+q6TY\nUXGe1wYmrAXVg7x5yPqGp7vxG1CxbvAspVMwlvVwmacD8rUR0dsdI28wT7eMxlufeaSYz8PaOf7s\nzY+I53v1VH4TfXPeXhkcxzv1pGsK48EY2GvXXcpWzus2+t+yaR+N4euPAs51sd8FqRLEz1iAquEL\nxi/eMfMErQptM1twN4/qgTA2eOnOHzy8wID1lbgKhjxDEtPV1ZVNJhM7Pj62s7OzhUtQPYat5D1X\nps4Aa2try7a3t+3g4MD29/dtd3fXtre3bXt7+8ZrfVFfNvqxwWEwGNjFxYUNBoPKI8vMFrx1ot2U\nqP7MkLX+jUbDjo6O7O3bt/bjjz/awcFB9Vw9fkqFQwmVMLOojMgYEMVRBqwGDg7zmLXOb23/1dWV\njUYju7i4sNPTUxsMBu6ONQt95OW1RXfI2HCF+m9sbNjW1pbt7e3Z/v6+HRwc2N7enu3t7S1cYss7\n5B64ZyAEnnBycmInJyd2dHRkx8fHNh6PK0WFDUpefyuAVVDR7/ft5OTEptOpC6C5X7S/dC6y5yp2\nXhmYwQOMDVVcJtaD7qpqPXSMmOdw+zzACOM131UWkbaV+1X7VD9qDI8UlxWt6L4pMhSDIkVnGUqB\n+ZKwSDFlGcJpzD55rHrH9kpxX0kcNcho+bqppncXKq/h356C5ilwWh9vwxMGAyjbCI+UeP7PddD7\nXfklSMCQ8MRWxc/rW5Uz2q+KhSH7sMmpG0veSQQuhz1hUGd81EtQ+1O9+jxjAxPPB4w7G770GhHu\nG4xBTsnWsBQuLA2LjBAenvPCdH1FYZqfN15Iw7iZPSiBwTlvby55c0zLSbUX+eMFBTr31LjOH2/u\nox18vzM2OvXUj4edvTnHXqaIw44UPK/UmKwe9Tp2iuuUF2nfRWPs9a/2P6ePSPmlhyu9TeJU3bxy\nNW/PyQPPdWMlms9KUVhK/uXyS8mTKE2OIn0uRY/G8PVHoWgi34Z0Muruv5ktMG0YW9iAAsaWyhsM\nTZVFVcLUiMYu6XqXF+oAUAGlHke6PHCE/kN/Qoivra1V9wihfd5i9ZhNxIDQZhgcWq2W7e7u2sHB\ngT158sT29vZsd3fXdnZ2bHt7u/L8UoOXMiPdsby8vKwEEYxg/X7f+v2+9Xo9G41GleKMftKdQ1bM\no7axULy+vrZut2vHx8d2enpqT548sZ2dnYV03m516byNQHJKANTJM2LuOZClgi8FTlL1wtw4Pz+3\n4+Pj6p7CnIu65qHlMcBHvdfW1qzdblcGLp53nU6nur8Dc8MTiB4owxxqt9uV0fbp06f29ddf29nZ\nmR0fH1fzA+tV37TFbUVdtY/NrPKMG41Gdnl5WR234PFhXgKKlOVGo7Gwa4lnKBdtY77B9UsZjlEX\n9nbTdjUajeRRQh7P+XxezY8ccMjNO26fjiOUP27Lilb0UKjOnIyU2DrpIx6cqgevQZY1rBBHeTK2\nq+PxxWu+ZO3jW2UhnuubEr22e2m1rZ4C7PWp4kTGfXw/o9lNI5bnPaVeE9qPvIHKR/RTBoSUgs11\nRh7AosB0fEF87s5ZzwjDPFo3f/lteN4JDDUqen0fzRs2frF3HONos5seikrRc5WrnkHHw1caxuMX\njaM3H1NlaZ5efsBZmhcMg1hD3H9s/Pba5+EFltlenRgvqAMAb2Zi3ukGq/72xgllbW1tWafTsd3d\n3QVHiPF4XBlHtR2RERb/9aUZPF917abWCRN7mumpHZ0LJc/xHRlpSuRCVJ6Xn6erePxWw1SGeGVx\nH4J3Mg/TfvTWn8fLdU56faf9wHX3+jaS36kwL24prQxfD4Q8pr6sYqITk5mCCkkobbzjw2/BUW8B\nTzn2PhzOzFANX+piy8cbcfmymVWMPgIWHrjg/kD4dDq1Xq9X7WBA6Yz6UPPhvkXdG43GgmfXixcv\n7OXLl/bq1avKywu7MQzevLH2ygAowRiNRiMbDod2enpqJycn9uHDh+rtlLxDCIBr9gncQPH1AMF8\nPr/BIIfDoZ2dndnp6am9ePHCnj59utAf2g7tM+1P73nEcKN8coyQ40TMO5WHCiVPkdCyVJCbfbrs\nFN5eeGMZQEdkWInKwzzmlzkACDWbTTs8PLQXL17YV199ZYeHh9bpdKo7O9i7y/P48frGW9d7e3vV\njiCOb/7888/WaDSs2+3aYDCo+qFEmeM6TKfT6mUal5eXlXHYE6L6iQQ+yvE8GvjDxiuUo4Ja+4z5\nqtaTyTO6cVwcw9Sx5/ZqXVKKjPJgbS/TsjJmRSv6HBQZjUDK20GpNKXAeRkqWT+qzLEy4hm/UpRb\n9/w76ifFW+plkWpXlG9pffijRxyxCcGGL1bw2OtLFXftP26jXuhcWmfuL+aj7GED7329GN67csOT\nT55MwTcMdvyiKZzCUONXdJeZjhmXpbLUM0KqPNQ2KY7US/ERFm32KUbwwrz8FFdEeBZh2vfaHxEv\nUa8uhHE/oO+ht2AcuM9SsldlNZetdVxb++0UCeZbu91eOP3iGYl1jnn958Ux+4RhJ5NJtdk+HA5t\nPB4v4DzUF+tO64z28QuSvLnj8UlvbiAdz90SvSPV9lQ6TR9RpJ9wP4A8/JrSRb05Hn24TOWdXn29\nzY8UVmQcqnE8w5riVy0nykvDojVfl1aGrwdIucVVSiy09cNxzOyGtxd7fHl10by9I45anrpms9FL\n3dL5Hive2WAlXkGGMktebFj0o9HIut1uBbI8BpwDf2gLBMLz58/t22+/tT/96U/2zTff2OHhoe3v\n71fHGfUeJWXo7GWlgkL7++rqyvb39202m9nz589tOBza+fl55X3z4cMH+/XXX200GlVvTWm1WjfO\n6rPnB4j/s0CZTqf266+/2suXL+27775bqCcEVqnw8EBHxPAiUuYXMVOE5+qR+83zyDMo8E4T70DN\nZjMbj8d2cXFhFxcXlVcghIPmF7UZfaz3U83nc/vqq6+qz+HhYWXw6nQ6N14ZXWqI8owluoax2729\nvW37+/v26tUr++mnn+z169d2fHxso9HI2u32jaMSTAwIOX/cHba7u+uCRh4jNqJHnl8weOMiXxbM\nXD7v7KLPFMByHRj46RxQsIJ8vF1vHh/vknvtNw8oePOIeXMKxKxoRQ+FUgoAx7nrMj1K1cELL1GW\nVKnzjCMRoFdZ5JWjWEbjgh+wN5S3AVPaZq5n1F8ok/GeesiA/2KD07u2QrEeytPjj1qOHoXy6uwp\noKgj0quxa2dnp7o6gL2oo01YfqYGJcWezMc7nY5dX3+6c3MwGCxsEM1msxsblh7uV4wEGcbl8cYz\nyyxvzqoy6xkVuDzFl9r+1PO6Yfh9m7rxmOmY8BgDJ7PhC7iA66H1VAzJdVSMAB2o3W5XRi/vyorI\nEyjqt1QYr0kY2XZ3d63b7Vqv17PhcFh5qevcYDw0n8+rEyjAv9w+Xts8J715Bpyn/RRhatRB5wY/\n198p0vnFfRXlpfhR24O2cnu8/LS+nuwA6VxQuaP1Ts2LnBzUeF77U3Ij+s9pb1M3j1aGrwdGOSad\nIp0EKvhYCeK4MOLg7T58xFCZCOfLv9WrgQUqviODlx5v5KNJ6+vrN95Ax7uCYJrab6oEY5EMh8PK\n8KUMOmJq/J/r1Wq17PDw0H744Qf7l3/5F/vuu+/s5cuXtru7a81m0/Xs8sZYd2W8unt9D/D64sUL\n6/V6dnx8bIeHh7azs2Onp6d2cXFR9S2TJyC0nixkLi8v7ejoyE5OTmw8Hlu73b5RdwWjXh/yOGic\nZRRzNQB4gswDfZzWi5+qo64HnkcKZqbTqfX7fet2u9bv983s5l1qkcGCgRDPd3wAwL/++mt7+fKl\nPXv2zPb29mxnZ8e9vys1t7VNUb24bjB8dTod29nZscPDwwVPzOPj42od630s3jjyHIQBntN4wBjA\nTHmNEo8LeBvApGeYi4CSGtdSvE/7PJonXDYbxVJ9VTo/I3mg6Va0oodCj31OemvXk/ee0UuVxpKy\nUsR8jPEXewdxWZ4iV1e5ULmmm6OM9/QIIss37ZfImMTykTedtI1eGzwlU+sKvr2xsVEZvHDkH942\nEf5UXKfPvTCVOSpDZrOZdTqd6soL3L/ERyBR9wjvg1je8MkAYHG+o8oz1Hk4NTLgantZzqWU7mhO\n5sK85xyu5aMvvLi8KY04POYYF+23CLsoqUxWXWBjY8Pa7Xa1ocnGVm9deHqF5u/VSXG89g8Mb/ym\n7OFwWJ0yQTpvDLC+GRshDOG6sajzSdN6vz2dN8JzXt+UUjRnU3G1jqwLaLinl/JYeePtrTPGnPwb\n4axraD9EfCPVxgifes/4dx0Zcxe0Mnw9MKqzoCJi4a3AQycYhCR2k/RiRjAcVcq5LIAMM1vYdfLq\nwoKZQZC+SWQ+ny/chwVm6x1t9Bi/9qnZb0J9OBxar9erAI1eQMnx+TcW53Q6tbW1Net0Ovb999/b\nv/3bv9n3339v33zzjSuQUswp99E26DP0a6fTsf39fXv69Kl9++239q//+q/2888/208//WRv3761\nk5OTitHiTgJ+ZTjyx9hhzNGG2Wxmx8fHdnR0ZL1er9rtBDPVXUyv7++CIkU+8vThOJxehT4LR88I\nxMRM2hOq3N7xeGxnZ2fW6/VsPB4v7EozKThlryrsgENobWxs2M7Ojv3pT3+yb7/91r766is7ODio\njlvgOK3uLulvr49ybef+Zt6ytvbb0Y8ff/zRvvrqK3v27Jn94x//sL///e82Go2s1WrdADXapzyX\ncKeECulozJAe3qmR4RgAdjab2Wg0ql7frmPL6b0dXy2Dd3gVoHFc8BwFZGgTK27eGHEdvX5EPC1H\nxy9lWFvRiu6TSuZlynhbd26nQHcUFhm3UoZlTylR+alxbqsMcF7AY2r48uoakWco4np6Sid75KJs\nPk43n88XNnbg7aXGQM/TKMJMKIePUCJ+tPHEMgR1RN2Ar/b3921vb++Ghxcb5rRe0cYTP1fZGMlv\n3mza29uz6XRqw+HQ+v1+5QUGz2Zupx6D9IxT0+nU5vObl9yrR4qOidbRM97pXPRkWArD6XrwsJZn\n+PKMMJ4S7m3aeuVw37FM9445RjLa08O0bJSztrZmrVar2lzsdDrWbDbDucc8SHkL8uUwfY66sOc7\n63Hr6+vV3G+329ZsNqtNXRhfQbyhiLUP/cPMbpSBeqEtWk8dNx1bxFWe4xlgPIr0Lo8HR/Nb0yl2\n9eqrcUHKS7SMaF577dN+1Dp5WDEnDyIcqnM5kpNRGbmwu8KtK8PXAyNvkubIU24io5dn+NLL5CHw\nPHdpVZA97yHvw7tvbPTiN/pAeDQany4O9Y448nllBQ8Ro0A7U0o1p9cFDKEG75bvv//e/vKXv9iP\nP/5oT58+tf39/QWhqN+adwTavGdcRw88zOfzyhV5Z2fHDg4OKrfkg4MDe/fuXfVGQbwxb3Nz88aL\nC7ie6F/s0gwGAzs/P7eTkxM7ODi4Uc/IcOj9X4aU6aWAjqbR9aFASvPz4nBeDDDQd1qP6+vf7kfD\nsT8GTdzvum5QBygBmPPYbTs8PKwMnK9evbLd3d0KhPC9X14btT9B3B5ts6ZR8MS7SVAKUP/5fG7v\n37+38/PzCjil1hzCYYCPgKu2gQ09PEYRHwA/Aj/Y2tqq2gX+EoFgzkeBIwNF7VvtQ15v3Kc5YBBR\nxKM5jPuM2xTNkRWt6EtTXVkRAf1SSoFpL0yn/vfmAAAgAElEQVR5jJaX4lXMQyKDV2pN5taqp3Ao\nVvNwXUSKMXKkfFCxqHp6Kc5IXV3heUqhjhzOnk6Rt5e2He0ERsQLmjY3N217e9v29vbsyZMn1Z2Z\nutkX3Q/p4aIc7osMZYpzGR+3221rt9vV275xATnaj7Te3VOQ0+wtBjweGb5SbeAx0bkY4UNuYzQP\neQ5qv0SUWp9eHrqutQ2Me+Bpjv7y3o7q4QNtg/5H3nhpAuYcPL28K1N0XXCekeFLeQ3SeHNYsTbf\nvQqcOhgMbDAY3HCa4A94gNfv+sxrQ8SLvPZhnrPhLjfHcvNJ61pC0dwHdlYvLMTxeHmUb4pfcP9h\nrmh5nCbXT1E9lolT2td1yyuhleHrgVHpAvRIgYZnmEI8EJ/p13sfIoOW2aedDt3d0HLY4KVGLwZD\nbHjDGXZ4e+kRx5Tbe8RoIKCgVKuxgtMz80d74An37bff2n/+53/a3/72N/v222/t4ODghmu+J5SQ\nn2cgygkBj6FxH+M/Lr2ES/SLFy/s1atX9vPPP9vf//53e/PmjY1GIzMzazabN95CAzDKfQABj7uq\njo6O7Ouvv862UWnZ+RyRCmQPbHhlp557oETntJbPRhete7/ft6OjI5tOpwveXvBojIwVbPRC3s1m\n077++mv79ttv7bvvvrMnT57Y/v5+9bKH3B1eChqiduXCtO/Y+wtj/+rVq8or8P/9v/9n//d//2fz\nefxGUZ3f2HH24kbtgociG58YcHF8AH3Mf+VxWp5XB2+uQGHSeejxRT4asIyhS/uA84oUWwWwdwUi\nVrSiuybm6UyRoSqKz2GpckrDcuXkZJHGTXky1TF+5QxUagxSA1iKOO+oTR725I8eoWMjgveiIsZK\n2k9eP/IH9chd9s5tYaPX9fWntxjDo3pvb+/GXabsqcb94+EiyD/FgNwOTcNhbOwws6rfms1mdfQS\nm5O9Xs/6/f6CEU833ZCXYhI1fLGBUmV1hDciA6X3re1MPfP6JSfHcmFenvjmueaNLW+i6dujOS9P\nLkdGNVyhAvwEo6Z3BJjrE7XT0yO8tkbjo+sdeHdzc9N2d3cXnBLMrDK8Ii3mFxu7uUwdQ+V9Xhyu\nj9fXMIAzxtINSe2PVD9FfVZKURt5LefKi/LMxff4UbRmFYem+pvDo7EobcttwyJ5nKKV4esBUbTI\nU6RKW+TlFQEjZtx6/M1TnvS5p1xFH3WzZ8MXFF29uFE9vbwdQe077k984zgnPhFj1X6HV8ju7q49\nf/7c/ud//sf+/d//3b7++mvb399feJsKC0vvmccA+XnJPEjNDzAB9A/qBk+wp0+f2k8//WRHR0d2\ndnZmjUajOkbH91kws1xfX68E1mg0snfv3tkPP/xwY+fQ87jTuuWIBZ4ysxTYV0GJsJQSoHlyHlq+\nrh/PWMFu1tfXv11EOxgM7OzsrLp8VsvU9dFoNBaUALPfjGB4S+g333xjz58/t6dPny7sAEa74V6/\nRcJK06Tml2cg0vHf3d21r7/+ulpvv/zyi3348KFa19rnXHcoSJ5x2iNeE+pJBdK3Tc3ncxuPx5Uh\n/Orqylqt1o38EJ/vkPFAGIMr3s3jeqSAAkAiv0reo1Ihr3xXZQHznpziu6IVfSny5DeoZF14MiO3\nZlI8sYRfenXQMlPKj36UX3D+ESbz6qj8OpU2ag/nA/7qyT+vfsx/9NhhZPCKPOHY8OXJHvYKSXm2\n6Rixwevjx4/VlRo42ohrBDY3N2+MHcteHWc1RvCzyLjFaTWM89D2NxqNqo58ATruAON7XjGGWi94\nfsEoqS8e4HZyH+pzbas3n711yjjKm38617y+qZPOq4e2jdc+fuubElMGXV0vinX4GXv14y45vKDI\nMwqneEuqf5btUy/e+vq6NZvNG3N3Pp9XG4roDxiIvSPWSKd3wUZ8MaoPnjOvwofbnmpnrt+8OCmK\nsB7WlPZFKo2G6Trzwj254zl2lKwVbVNKTuncKZHFy8rpknFQejSGrz8SOM9NQrO0Aq3GLy8NP+P7\nEPgVyZxe06pSxXE8ZYt34Ngdnd3fNzc3qwtDm83mwmXZ0b1e2m9qucbn+vp64eL+aNeEFxe34eXL\nl/bXv/7V/va3v9lf/vKXyt3XzD+2EFnYPeHvCS1lUBqWEk7z+bzaDWy1Wra/v1+99a/T6VSXUiIu\n0vF4oQzudxi+ut3uDUDk7VjznChhTsogmaL17+WfygfPckqOx+i9XSNOy2BoMplUF9vjzg1dm1oX\n7KQBUG9sbNju7q79+c9/tu+//95evHhRvS1UXd4j4ZciBl9e21OCRuutgHFtba26bwzHZY+Pjyt+\n4yk3KA+8iPPXvtLxRh3Y6JQa40ajsWDwj3gCe0FyXUDMI3m3l+sReXdFICZH6GtPMeU4Ot/0o3mu\naEX3TcuAYg9ol6Rbhrz8dB179Ynkv+IPJq+cSHallLoIG3rlRHxAZarKjIi/KMYDT+TjjcAP+nZG\n7Sv1oDKzBfyB55HhS/Pjo33g4c1m0/b29uzw8NAODg6ql8VwH6GunodWzqsiNda5MJZBaBPqgDdO\n8sYxrh3A9RaMhdE3eAbDFz4sF9m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MHBg42MCD7aBHR0dpqyMbgUej0dj2RmyxeP/99+3+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yHXicGrB+S5fjwOZmZenuXh8Watlz7jMBjMl5aW0g1h022OU7oudFXAHmGdq+ZdJr2i+Fcp\nSw6zqbeA8mYtT6QE5eRamXqXMXCVUcA83u+ViZ8p/4/K4tWxqBwqi+FJA8MXZOL8/Lx7thfSYDzl\nyT0YziBDcU7T/v6+HR0dWavVSpfT8NmfekQF5AZ79KkSzAthnoEF6bARDgfst1otW1xctJWVFavX\n62M7GcxeHpYPWejpDmV1EK+PuU29Poz6N9fvEWbw5oMne+HVj9s94QnIuplXXg+fRfX33gNeYg8s\n1hcQhm2q8PhuNBpWrVYvGdDYk1CNXMiXvb5QbsW8ubbVOkSkuEmfe2mr7lvGGFZGH8uNWf7Gby67\nN3aRH+P/ojGQy5v7xTvUXr17uU5l66VhZeN775XlwWXyvkqciKaGr2tEGLxssIgmor7H3/qMhXLO\niIF4bPhihqrKrCcIfwjEhq+joyO7deuW3bx50+r1egIqOWavSisLImxTOjk5sYcPH9qnn35qn3zy\nydiBkkwKtEC6uqflYQbKAhDPQDDkDQaDxFS/+OILOz4+tn/8x3+009PTdJDmD41yxi8AY74kotvt\n2tdff21bW1tptVYBoYIjDcvNGZ1znsFMeQL+7+zs2G9/+1tbW1uzDz/8MIWZvQRRSIcBth5oCwJo\nH41GaUUY2yMApNWwx/XwVjE94ILyF22bZt7ngdvcuwqImB96eXuAC3VCe+p5jByf514RKOQ+17by\n3sOW66WlpUuXGUxpSteNIixT9KxMejnl8yr5lynbVeeip7yogs7PvHeZyijgUb6aRlQeNtQpD1b+\n5dUr1w658pZ9T99huQoD0HA4TIavSqXiGr6Uv6vhS+U2b08aDofW7Xbt+PjYDg4O7OTkxLrdrpm9\n9OrQc6b4iArG4p6cUwMHv8MGDZSHvdxarZbNz89bq9Wy1dXVdNA9H8PAuJ/xCLe5Z3DwyqrPovEV\nyWvuA6+fo+deWl57XlxcjHnC9Xq95AmIeDnDh7ZPRF590G86njAmebx2Op20+IWxyjgOmIaxCNeZ\nxyp/1PtL43u6CcjTVb0+4XeVR3jGrknpKu8oKQ4rw9MVq5flr16bYv6zxynSynn1Ik5uHuizSeRt\nEY+N+i7Xp98Wdp3ug7hGxNsLc54jqlybxSuAKqQ1bSZlhNHEzMW77krYaDSys7Mza7fbNhwOk4DR\nFUCvPxSARG1+cfHiIPl///d/t+3t7TElWhmbMt9c/6H8ubqZ2ZgnCxglu/fjUO7t7W17+vSpDQaD\n19a+3ycVGSFADOwBSgEUnz9/bvv7++7qrJe+twIUKS68nUHDcv1aqVRsMBjY9va2PXnyxB4+fGjd\nbvfSllxtBzaE4sOrTHyL0OLiop2entrx8XE6AJjHpMeb1Cg2yTOvjlEYt5XXB54CxGGqeHiekno2\nIsCl1kHbOJqv2qceoFEFc2ZmxqrVqtXr9bQtZUpT+qHQ68QORQafq6ZThh8X5ROlOUmZcooGP8vx\nCI9vTUq5urxqPb28yvRHhEe5njAA4eZdhHvbi5gi/G320pAF7yqcpYXtct1u105PT200ennjo5mN\nGRq4Xl6+XltMosSi7FhkBbbp9/tpgYvjafm0bRWbFhmGtOweTaKMT5IXy+4iwxe8vXA+56vMlag+\nkUFPDRy8iMbnj+HQfYwp7jNNQ3EZ58lYB4Q+jwwtRe2Qa6ur4jtQ2bGj48Dbvhvp15P0dVFddd5q\neXUsejc5sj5yVZmk9G3q6q+D178KTT2+rhFFBg5WfPTwwmiAlFEyvTDko0I4Up4jkHGdCYav09NT\nq9fryfDFt3eUVdSZKUFw9Xo9++abb+z3v/+97e7uXgJZPAZY+VWDAsrCYcpwPEbPqzv4D2ZrZjYY\nDOz8/NyeP39uX3/9tW1sbFitVnv9Df0dkY7PssKVDV+4WWd7e9sODw9dV/IoXy8/Hhe6QhuBy9xc\nxyru06dP7fPPP7elpSXb2NhIK38esQenrhiywWdubs4WFxft7OzMms1mukWS21TrjN/KqzyjlFfn\nqMwIuwqv0bIhPa63KjYef9TymI0bOCPgxG3mKS4RHx2NRkmhWlpaSp6n4CdTmtJ1orKK7STGqzL8\nwJtXk4Tl4pcpX2S4yeXjpaEU8bUiuipuU5kU1Vf5dBkFLddGZcZDZBxinjwajZIn99nZWVrA0EUg\nxlRIQxcxVGHFwmGlUkneXs1m01qtVjJ8IS/Fdyr3PONM1I5RfygmRDlRxvPz83S4/2AwSNsdIwwS\nKfKRsSkKU2wUGSOidshhOu6zCPNxmjBS4tgIHOkAoxI7IyAdPacrIsb9Xttoe/B4MHt5azvrguqV\nhktueDsrp8NpaNmBAfn8YMa+RfiO6xHFi3Bf1HbKN8oaoJTnFM0Zry6ensvlnYRy5Y7GPXuMqkOE\n7sSK6Cpl/TbTidKcRL5PWp6px9c1IZ6YzPRYqVaFTCdrkVErMl7p6hU/9/I2u7xfvCxzetNpNHpx\n8Gez2bThcGjLy8tWq9UubRmM2jTyrpudnU2HhW9vb9vjx4/t4cOHdnx8XAh0dfWF213DefUSaeie\nfd6CxatJ8O6Bx9ezZ8/s0aNH1uv1vq3m/s5pEmMUf1qtlu3v79v+/r61Wq1LYCjywNN0Maf47A6A\nbwWWKAfiRkoP5727u2t/+MMfbG9vL9w+yGNXvTe9Dw66Pzs7s06nk85Dicatx4cifpTjXVG5o/5j\nkF8EcLhvPC9bBs7Mkzkd74bcSHHTueuBS0+hRLyZmRlbWlpK2xx5O8qUpjSlyajI+DJJ/NdJRYah\niCJDRS6+9/t1lqnsOxHfzqUVGVk4vqdUIgweX3xhjGf04rw9vKwGJd4qiAPSj4+PrdVqpUPKOa3I\nyOAZSKK24DrxRS3qoYxnvMh5dnaWPJxyHl+RXFMDk4ZHNOl4KhobkbGjbFpoPxgBvfO9yqav2IUX\nUBVHFOEAzwsdOgqOnuAbSSM+EGEqlDfyLIravQw2i/hKriy5fMrqmLm0o7yLeGWEWVlHzrVXNOb4\nGzxEb5NVncRLt2yeRXW96nyelK4y/yehqcfXNSNmVLiFxROy+PaYTbTVSOPliOOhPDyRvK2YPwQC\nIOp2u3ZxcWErKytpRSUyEDJpX7HQWVxctKOjI/vss8/syy+/TEYvrLZEQpWZngISBVxcJjacRoq5\nd2Wx2QtA9PTpU/vyyy/t5OTEbt++7Z5Bdh2pqA5qaLy4uLCjoyPb3d21VquVVm0ZCOM/4qPt2Y2c\n5xLKwF57ngLARi+dfyAGZkdHR/bll1/a8+fP7f333w+NJJVKZWw8sau7lhvjCyAZ516op1ukKGib\neyA1B8q8ejJPmhQcaZpIQxUaBQEeAI74K6dftI1FwzTtSqVi8/PztrKyYrVaLW1znBq+pvRDoNcJ\nsl+nAv5tGntAPI9zxg7EzeWR431qBJq0jB6eLFKSonyisJyBoQyP98rmhfFikxoWWOmEkYHr7Z3t\nyAorzl3EdjTcDNjr9ez09DTJGd66pm2s9WZDFecN+cyKsRpN1CjEH/Zw6nQ61u12rV6vj+0q4bS8\ndldji5Y9Nw60f8rOHcay+iwXvygtGL5w4YF6e/HODMYL3uVTbOhSLBnhH8b5MJCaXb7sCOMXnmnd\nbteq1Wry+uK+w6KZ1w46tvm9aLHVw3b6nOvj9VPUD5qWhkfPy/K9XJxX4a1FZc+ViX9jkZl5EOKo\nYZTf1zmeyyN6Nml8ff46ZHhRHpPQ1PB1TYgnNpgVK5/eSpHZuMcCK+L8UQEUKX45JscTrFKpXNpq\nk/N4uS40Go3SrS7D4dDMzGq1mi0uLl5acTErFqhI0+xF+ywuLlqn07H//M//tM8//zwdkM5CNGIg\nvKUR/8/PzxOjXFxcTK7OeMcTaLi9MQe4zF4avv785z/b/v6+vf3229ZoNK51H+faVz9oX4CAvb09\n29raunTemRp3EF+BNBuWFJhon/Cc9jy+eJ6bjd+wCk/FZ8+e2e7urv3oRz+ypaWlMTClhhWUlfkA\nG71gyMOZKMPhMF16gPJ6igk/5y3b3O5eO3KYplUkBLU/ikAJGx2Zz3k8zwOsnHYUzmloWT2jF/cT\nvDCr1aqtra1ZrVZLByJPDV9Tuu70po/hnELxuijiJ7n40acM5ZSeKD/d+u296/G3KMwrk+JT752r\nGDgYC7NsZ0/rSOnk+vNiFtJB+v+fvW/9jeu6rt8z5LyHj+FTjKxYlq04zsOO49hBg+QXIAnSDy3Q\nr/3Sf7VBURRtkARF3DpJHT9kW7IsUqREDsmZ4Wt+H4R1tWZx73PvUKKlUWYDBMl77zn3PPdee519\nzgXpValUMjt5cHBg3W7X9vf3bTAY2HA4HNnGxNiQ36PkCqLwdccFb9dEXTi6n+04Y0MIopkODg6y\nM8gQPcSLnIxHIqc7wlVPQnqx3+KN8Wh86LvVZ+Jn8YP2ZHzDW2B54ZuPudAzUZmYgi1H+6CfmEzj\nrYhmozsBIEywoQ4gvnDQPX+VM+ofxanaBtrfuugatW/UB5ctRXVeND5Tz/M7Uvfz0qeEtzdjDKjP\noH2h70nNvagsqfJdhMB6Hm34lPiaIGGlgh9skeOzCDyyS4kvjgJisgbp+Z1FFFhkmPKemSQZDod2\ndHRkR0dHWRvqJ57VsebfyIP/5n7Z29uzL7/80j766CO7d+/eOWPDxo0JGAj6H32LA/e9gxDZUGk0\nD4hU5M3GmT9hvbe3Z1tbW9n2vmazeQ48TZoomFLhdgeA6ff7trOzY/fv38+ivfAsQBDmGK/8gjBV\nMMtlYWKIv6TIYDbVnzzeAKSOjo7s1q1b9pe//MWWl5dtfn4+A7n6vNko0Y7rDMIZ9PN2AHxGO3JU\nlOwqAnC5TtpWqbT8fNS3Xtvr3FVAGOlKfpbHi0dIeVsHVIdznqz7a7WaNRoNm5ubs/n5+SyiYCpT\nmVTROZs3r8fJM3Jwo2t598aVouVW3aF6h/PyHBy2Axp975Upr55Fyu3pTFxPpSlyT/NO2csiuj1y\n7DUSB8/wx128BU7YQO+cV424B2ZAFBW+mK2R1fxuxZf8PsafHinCOwaw6MbbHvFb64W2GAwGI4e5\n88Kdjr1ozuAe//buF5lX0RjzCBq9F+WFe7rgC+EFSLSBpmcCSvuI68jv4HaEYPzxb69e3GbqS6C8\nfNZXvV7PCE6MUcYZXG7FIxEeSkWoeW1dVCLc5tmFIuOliO4qes/7/0nIM50zGv0H7K8fGEM/F13k\n1PHjEXhRPqk2HNcWX1Se5nsmhvi6CMiYdCnCpCJSqFwuZ/vvmQDjcGwGQHzQJhtqzpvT5JVRB2XK\nuE2qDIfDLNoLhCPYdwVlkZOtChKA4+zszHZ2duyLL76wW7du2c7OjtXr9RECA/1pdh7gsKEqlUrZ\n190YzKhihfCKFZ/XxF82giLGitRwOLTDw0Pb2dmxra0t29vbs7W1tUtq+WcrbBS4nUHy9Ho9297e\ntp2dnRHii6PuePUOH0PgiCiO5sJvjB0YPDPLwNfJyUnWL9zvHHLPBArqAWP52WefWafTsbfffntk\n/HJd9W+O+lKnigEcDtJXUsvT4QyytL0hXlnUMfXArpefNxdTBp3z9Ygvnede2bkfeBXPu69OB9dV\ntzgMh0Or1+vWbDat3W5nh9qndOzfoh2dyt+OPGt8kQLnnq7xiCslvVhnqJPNz7ANUYJEo/u9cnv/\nF9EXkVPOdSyaV5R/6v+8vCNChO9xWb2oKNhh71B7tn+8yMXtz84qzl86ODjIzs7Ce7w+UvzN+B0/\nZo/JGY5G1/T4GxFHiFzCT6VSGRlHvL2PjzHQrXt4nsdung+g7R71mWd3vfxS4qVT2x1hELQnSC+0\nGY8NCNoFRAXuabSdF83D5DTwnUeSpTAJxhAWRnmc4fxPHqPQHxpV6JFafM/DNHntP+6YyOvnlB4o\nOrYivaFljd4V5cE6OBKdL0xksf7B9lSOOGW/UEl6HR8XEcaoWueieXr42ptrXrpxJG9seTIxxNfe\n3t6zLoKZ5Uc5XeZ7WfnhBxOBz3VBiDOvULADzIZaQVHeREUZWLxJp45pkfyfdxkOHxNftVrNarXa\nufYzi1cnWbkrsDk9PbW7d+/a7du37ejoaMSIIS36VhU351ur1UbCmiNHnIlOdqhxD5EjOOAVv0Gu\nwgD2+3377LPP7O7du/bKK69kIGySxQPD+K3GfjAY2N7enj148MAePnyYtY/Z47NCsMqGbWjetgLu\nRyayVBDlA8FZE9548cKfUa7t7W374osvbGtryzY2NjIA5pFQUXvkEV8RCI6cpOg9nAa/mTDj/J6m\nfuZyR4SVzkNPP2qemr9GfHE0IYSf4YhdjKXT01M7PDzM/td8vXHwtyZXrlx51kWYSo70+/3sbyWC\nVFIkyzhpcF2drzwnPnqX97w6AZo3O5q6YGA2esYUfnQRjKOAEcHrLVCo3oowWcoJTV33HB5cVzun\n+Xj6XNNxmiJ2JVVe7RN2PJk80nOaIofOzNfVahtx/hKwudnocSBKSihG1/KgvFiYZFFHmgkUjBvg\nP47o58VPRA5hu6MuqCpB47URtzn/DSyqz+Zd1zx1gZf7RvvZm+tMGGLMMemFL1uC/EObK+5XcpTH\nE+Mrz/6DNEM9MWZQHg5WwDPoc43iQrTe4eGhHRwcWLPZtGazmZWR+w1RfNwWOo4UC2HL6+np6Qgh\nh2d1ThTRi9znOq50zOC6FwDAhCPKwXXFj9ZXP/TF79IFBtalfJ37AD+lUukc+cn5K+mFcmOuso7n\nQAT+8WxMkTZHO2q6yAdSybs3ro727GSeFH0OMjHE18HBwbMugpkV67in/T4lu5idZzCE1X5MkKOj\noxF22OzxtiU1pEWBTcoRjsgYKPRJJr3MHtUTKz7VatXq9foI8WXmryYweEM+LHBcv/jiC/viiy+y\nFTU2JqxUGVxw3jjrBwQLGxDuD12t85x5fbcqZHxYod/v2507d+zevXsj0SgviniOChubfr9ve3t7\ntru7e05HDYePtxGUy+WRUHN1cMxGV9s8w8O6AMIGHHkooMJ1lm63a1tbW7a1tWXdbtfm5uZGtjlo\nuXg883z2iC+cg6TYE8cAACAASURBVKdgRsvBY1fnij7rXQPQ4/y0vThtlA/f80BBEYPutVmeeOCF\nAY7OPwjGEDvAODMG25s1/9SXP6cyledFmAgwG9W76gRHYzkP1OelGYf4isoaORvqoHjOL+t6JrxK\npcer/pjn7FyD+EKUNhMb7Iwz7tMFSc9J8drRS+/hyHEcF09/62/vXl65Uzpf+0d1MuMhtr3cx2wD\n9R7jdthG/iogkxrcR+o0832OLkM78HlenAZ5KTmDPPjrlbywiTzZl0CZB4NBdmYshPEp3sPtycLj\niMsa9Qv3neoDfdYjxbSM0Tu4z/ken+3FEV+KPdSnUvvLRCrXSdsGY4WPtuC0uKfEo26ZHA6HGWnJ\nRKuXVseJ9ptHPPE5eCDOuL/Yx/BwVXTd06M61/W6lhv/65xUnKX9pOQW563jQwk3Jci0XVhHozwp\nghSLm0p6YTcF4zyvjmxv8tpc73nX+fe494pc0/5N2Wozn4spam8mhvjCYeJ/S8JGs1qtZtuj+Owm\nPj/A7LHCwcpArVazer0+wvzji2s6qfPAkDqtrCzZeGPQQlmnDNukCUBCo9E4F/HlTTrP4Tc7TxYO\nBgP7+OOP7dNPP7VSqZR9KZIVpIJlBjczMzPWbret2WyOABJWhADQtVrNqtVqpmR5W5oCL1VI2LqH\nVYijoyPb3Ny0Bw8ehMBikkWNrNnoVlEQX7zFUcUjpzVPrPRx5CZAlqfoefskxg/uwYB645GBAPru\n/v371m63MyDMwI3TKBhgoMcrUwCKSsil/ua6FBUFvxd1uhTUaXurY8fzT4Es2p8BTVQOBjNYPYWT\noenZ8QHBXa1Wszbv9/u2u7ubzW3YCG91OFWmqUzlWQpHOZqlCebIKeLf0fPevYtc9/JUO83XvGgA\ndaL5TE62G/pFQV3AZOcKRAVsOTupjBmUbFFcgnyZ8MF9djJTPywRFoxwkurg6NmU3SiCP9XpUtIr\nOidV+4HbSHdVwGnlyCkscPBZXerAMummXw9XEoIj/Tg68Ozs7NzWOTOzarWa4TqOJjEbjVyJDnZX\nm+gRWYpZ+brXB3zvIn6DvieSlN3nNvA+EsB4WRepuR107nEa9KOXL+abLlrxwjPq6RFUTDzpNk3s\nQtA2y9OzyNcjKLjOXrqiwrgq6hPNn/1PT99q0AXPragu/G6NrPXGJuNt1e3oCyWneZwq4WZmIzoX\nfYj+Bs5jDgD15+hPnXuq37z2z7Odnj+SRyqn8oN4xGaRNCm7HsnEEF8volPtCYMSkBT48RwaL2oL\nv6Ec+YwBDbs+PDwcUZ7eoC4CZLzndXJNOgEGpTIcDjMCkvtAJaovK9RS6dE5C7u7u/bFF1/YnTt3\nQsdZDQLynp2dzSK9cN4bG6JKpWKNRsMWFxdtbW3N5ufnrdVqZauP2Ka3u7ubHdwJQ4myVCqVDPww\n0Ds5ObHt7W3b3t62fr+fEXYvqmj7Hxwc2Pb29rmvOZo9JrOir0CZWdY3Kysrtri4aPPz8xkJ9PDh\nQ3vw4IHt7OzY/v7+iHGFjqjVaudWivA7mmswwsfHx/bll1/aV199ZS+//HJGfLHoKpbWgQkwJr9g\n3DF+kEbnhP5dRD9EoLiobskrhzp/uDaOQYbzoddZ0FfYOsHRuexIsdPDNgE6fTgcZud5KOnJ0WEQ\n76MIU5nK8yCRczPOvBvnefz/JPd0HrFj4TmH3pYXs1Enmr8CCMJLz5hi3asR2nz+KDtu0BVKtpRK\npXP2hSM2tA1ZL3rl0R/VqRFmzbuu1yDec6l8+b5eU8zF/cILu16dNS1/0Q/b5Hq9XnaoPZxTtqfR\nuOJn0J/s5II0rlQq1mw2rdVqWavVyvIcDAbnturhHnCft+DJDjU77t448fpP88JP5BsU7eeoH6Nn\nNA8uX/QM1xvtxlF13hZYjsLXSC8EIrTb7cyXwzZKjAlPN5g9XhTgMaK4hccrX+dIUJSdx5v2jwY0\n4D6P87w+0P/zfHgth9dX3t/e8xFGLCI695T08XSgh6WUZGJyWYNCPJ3DBKmZZZhd8zezcx/dANHN\n0YlevfKIKW7fi2BFTueNV+9dRW245h2l8WRiPNSLDOBJEwx4EBn1ej2LLMKZTQxwirYJH7jYbDZt\nfn7earWalUolOzw8tN3d3UyZYw94VDZPsSiw4jQvmrBDyaAxMgIeqGLANDs7a7u7u7a1tWVffvml\n3b9/f4S8wo8HQmFc2+22tdvtbDuEArNms2nf/OY37dvf/rb94Ac/sLW1NVtYWLDj42Pb39+3Tz/9\n1P7617/an/70J9va2rL9/f0MoGHFsdFomNnjQ1GZ5MAXDff29rJtli+qsCEzM9vf37fNzU3r9Xpm\nNmoMOTKnVCplwAWrMzMzM7a4uGhXr161d955x1577TVbX1/PCKjPP//cPvroI/vv//5vu3Xrlu3v\n758DD9AVADRsGD0jw8ZiMBjY7du37e7duyMry2yMOQ811myg+W89b8QbuwykGGiz4eayc7t6YE/r\nGwEtb05y+3hlRHl4BT7Kw5vv3v9oXwBqRHnhGSa6EAkI0MzRvwC4AEVeW7ADPRwOs0OKNSpvKlN5\n1uLZ0XEAd2r+5Tm5URq9hv9TxBccHX1GnR8zy4hpzGsscvIXvDQ6S50u1t985AH0BZNP2D3BWyhZ\n96LeXt31b8YBSgIpIeTZDM9RVh3tkRr8t+bh3dP7fC3CtYxz2LHkRWImP3jhiNMBJyK6HtvOeKuj\n9mXkuOM6R/bxVwbr9brNzc3Z6uqqra6u2srKShZlvr+/b91u1/b29mx/f98ODg4yO1mv17Ny6wIa\nEyRMyjD21wgwtvlK7EV4IMLMRfowsutF7nEZVUegzoh2A1nJEV/aPhztg/8xJxcWFmxlZcXW19et\n3W5bo9Gwg4MD63a7tr29bQ8fPrSDg4ORjwhg/HA/e23N7ct1wZjliD6MT8ZaiuUwr7kPuH2U8FZc\nxG2P9lVMFulQ7XftH85L+5d9Jk+vaD1T44r7NiqbN274GZSHz93WaE4tkxfha/aY7AbOZ19PF0dA\nfDHRzfpF/fWI4PPuqbC/6vUF96G+I7L5KZz+pMTcxHioXiO8SALFAQXZaDSsXq9nK/tQfp7RSAkr\nLGaRFxcXs4mGA9EPDw/DSCNPMUQRXZxWlc+kCofGqqKCKNjiNuBnGDBVKhXb29uzO3fuWLfbtePj\n4wyMch4e8EabAixzeXCv1WrZlStX7K233rLvfve79vrrr9vi4qLV6/Vsi9T8/Lw1Go0s/H5zczNT\nzpwXok14VfDo6MgePHhg29vbtru7a51Ox1qt1mV0wTMXry+63a7dvXs3I75YZmdns+2wZo/Bqtkj\nMqLRaNirr75q3/ve9+yHP/yhXb9+feSsreXlZVtZWcnA0yeffGKHh4cjBgPzGV/iHAwGI04MxgPP\na4DZ4+Nju3Pnjt25c8f6/X623ZHrywZSgYMHLACcGSQzEZoCGOpQ8N+eI1D0XgRKtCyek6FAKQ9g\nqUQ6gg0/k2oMQvk8H962omCI8+StFdo/SIezCWdmZrKtD+OAhqlM5bIkmoMqOr/z7hUhwCJQnyK3\nPMeN57TOb42Y4WMslPDiaCEPR+m7OcoX+AJfhi6Xy5nN6fV6I2dLKW5TfcrXdEEC5VNs5O1IQJ4R\ndmJHkyXCj5pv6rc+n8qX72u0lzp4qfpoxAbsM6KulFTgtmanXNsRthWkF85tqtVqtri4mOGGTqdj\n8/PzIx/Xwa4AnB2ECH7YmlLp8dY7dZJhzzmKRJ1yr22j66ivd0/naN49Jdo4jd5jm624A/dY0Nb4\nkJCSfFwOjuIGOYF52Ol0MkJyaWnJ2u129vX1ubm5LMABEf6Hh4cj46NcLmfHkkT+mc49lBUkKcoP\n/VAqlUbIV25Pj7BQIjvqV+0XtI3eZ93FgnsRGc/tEukB/eH7ec96ukbFG8Msqk/zIn3RVqxvosUO\nJld1sRt9y4smvNjCWzDVp+G+8PSS1x4etua/df7jd8repCTCBOPIxBBfL7pg21Kj0cg+OwtA5IGB\niwoU1/z8vM3NzVm73balpSXrdDq2tbVlDx8+zA6YVYXDxl63PnH+PLFZGU+yMLBMbTPVNJ4ChbHF\nas6DBw/szp0758iTiPSCALAAMCtRVS6XrdPp2PXr1+2dd96xN954w5aXl0e2PbVaLVtaWrJqtWpb\nW1t2584d29/fd40NxiiHxWO75M7Oju3u7rpb/iZRtN+4Lxic7u3t2VdffZX1Hc8RtBfnBcOHldk3\n3njDfvKTn9jrr79uy8vLI+9cWVmxK1euWK/Xs/39fbt3714Whcd5lstlq9VqI84Ml1tXJXHt5OTE\n7t69a19++aX1ej0bDocjh2lizHsH5nqAQq95B7p67Yz5gN+qa7j9OV0RQ+nNPw+wqbD+0+gFToNy\ne8Cb66flSTnOpdLoIdZe9EdExDHx5dWLnWt25LSfnga4mMpUxpUIUEdztEjaPPGIK3UKPAch+lEi\nm+ckbzFDJCcWOnmOY6ETzqmnp6J3R5FkrVbLms1mFu0DO+7pEH1PhN9gS3CfnTjWU5Bo0RD3Iqcz\nwsAp/JVyYj2n2Xun1knz9MrM9p/P7WLiyztvyyOIzCwbA2zDmfg6OTnJ8MTy8rKtrq7a8vJytp0O\nJCrvHDk7O8vO/EU92L6USqVsHPH4RRQJYwImJ7QOaMPIfnuEh7e4740Bj/jRecr56buYxPHKhnz4\nUHE+q0vT4DefsYc53W63bWVlxdbW1jLSCz5eo9Gwubm5rK/Q3sDSPMbOzs4y0tQjBj2sp9GBwCwY\n03wsBeej/cb3PP3q3YvmF99XTM1t6ZEwfN97v/pBOjZSOqGIPvHezXUxi+0DY2uUl6NCuR76o8J9\nqzYM8xh54ygN/vCJHnfh2Tl+F/vA3AYRhjWLP2yl7abvStn7ce95MiW+nrFg4AP8gPSCsbpM56NU\nenSIOlYD6/W6DYfDjMAoAjgiBYgwWvzPvydR2Jn0ztpIKWaeyAqYzMz29vZsc3Nz5FPuEHW+ce30\n9DTrM6zoaV/NzMxYp9OxjY0NW1lZsbm5ORfAYhxsbGzY6urqCLmloLVSqWQrlngH2gah+y+qqAE7\nOTmx/f19e/DgwQjhpyuoCtZghBYWFmx9fd2uXLmSbTVQqVQqtrGxYS+//LJ98MEHtre3ZwcHByN9\njX7hw/N5XKrR5INRh8NHZ/11u13r9/s2Nzc3chhrtBVOjbMHCnhLhDc/0B6ogwIvNpoMaJCO73G/\nFBEP3HjGnvUc8ve2OnJ5PBAQgTTo3lKpNHJ2FxNe3vk+KWGApSTYcDjMzhbhj3NgTnO5pzKVSZS8\nMVzkXt5vfg9HYOjcY12KaF8mpPgoC17lZ+ylulWv4Z3s8KJM0DFMdMPJwnY7bS/WdxAvAoRJL36f\nllGjNpAft2P0vNaZ+6CIkxrpXn3Gc1JRTs/OcVk9ewAMzNHefDg8n9ek9hlp8Q4eE149ETWI6KFm\ns5ktmLOjzc/hB7YAdlP7sQjhy23qSarfUj5EHiGlfRml8/KM8Ig+C6ztnc2kpITOfY7eRlADyC4Q\nD/hC/OzsrDUaDWu329Zqtezg4GDkQ0joI3zpkUkLjyhBHTD+VFdo1Bi3qfaRR0gV6W+v77QPojGh\n5dF8WQd5ZVWfS9M+CcZBep23Xp6eHuU+0a2OeMZrj6ge6FM+g7lUKo2cB65ng8N/YwJV57Qn2rbR\nM9oXfC9qq3Hn8JPIlPh6hlIqlUYOJm+1WiNfC8xzcJ703WaWbafE2V47OzvW6/Uy4ksnGtJC2Xr7\nhD0FpQZq0gREB/rMA6dm+UZBnwHRuLW1lREielimmZ0DUcPh0Gq12sghmcgb6UCurK6u2uLiojUa\njRAE1ut1W1tbs8XFxWzbnH5hi51ubGGDAQcJ9CJ/fZUNA1ZcEY3FAjKD+xECAzUzM5NFW66srFi1\nWnXfOTs7a6urq7axsWHz8/NWrVazVVqz0bkIZ4rP5oiIEsxLALqHDx9ar9ezhYWFjJzjqE41xHk/\naCONFGOJ5okHZjkiDOX38kjVNfUuPJcyyJHBLQKkIh2BsQJwDMKLP2QSHWydKgvGWblcHiEveZUe\nzjDy4tXDqUzlWUvevMuT1HwdJx3+9yKh1Inka96X4GDHQXrBEebzQj2yBRI5iKoPlLzB4hifU+Xp\neU2vJBe3BwiqIvaAJXUt716Uzzj5pfL0HFUmt/QnFb3GziwwkkZ7qSPM7cn95S20Mv7UI1KAP7gf\nMQ6A+dm+gEjh6DQeX0ymRsRJHvb15rOObX0+RXBpusgpjvRINH4UE+hCoJJNnD9HefJz/FEa4Gbo\nCMxN+IHsA7LeQF/pAedcF8UH6E8lxdVv0/w4T2/MMS70xkEqD69vkK+m12dQRp4r+mweMVNUovbR\nXQkpAljvecRXtIMoT+9z/rx9UdsJY4fHB4TxntowT1LzVa9HujmVlu958/VpYdMp8fWMBCtvYPmh\n8Djk8esUgDFWzDxpPXKLlSBPGmWOU+GakyJ8bo5GWKlC8gyxZ1DOzs5sMBjY7u6uPXz40KrVqi0s\nLGQgy1sNQV7lctlarVZGhvDqEIS3QeYRqXDA+dlIYWGLBivIfr+fHYz/tyCpCDe0URRdx2AoL4qn\nVHp8hhuigTynCM81m80MXPN44YOMAWzxDM5129jYsLW1tXOEF5c7Bfx1nOrXJdl58gCCjnFtB/xm\nYMPpGJAUFW+OKVjx5h8Teh64idoL6fl56HwmvPRrbvol39S48cCQ13ZmljlCyBeLHlOZyosoKScN\n973ndc4qycXPMumF6B44INDlcG6Z9GK9EUXSRnaZ35+yJXpuILbBKMGlzo/n4HsOX2QfdAFF2z6F\nnbQO2k+R3Ui1lT7jYd080ityRvW6bhPEQgRwNmM3js7Sc34YTzAG0OdThKnaDyVb0f98j22JErlq\nJ6M21r7Ju6d55fWljr8ojTfOoud1jntHiXjPKxmcIjS4XJ5t5+e9fkvlpfMZuA9jUd/j6cXUGI/6\nU3GG1wcehksRWVquvDKkMGDUZlx+Fs+H88qi11LjAc9qlC/nk9fPrKNZZ3uH35tZhik1T/2arxJh\nUf34/7z21nyKPu+9y0tfJF+WKfH1DAQDniO9sApTxFhfRnlQJt4uZRZHVniKrVQqnVsJwN95kQrP\nu3CkjtbHA6gQVuiqwLHv/v79+/bVV1/Z8fHxuS93es7tcDgcIU2hSD0CEl8RwtbF6KwOkHB86Cre\nhfKiHADwfEhvv9+3+/fvj0QjTbqkxurp6an1er1zUXFmj0ntiOjFWODDaaOIL36Oo3UUCANA46MF\nerg5jKtHTg8GA9va2rK9vT139R/5eyAoBe5SWx09oIN8vDRoixQI8ACaviOVR3StqN5ScFME8IIA\nHQ6HmTOqWxzZMfacFe4L7Vsmv+BwgfBEepBt0N/TyK+pTKLkjVfvvoJ8j/ji+94c0x8mOmAjQTjl\nkV7enI6cDkjkqOn/yJ+jR7BNUZ16XbDwnFnPCYr0naZL1cmzDan65un8yCnScniOqqd32d5F2I9J\nCkQK4YB0/jofnlOikftIF46ZnOJy88HZitu4X5nMKUJ24B38dUP+SpwnT2I7itraJ5Ui9p5JL69d\nuQ+iPNgOR2ShvkOfw+88MkTLwIEJ3H/4iNZFzl7WuZb3jHdN/46IMm/uRvkUFY/ISo1j7t9xxrVn\nT1RnqO7n54roSryHfQOO8GddxtHGjA/NbMQGsD/p5eOJp5O9cur9Z4Uxp8TXMxAMPpBezWbz0rc2\nFpFSqZQxwqx8vWgtnjTq8OGap+AnVXjrUOTUegoqpbBPTk7s8PDQ7ty5Yx9//HFGmCCyR1cDVACg\nua/wPgDu+/fv2927d213d9dWV1fd7Y5mj4ire/fu2fb2tvX7fVd5QkCQcrsMBgPb3t5+oYgvM594\nNHvUvvg61jjpMEcGg4E9fPjQHjx4YLu7u5kj5L1ne3vbNjc3s69+8qofC398gJ0cjB8QGnDIQG7g\ny5z4qEGegYsAsjptMKaRUYwAjwccveejVaEUQEiVX4EKnld9qCBFowJS0Rr8N9IhEo+3N3or/pHe\n0bHgOePcD6VSaeQsCJSjXq+PbH2KznebylQmTVKOS4r00nv4rdu92GFFZA9/bAR2nb/UzYtoXoSH\n4oUiGMoj+rn8bBN0Kz6n4ygRr91S2Mf7P9LV0f8efixS/yJ5euX26sl9431kQPuO257bGCTRYDCw\nw8PD7Gw1rRPnD6KUP0SkeaPvsIWy1+tlEbsnJycjz8H+oxz4iQ5rNzu/DRP4AQtx2LIZEcAQJRRZ\nvHnn3Us5yEWwyjjXeUx42NqbsxHxNRwOszZj0pM/XmFmbr8o+aVkq7YL4wEI+hykF36w2MpBDtzu\nXH99R9S33MceZntWJIcn0ZiMMBPu5eWXeo6xZIQT+blIv6bKwLseeFuu2eOz5kCq12q1bIxhMVT1\no9anaHsUFW9c5OmD1P2iMiW+vmbBCj/24/PWwiKG/bLL5hEtUdk8heYBDVYwz7qOFxWuQ0R6qXLy\nlCrul0qPiK+Dg4PsK0twghW0KKEAgqzX61mtVhtZ6eOzO05PT+3u3bvWarXso48+skajYdeuXbNa\nrZYZx9PTUzs8PLSvvvrKPvzwQ7t79+7I1geUl/sY44S/LjMYDLLz4V4Uica8mWWkpRJfatC8PMvl\nR4dL7uzs2K1bt+yvf/1rBnQ5Uuzw8NDu379vH330kf31r3+17e1tOzw8HInm4nKqI6bzjbfg8Orj\n8fGx7e3tZV+MTIEXrgcDLZ0TKINumfTaNkVI8X1Nh9VMFe96VH6vbApyPb0Vpc/Tcbrax8SXHmTv\n6YAoAiFVDx0LiAAD+YWoLyzIsIMUnfUwlalchqjOiYDuuPfUmYue95w6de7NbITo4ogK3hKGecfR\nXqzjUzovapuUTfK2emu5VWcraa46t1Q6/+U9fs77/2k5uKm2GOdehMdSeWkbKe4zs1AHl0qlc+QU\njyXgNX6WI37UPmh62GxeyAJ+5K+DQq9jTOBM0r29vewjOYPB4FykmBI9KBeTN9iy6c0DtTseOXJ2\ndnaOXEX7eQRCNDc5f5U8R5nfkbKdWjceH/xhB5QHxAKIh6OjI9vf37fd3V1rt9vZIifyQDQgnul2\nu3Z4eDhif0ul0jn9wuMtwid8NpmSlrzIhfJ7X3mN9AmPDx3TqpOifuX/+bc3blJpeI7z8/q3Vwdv\n3Gp/azqtD/9wlJ1Xdm+Rg+vgYWP9P7JvisEx/jAumcQH9mScziS42pJU+6fue+XUe6m57r2P6xvl\nn5Ip8fU1CgYbVv74i3zPg7DBxv8poFVkYqQAyiQJK7Vo9c8zPqokcb1cLmcHwgPEYLVY8zCzzBke\nDofZOSEApfqVF1beyHt1ddVarZatrKyMjLmTkxPb2dmxzz77zD744AO7ffv2OeWH+mv5eUsUooZe\nFOIrb9xGxFceIYHrIL4+/PDD7DPkCwsL1mq1sme73a7duXPHPvjgA/vTn/5kW1tbdnh4OJKPB1LY\n6YpACq8mYhziC1+pNvGMNTsJfI+/bKTPazuzgffmFsrF0Vf43yPX8vRWiizy6u3lH+lH7zoTivwD\nEpsd5Gh7Iztg0XYHDwCyTsAzGAe87RHvrdfrmWODqJWpTOXrlJTTo/eL3IuuefcihyblMPEWYd4G\nhnnFZ/dFEdyRPvHqFl1XklvL7BEz0SLNk8hFdIY6ec9SUnaNiS88A8G16JwmCNtFJShg45Af+pUX\nN/nMMEQRYUEMkR3VajXDE8Ph0Pr9vu3t7dnOzo49fPgws/nAcIwlmdBiO97v97MzRHXBVbdLDYfD\nDEsy/oD90TECO+9FiXMe/Dfj64gQSM0XzkvnjdaL7SfbZH0Hn9l2fHxsg8HAut2u1Wq1bIfP3Nxc\nNk4Qrffw4cORvhkMBiNzlqO2vKMIOFIbY48JSh4zyIP7B+2OMwk9csfDlEzIcZtw/6g+8vox+l2E\nHInsBerFkVbatzweNdpdo+f4Oa2TYm6PPGMsGekHr75aH8/35vHJ/afjFsQsE7R8bI2WN2r3vHJG\nz+Tdi2xHXl7j2JyJIb5ehJVngB8mvZhketYC50udKk/p6f3U7xdJFAixAsP/ZufJQe/60dGR7e7u\nJg+TVuNiZiNb15g0U4ERe/Dggf3xj3+0fr9v29vbtr6+bp1Ox46Ojmxvb88+/fRT+8tf/mKffvqp\n7e7u5o5FBc7D4dB6vZ7du3fPut1u4bacJFHDenp6moFGSBGwzPkdHx/b559/bmaPyKePPvrI1tbW\nsq/5fP755/bJJ5/Y+++/n50Bp3l5RgB/e8ZZ567ZozOfsMrIBhDvUnCJOiI/j5wxG/1UsooSXhjP\nuAfAAdDCZdL5pMYZ6dWQRySRJ9x+euYegyjNTyO6orZBuwF8IBJYI7402iD6ulc01hQYMCjDCm25\nXM6ivxAtgDP82LmZylQuW3Se47fqkbx7KQcrcpii/3X1Xh0njsLAD/QWSC+N5vXmbqSnvHspbMXO\nThR9wDrKe0+Ur9dukdPhtWXeO7z3sX4v0jZF6xM9q22kGI/vqU5XvY+ya5S1EpBqn1nYnnHZsEiB\nqKHhcGj7+/uZT9Hv963X62XRRQcHB7a7u2vb29u2t7dn+/v72VjlqCAuK+wyiBbkCfKLv/7tRY4w\nCYL/lZxFfZSg0D5VXILfeeOqqCOtZdJoL34Wtpsj8tAOsKN4BlFWu7u7WUT1YDDIvuiKLaq7u7u2\nu7tre3t77qIqiC/vLLezs7MR3MBkGZPwIL846ovxGtoTY9fDcEr+KCmraTxix9MJKSIkpV+ivkY9\nuMwor5KEXpQX11EJLSX3IhsSlT1Vv6itcE/1u+o/nns8DkDCQt9gwZUJMH2vZ/uivitqc/Va6p6X\nl5cmShfJxBBfzwMx9KTC5zwo8fU8CDtWLGqQ2XBHA9J79kUQjbbwHE+z8/VX5QVnc3d3146OjnLf\nqw4BlFd0wGNsVAAAIABJREFUCDUrvL29Pfu///s/29nZsXv37tnVq1ftypUrdnh4aA8ePLCPPvrI\nbt++bTs7OyOGkN+nbWD2+KwJHPT+IhJfEbCKzvgqsoLOxuvevXu2t7eX9cNLL71kMzMzdnx8bLdu\n3bLPP//c7t+/n30t0wN8mjdEjbYCTgbl+/v7GfGl5eTfWoaI5MH78ggTb55o/mhXBi9eGnUaijiJ\nDGjUaDO4UEeRdaLnBKXahp/B6jCc5NTZXnlnfUX6KAJmIL44UgDEG7ZQI+KrKKCYylSeRC4KkD1n\nKbrugXO9780XL8ILf/M5Ouxo6scqFFtF+CHVPtFc9OrF5L/iuKLvjmwNv0d1r5Y3cmSiPNXZ/Lrw\no4fTPF2L+1xOtlWMo3l88Lk7qtsZO+s7tU047ezsbHbgPL4Sjq9OHxwcZFvpDw4OrNvt2u7ubvZF\nanaWQYp4pAqfU9bv9zMyx4sa4rbx/AEQB5540V6RHVcbp9eie146iNp3HbucB9qey43r2EbGY+Dg\n4MBKpUdRWP1+Pztvqd/vZ33FpCLaFe3I5KTu8oA+mp2dzdoX+B99CeJLtzvCB43woacz+SeKjON2\n53x5nER62+ubPN3n+aCqw0ul0eguxsaRzvfGhI5TTac/ikujMahzXtMz1vXKp33IkZdHR0cujlS8\nqW2hZcmzqVF/FO3rce19lGdKJob4qtVqz7oIFxYMRIS5Yh9+pPyfpWAymD0eSOqweSGaquTw3NcJ\nWr4O8cBQ0XRmo+10fHxs+/v7hYgvrwzjgGVsaez3+/bpp59ao9HIjOD+/r71er2xDrNWIIjD7bEV\nb9Ilz8ACZOq20CLEFz8/HD5aRb1z505GfuE6yKhxxwfKwIBHQQcbCRCXqA+vVHtkFP/menO7RaBJ\n6w/xALKCZ438ygOyqfsK8FhQZnUANH/OJyK9omucZjgcjnzFCwSYAhPVu0Uiv7g9GXCaWXY+ia72\nA7Q3m00bDocZSJ7KVC5bdCEw0h8pEByB9NS9yGFRHajOhZLxZo/xEW9v5C2OEaHCEummosJ6k9so\npTtUBzN+854ZVwdpvRQT6TPR8/qsh0Uje6X1iXQ07FlqkUExIGPlKMo3JfxOHjNIxwQFj02196XS\n46MUut1uli+ILXy8iG2BRu14BMBwOMyihUB66cKWRqKg7mp3ivoFnr32ZJx73njBdR0f3Aaol57f\nhj4A6cRp+WvJyOvg4CA7d4lxBggt/s1jU6OSdGER5eCPZvCzIOa9qL5IX6iu9HBdFC0VSZFnvOcv\n+mw01rS8Wq9xy6kS6XT8rfrD0zGeLswTtlH6Gz6f7jLg/EGQqh6Ioh9TNpXve9f5d3QtlRffH6ef\nJob4ajabz7oIYwsry3K5bK1WKwtvfV4OtGfhyajXzdJOoyrOaEJPshQBrF4aM3/1g8/4GrccvM3Q\nE71+dnZmh4eH1u12zxkyTwkjjwhsav4wpuOSNJMmqDdWPzVCionjaGyooj85ObHd3V17+PChO4fG\nITm5HAxYFcTxexC9hsNUGfjrGEg5IGbnt6R741MNu7d6piBUy8J/KyGm7/fmoJZbhR0BLx3rgdTf\n3IfRj9njz9cXOdBe8/UAkrYnfmNM4Dm0HaK+Tk5OsrPGULZKpZJtx550HT6V51sUhEcEVupe5MDo\nvdR1b85wpIBe0/lYLpcz4gt4z5u73kKJYgvVedEcLIJJ+JnUAl7KGfPyyvvJq1tUfs/+RPXJq4N3\n3bvGuMrDex4phv+jflWyKmojpOcoDIwzHl+eHvdIjv39/XPtyZFCECUuWHisMzGjWMVzYD2s6PUj\nX8ePpotwh2IEve5hBm8Mevd4vpuN7nZAHw+Hw3NjhvNTnYKPBCi5hXSMPfhenr7zInUgGCu6HbEI\nWeD1xbhEgzfXiowRxYL8/ij/vHLpOIra1dMT+D8aO3y/6I+HGaN8UmXL0508f4+Pj0cWWUHQcnl4\njrMO8DC+52to26bGUNHx5UUK5qXxZGKIr3a7HSraaOLkPRfdK/pc0XdhNaBer2crgM9jtJeZDxLY\neOOa/vaUSeQ0TqKkwJdeV8XOwu0BYHIR4qsIGaJj2OzxaptHsOjYzVMkRZ55UUSVNEKHNUrOM1xR\nPprOAy3jzBsGSQDMZqOgx3u3RnxhxTDVr6m6MViJ6qDgQUGppvPABwuTX0XKnQIzXn24XiCpGCgw\nYMgLJfdILH1GD55OgaXUGOF2hU5WJwntB2cJEV9YDYTzznlyv3Ebfh329KJ5TOX5F4wz7reUQ54C\n1BGojpw+Pc+E8/IcRjgS6nRi/nLEFxwMnb/eHE4trkWOkHePHWcvj5QO8ZxApGFdqM945YjwZKpu\nUb14QWbc9oiup35Ut3Me2n6M9xkH8PhhG6Lklra71pn7E/mo7cT787BnJHn2BPmrPdP+0DZgbFJ0\nXHvjySvruH3PZeN21HeqLtC68eIz8IfOp6gurCu4XOinvHHplYfT8za+lC7kMe7d8/7md3ttrfMh\nGlM6n9Rn9PpDf6dk3PHk4W9uV/Q1+lnnOMpVZIFDdQzrGe8+34vaPGpr/h++CwIVqtVqxk/wR5Vm\nZmbORXRGW1lxL7LT0b3UuNT7fC11ryjWmxji63k6C2scGQ6H2Qo6AFAR4/IsxFOCqmBSDgbf5/Qa\nPfE81r2IpIxs6u8IRIJw4KihccqSJ+M4fNpveYQXygAlqecNPK9j/EkFdfSILzZieNZLr/c8Qx+l\nKyoMJNRg8n3kzZ+3LvourUMRw5MCpKpzUmDHa6c88FZUGIBhGyCXXYFK9H8egcVjxdvO6JFkERGm\noInroqILGGY2ctA95jOAEPIZB1hMZSoXEU9XeQ6odx3C1xVvwNnUdOy84h1Iy/ZMn+H0uMbEMbar\n6byOHBfON7qekmh+an29d+Q5k3nvv6iOSL03emeqLEXxWV6Z1GHWe167aRomSHWrkPe8Rn5ENu+i\nfTVu30TYNWqT1LvzpEg/5eWteaT+5+e9fJmw1EX8FGk8Tv2LzCmvDvxext3e+PL6JhpTnAZ/FyWa\nUvnlvVsJyLz28PR6kX7w3uERbvqs4izGW169U1jPy1PnPO6niNSob7Vuni+OqC9E+ONdlUrF9dW9\n9tVx5rVhZG+5bBEnwOStjh3dJaJ1zZOJIb4mGXDzwC8SqfMshY1tBJaKGFwe8FF49CTKk4BSFWyX\nG+dsrYsIExzM4HskZVEDh/7FaiX6WfN7ESUivszsnKHylLbmVZRkvEg5OaLHM/pK8LBTF40F1Q8e\nATZOmbVcCm5SxhfXvfLptSLCxK3Z4y28Zo/JKXZSOIoD4pFeWha+j7wjAi0ixTzQlKoXiwIHrhM+\ndw7iSwH2VKbydUmkY7y/vbTj5Bdd83SdOpvqLPCZfakvsXrOS54DmEdu5dU7lX6cfD29rQ645+zg\nd2QXIz2fdy+SyGkrmpdXZi+vqN2ZPNG24XRqC7y2NfPtmzc2ozJeZJxE4zLKIyrHJImOZ8VGik34\nN+fhXY/e5z3Li5dePrqNFkEinEajk55ELqJDWFI+xtMqY5Ey4D15OsvDSd7vCCt7cycisPi5ItGR\nKUF5vCh/EF/AefgABmPPi9igpyU6F/Js/ri6ZmKIr0kVdXKeZzLAY5gxUSJ2G/9j4qe2N06yIcxj\n1vVZM3+CqqIcJ8JGy8GSMrz8k9oOVpTM07HBhAm2S3lfsHoRRI1HdLYUxANGnlPFz2ha/rvIGFTS\nZZwDo4uMRwAsD/h5jpzWyUvLwEHnSMoJ8vKOHB19RypfEJoABwCUXjrWm/o5cUgq6ssjuqIor6LR\nIqn2AhDmhQhEfPFqIL5IVyo9+vokf8FrKlN51nIZWKIIGcDzSKMsQHjx9kYvKlR/mxWL/sBzeYRD\nqj6QiMRQpzBPIgLsIs7xuGQUl+FJJCpvEQdz3Hfou7xxxTYB1zQCosh4iRY6ozJE5VXSziMKis7H\ncdszr66X4VOl2sp7pkhZvPbm/mFygt+JPvewPI+FCDNoGaJx+DQkz2dIpYvasagO8vxOD0tq3jqm\nVSJ96uFA7zf+zsPCHm7Ou56S1HhTnIcIfz1qI4og1PyfB4nsoidT4usShUmvyGl63iRSlqktUzoZ\nIuUwLsnzvEnKofeUajQRx1XqURlUPDCjq4tKfDHQjBRs9B5d6TZ7THxNOumVN66LEl+a1gMeEejU\n/ogMLadhZwrReAye85yiccBQ0XGcMvh6PeXQeAYY7eSBHpUi9RoOhxkJZHae+PLqwMCzaHSHAtTo\ni28e8YX3p8LguT7a56XS+Q8KcJ789Sc8h+2PU5nKs5CIpHlWooQAkxV8rIWehcTzNlrR1/fw3+wE\np8oFUac5el6d+CJ2gt+hDqZijygfzTPVDtEYKIKzvDSp+o2D1fGcp1Mj8UgMLwoX+l7Pa/PwZpR/\ndD0V/c+/lRjwotbGkVQ/e5J69jL9qah+4+J3zkfnB0eRMTbn93rjXvF7hD+88l6kz8apL6QIvvPu\np+ZtlHdq/uf5Mt6Y9iTVJ/xOzw+M8C73nedT60+qDbSsLLzzg303/sInMKjaq0jX87sYj3tt4pUp\nbzxoHYuMvaL6YEp8XaJ4Ds7zLpEj5U0AJjeULVcFwQZ0EsUDqp5yU0kpUnZ4i0rR9lNQHoXXa/m5\nHiBS+Bkth6cEX4QtrWbpyKii4zkFPL1+YdH5l4oMyHOgdKxCohWdPKdC65RqC68NiogaTB1/aBOQ\ne57+0bxSdVCH4OzsLPt0OwOHvG0DqitSER/ej6ZTPeE9q+VJtTGDcC4zwBBvTT87O5uYRZupvJhS\nRAcVTcfO1Tg6jtNDVG/zIidvc/R0gOoIljybEzl0Spxo/Vi3FXGAWafzby8vfafmr3/jd1R3r4/G\nFbVrUTmfVK95uEqJjjw7n+fsFi2jlkXrrLiD7YbXdx420DRPow2fZynSFql0modi89T7eNzm4c3U\nnPZIliL+S0qepN9TJEmqjqn7PC7H6Z/oDD4PV6XKxb/5eh4uj+a69pd3bxz/Q8efEl9cb4+z8Gzn\npMqU+LpkmSTiK0XoRCDSrPjKSBRKOgmSUmAXdQrL5fLYWwK9dvb+9wysAjCvDhirrOA8cBopvjww\nPWniGQ4PLKbSaXruh7yzBXTceWPFG3tqvD3DPC6Qi0TndlFnJQIJmnfKMWTdpMBnXKcJ9QAJdHZ2\nlkV8DYfDkKTS+uC9XmQHk5fqEKd+OB/dPsWizqo6m9oevOKs2x0BiNRhf5Hm91SeL4n005Pm55Eg\nefc8fRw5EpgbIL2KfMm1CKmh85edYJ2P+FvnvW6h4rJr3p6+8Mri6ZPIydfnPade6+C1/5M4XVF9\nnhYm1/qpFLGFnr0vkjZqb68/IwIur8+4HKkxwvml/o7Gj76jaP/kESJPU5cUvQ7x2tmLhtTI/HHK\nHLWrh7GKlFnTeHPby3ecMo+r64vqZq/OKZzO5zR6uDmF5VP+YHRP/4+IyDycyXXz9CfK6ZWbiS/v\nfF9dVNX8I8mbh1wmr788Wzzue4rIlPi6RIkM2fMsOggxSXRycgg20mFrlSooVi6T7DR5EzQih4rk\nMTMzY7VabewvluaBUi+6ywtT5zKxglMj7NWd36PkwySQvONICsimSBm9HvWFR3xxW/Lc0y3HXpn0\nmkYoeaBBD1AfV3S8MbGjTl70jgio6fzKA0xFHNuo/NBfAAPY8mdmrgPr9YUCkcgGcFtwtEiK+OKI\nEp2zTDxGEZ5eWXgxQut+fHycfaXOC32fylSepTyr8eg5gDo/dS5z9BfKzr+Rb5F3e8JkmM7viOxI\n1Sf1bo+oGddZz6vPkz47rnB/jNsPEWGlDmykQ5UEUdyV50Ok8J7e1/GgfefZjVTdNU3etSgvttce\nXsnDJWz3tT/UeR8H40Q2vMic8fpU+wLv4Ehzr96pd+lYGxfDpdqZy63XPUzztGUcHKfXU+XzfFNN\ny1sDVbxx7eXP+UU6wntGt65G/aDvUkyq6dhOKPnF9cBYUh3yIsiU+Lok8YDBRZRRKv/LEE+5FzEa\nDOZUSTCjPukRX0WvF5HZ2VlrNps2Ozv+NCwCULXd+TwudXY9QMaRX5q3KknO60UhvXTeFgGEeC66\nzsZWw6sVHPFKPZMfkWHnuarAV50TfhcI2Gq1OtbWWy8vbi8PgOXpQs/AM6CN9JH3nrw54uk6yNnZ\n6JdvzMwlpby6em2iRDK/P9riqD/qUPO7eNVO57jXPzzn2TnQEHg+x06d3KlM5XkQzzF60vyK5qVz\nPIr00rldNN+8Z1SfwKmJIkFVt6oOz7NdLBEJUOT8J9y7CCb28EikxyMnPhozUZ3U7kTv8ogOb6FE\nbbHmmTpLy3uHtn+UXvtH68uYUSN/2U6kyBVvPHkkQZROBYt93njP85FSpIKmT40bvs72mM9RRfvx\n2PLGX1EcmapPKl/vPZz+SRemdexEv/PKXaS98VyROc/4UPOM/k7NCy23V49x+tHDqt6c8nBg6l26\nk0q3vJtZRmCxXVCsx/l4UWgpeRZ48Ens/pT4uiR5WkrGrLiCeFLhSZcyat6Ag8OMA5BBjKgxnWTi\nyws5zesPbStus2q1au1226rV6tjlyVO2CoAAZPQweq4HjLmW3wPXGu1VLpczcPSiRHwpKREBUogH\nlPW61ye6FRXpYKjQJ3kAg8vBBo37CMJ1mJmZsWazafV63SW+IsDvOQ1M0HnbevR5zi8lEajBNV6Z\n4rKME2XKzynxpRFxClLUqUVf8vlY3I483zS9th8TXkySo5/R1ylnB+/kdw2Hw5F6YExiLnO+Zo8d\nuEnV4VOZytMSdpTMzhNfKTI70tvRolT0fv2b57Gm1fKmyBBO4zlbeU4q27kIp6QcRq6PZ2Py2qPo\ndXaWPfuW9y4P05nZuXFRKpXOjYuUcBsyjs774fGj6ZGv2gi2zZwGtu/o6CiLegYOUXJXyx2NG34m\nVW9PPJujpEEkitk5ksbMMp8F7+Z3eYQI22QmvLjPzfyFP66nzrWIMEoRSZwfY0rYaS27d0xCaqzn\nSZ6/eNE8Ua7ouueXXIQA8XA58kthVsX2ReqUytOzF94CJ8rIeFLL7YnWC+MCWC/vnK9x2veiffF1\nypT4ukRRB6moeIaDjRRHfTxtgiE1MfG3p4i5jF5dzs4enZczqV8Gi/owMhrRxOe2mp2dtXa7bZVK\nZayysKFV4KYKUg+rPjk5cZW1kl6s7BSI8jYo5Fcul61er1uj0bBqtVpoVft5FTZuMzMzVq1Ws/YA\nAbG4uGidTscajUaWbjgcZp8H5mseQGUi0iMjzR6v0nC5YLSiaDx+pxIrfI8F47DRaIx95lzUbgyy\nlHCJiCCUz8vXA0EKfDQ/1pmR4xaBqNPTUxsMBnZ0dGQnJyfu1iVuVy2/gil8Nccjz73ILi96BH2v\nPxzRmed4eGMIq9Zmj8ecrvZ7kYNTmcrXJSn9UDR9XtrUM3np1VnxiHFP73l228N2KR2m6dluq973\n6pCnW/Pq7ZVBCRZ+NsqbdXbqvWr3imCt1L0IQ3u6NGpP72/8r4QontH2idKyPecoXq+8nn1Q7KH4\nkevE6TTql+2SEifatpEfkeevRPY4qrMX3cLvYb+Ff8NW8nOMT2ZnZ+3s7MxarVa2KFipVEYi37x0\n2p5e+RUjaNm5Dzyc541J1T/aJlHUeLSYH+EmHTOenuM6a39wHYqKN1fz5m2qL7Q8Snrxc5hzXjr1\nj/CeSM966RX7efMxKqM3nrQdIBqRiHueL1IE/49jj6P2eJYyJb4uSTxnL090AumK2dNg6IuWO+8a\n3zM7/7llCMoPsmRSiS+z0cmuRj9SBBFYPTs7s0qlYnNzc09EfOl1BTlKfqWIL64LjC4U7czMTHaf\nCbRSqZRFDC0sLNjCwsKFItieR4Exmp2dtVqtlv1UKhVbWVmxjY0N63Q6Vq/XRwApQBVfS/VLRHyh\nDyCe8VWDzu/T8wGicTM7O2utVisjvjjfcUVBGEerefpQ68KAK+W0Re2Ae5y+qCPFaU5PT63f79vR\n0dFIJGMqks0DQBxFBb2hAFKdCW47EJFIr2MmikDUMcfCZeD2UdCtYfCXaXemMpVxRR2zp5HWu87z\nNZpXnn7Ii3rVfKLoVNVNZqNYsMic53eqw6bOWJ7k5a/Y1dMbXnolPVL63Uvr3fN0e56k7I7mrTof\nv5XwU0LCwwZePZn48siIqExqF7woYCVW+B5jRtyfnZ21arVq1Wo1O/OR2xhl9ogQvsdtHNUh6ntO\ni7IyTvWwg76f25rfw19jBSbv9/u2v79vBwcHdnBwYIPBYMSOM57RtvT6hf/2xo/2j5bbm19mjz+U\nhR8NVMAY9BbTtK9SfeM9q9hOiZMIh0Xvybvv3VN95o0/rQPSefhJ+1XTefmYWUYQR+/02saLBub+\nR75e1CPX3/vN6VXvMz5lTJnXfil5Epv8JDLue6fE1yXKRQaO2eg2JLOYVb9sUUXMk1sVHCYMG2oe\niC9KxJfWrShQVGMN4mthYcFqtdpYZQEwiYyrGtPIOda0+gwTL2dnZ3Z0dGT9fj8j7NbX121jY8Ou\nXr1qGxsbtrGxYe++++5YdZkkmZmZsUajYTMzM/bGG29Yu922Gzdu2HvvvWe3b9+2u3fv2ubmpnW7\nXTs8PLRyuWyVSsXtewakkcOD+0pIKBjmvBUweoALf0OU+IIx5Hy8/Pger4zhGSa+zHzgq8A0Ml78\nvDqiCsIi8JlnHLntTk9P7ejo6Bz49J71wJD2g+cIRaDRixCIyK7UnMa7+bfZ6OGmumDBYy0Kgf+6\n7M9U/vYkRWrofZ3nHsGRN989J9l7Bz+r84UdJC+Kgp9Vfa1zN0+X4LouiHDZdGHB01Xe9SJtFd3z\nnMAiujbPMY3ekUoTOaepsnj5F9VzOv6866zXta1S/czXsQDD/cwOskfy6PzAcx7Gw72TkxMbDAbZ\n9sZOp2Nzc3O2vLxsa2trtra2Zp1Ox9rtts3OzmZ10PrpWCtab20DLlukH7x+S+kNjvjCD0g+tGe1\nWrXl5WWr1+vW6XTsG9/4RobteAsovwP15whq7itcGw6Hrp7Quo/zTGoco28qlUr2o+eFenjEI1tS\nuCWaQ6qvomjBJxElQLXd8Uy0EKFlw7Pq23K9tJ083ZNqG10gMfMXQHQue+XOw9IpjOhF5+o4iMbh\nOL6wli1Vbm9e542XouNp4oivZ8EmqqQ62OusogMC6fU9XhjtZYj3DjYEfE2NmBJjCuxeFOKLQUYR\noxzJ2dmZzc7O2tzc3NgRUjDcXr94P3BiI4KF07LTCzAApVypVDIwcOXKFbt582b2c+3aNdvY2LB2\nuz1WXSZFAGqw9bFer9v6+rq9/PLL9uabb9qHH35oH3/8sX366ad2584d+/LLL63f72djnqPAPDIy\nEu4Xz1lKjbvIaOgcVOILh7lrObxyKbjn59BeKVAWlbmIjvUcX6/OeYaW/8Z9Jr4YAETAksuljoT+\naH4e4cWkIZwRzMtoTOg7+X++rn2hhCWPTd7q6NmGonKZditPngfMMJXxJW/MeA59NL8jQiylG/Lu\n63uiKK9I76VIL/wf6Rq+znM4pWM1jfejZeJ6F3E8uF6RQ+TVwWuDInZN68LlTaXx8vTawnuuiPOp\nzrYXaeMRVd7fyIMjf6M2jBzmqGyM75kQKpfL2bEVy8vLtrKyYuvr67a0tGSdTieLfOdyKOkbjTGu\nV9HxGrW3lxdf07bCNfzoPEV7IIKq1WpZq9Wyubk5W11dtW63a91u1/b397NIsMPDQyuVSiNEGOcJ\n4kDJLyYdvX5LETTeO3iMaf35GUS0RQuTee9moiyKcE31o7dwy23g6cmi4pF4XA7GXDoeNB2e43ZU\nAo/vebrca1O+rltOvTlaRA9GOqEoUaSYktuL9U6kEzmv6H+vrNEYiNJ5wu2Wp+chE0N85TmIX7dE\nDexNkIvkG4VXXrZ4ysJsVIFGX35TBw3XXhTiq1wuh5FWeCYCRh7YwNlKtVotO2cnUp6soOCQRqGv\nbNgj59gT3d7I27QajYa99tpr9vbbb9u3v/1te+2112xtbc2Wlpas3W5bvV63er1+oS9UTqKUy2Wr\n1Wq2urpqc3NzdvXqVXvnnXfs4cOHdufOHfvkk0/sj3/8o73//vu2tbVle3t7I5+zL9IfkBS54YE6\nCL8PYxNnsx0fH2f9zGd8pYBYXjmU5MKKYgoAe23AYzDVRt5cUyOoq1Wp/PgetmYjdN07FFbf6+lK\nzZ+3DDKw4HdopBcT1x4B6vVHpIMUdEQ/GlWGM8q8dEXk67RhKs8TbpjK1yNPs89T80Rtv36sogh+\nSwF/tReeE8XX1VnTvNRe6D11RDivcea7V8cn0QFRes/BxfUoXXSP9XHUJ3gudc0bG3y2kupfxnFR\neblsahu1DXhblNolPa8Ruh22bjgcZhFOnU7HlpeXbXFx0ebn563RaFi9XrdarZZtqfPGOY9BjwAo\n4shH16O+jCTvvmcPIfxhIGCkVqtla2trdnZ2Zr1ez/b29mxzc9M2NzftwYMHtr+/n5FfsOXe3OF7\nqS1seeXXSC5sRUV9vGdBfPGWSA+T4P2ef4Ny6XmkTJJwGg9/peaYtkGRdNFzOga9hUlOowuSql+9\n8uXpVa9doRc8naB5eGSR9k1U76gvtA08e1ZkTk4ivpoYL/Xw8PBZF6GwlMvl7IDvixB23oR9VpJS\nYBr+yJNHJ+VwOMyiByZRIkCpQBHP5jnYw+Hjr+nhoPRut2uDwcB9ngUkYt6e7wiIpQRAaHZ21prN\npi0tLdnq6qqtr6/bd77zHfvhD39or7zyir300kvWarXG3qb5ogjGQrPZzNoJUUIvv/yyXb9+3dbW\n1mx9fd1u3bplt2/ftocPH1q327WDg4PcCLxIUv3IQNoziCg3A+jhcGjVatUWFxet1WqdA/3ee/MM\nJYNFgGN11iB5ICbSfV79omspkKDlwDNwBji9dyBsBCS4Dl6fMRBi8gvOkZmNkE5FCC/85r+9tlUQ\nxdd/An7CAAAgAElEQVQ98kvH6mAwyNU/U5nKRYS3DpnFtisa5/jfi4b27ul1zVcjc5ko4K/melu8\nItE6pfQci+dU6f1If0f5cHk433GIHc4z0lEpm1IU30bP6jv0uej/VNm0Ll50Tmps8pjB+OAtZtpe\n3O467vTMIK+N9Z3R2I2i/1G2er1u8/Pz1ul0suguLGwquZFa1IpIhyK+Td61FIbw+nBc0oXbi0k9\njdprt9vZgmG73bbt7W178OCBHRwcZOeDIh+16bx9rlQa3UmidWMiiccbxmPqowOclgkv3ubI88Aj\nfr3299JopCuP69R8fFrC+UYYMLrPdfN+lNDj51Oi2Fej5Tz/Wcvs5fk0RXUF6wj9OFqkdzw8modF\nU3Zdrxe9V1Qmhvja3d0dq2KXLZGzY/b4LCCzR/vEi0bBeMb46ya9eDCb+ZFnDBy9yYtneWVpOJxs\n4osNSkRYjDs+K5WKNZtNW1tbs6tXr9pnn312jvjy8uToudQ7x1UGyPvs7Cw7w+v//b//Z++++659\n73vfs/X1dZufn88ON/26x+bzLogCw5aAV1991X7xi1/Yhx9+aB988IH9/ve/t//93/+1Dz/8MCNV\nxpGU4sd9NlaRk6Ch1djO0G63C5FeXll4zsOo8zhhQ+8BIeTh1S1yaLSMKTA7rmhUpa4CeiCf9WYk\nHtnFB9nj7yjSy6v3OPPcc2y1fz1nCrrm9PTUDg4OrNfrjUXcTnXFVIqIZ//GAcgeqE4RFfg7IpbZ\nCcb4xwIHyF+O3ojKGOULUWLJq1MkeU5m6m8tl743j3DH+/WdXnRqpN9TOp+fSZFemk4dSC+dF82m\nbaQOIdeBr2te/EXE4XCYkQ7YIggdr2MA19TRxHu1TVk/e2dAwobxcycnJ1nUNz7cs7i4aCsrK7ay\nspJFeMF+87Y4z/ZFdt27xn2U5+NEjv+44o0R7Wcuq7Y5k09oUxBh2Aq5sbFh3W7XHjx4YF999ZVt\nbm7a1tbWyFfPgY+888W4f9lv0ig/jRhXUgZ5cD3MHh/czx8nQH9ynqn+0DY0G8UzUX97aaN5y/1V\ndL4X1Y/4m3WEp3O1Hhz1pSRkVHbNJ/XDZUlhqkiXX0Q8/KdY7/j42I6OjrLz/vjDaJ4t0zIxmVvE\nfnv22ruXsvlFZWKIrzwn/+uWFPEVTbSL5PsshCefrk5AvEkLBa8KhQ3GpEYJwNCxROPRU6yeAEws\nLS3Z2tqa3bt3r1D+DMCj9rzIXCmXy7axsWE3btyw1157zW7evGnf+9737LXXXrOrV69as9kMt7lO\nZXR1h1dPK5WKdTod29jYsNdee83+53/+xz799FP74osvstXBcd/hCRshBlIQBm88x+v1erZls4jh\nTTkKKCPaAKS/AmStE9IrcOa8FeREz+J/TZNHrDFYhL5Cu0XlVrCoIFW3B+r70C569ksU6aXtr/X1\nwIMH0Pj5qC/V8cN1dqqKylRnTKWIFAHIedf5vvd8NEcu+mN23skpUjdIyulTnBU5TeoI47o6yuOI\nptHys07nZ6K+YUnde5q6Ylzs7bVTauywfuZ0rL+BHavV6gjxxThOI3X4i7pIz4535HAqCYZrILvg\nR2HRdX5+3hYWFmx5edk6nY4tLCxYs9nMtjNqJFERJ17HrrapN7aj6xFWGJekST3H80iv4Tr6kecY\n+0Wzs7M2Pz9vtVot+8L54uJiFgV2eHho/X5/ZEGS84lwVxTBqWXxIr60/b3D7ZnUwfu87Y/6N5df\nI870eU3n1cXTLam+8vorwjrcltGipTdmmXSM6uKNYc1X8yuqiyNM673Xaz/u17x5hDwivZ2HE1Uv\n6n29l2fXU3mlyldUJob4MvMV1LMqQ971PBD0PEpk4HniQ6DomCWGcvUcJQ6XnERhkJdyPFnyxilW\nYRYXF211dbXwtkEGTOgbvqflKjIGUZabN2/aP/zDP9h7771n3//+963Vao19+P5UHgkMHb56+eab\nb9qPfvQje//99+3f/u3f7De/+Y3dvXs3+0R2nngGVu/xamI0DviwfTPLziprt9sjqzpmacOWcjwB\nsjjaVcPo84y1t1Vcx7qmUcDFQEGdk6ht2fEYDkcPwPWcHAURvJqvP56d4FVTs9FIgnGAhoK/ImPK\ny0//hv72traMO26nMpWU8OJaniPjSak0GmmuktIl3vVxxnjKYef3pEC65zBFjpVGGbHe5HZgnRi9\nT8vI70rVmZ+L8OM4OuIiuiKvbvpb9bDWV/Wb95Oqm6fzOeKrWq2OnP10fHx8LoIeOvf4+PjcFkP0\nLdtGpGXiC/dAfA0Gg0yPt9ttW15eto2NDVtbW8uOOgAZEpEp3E6pBSEmX/R31C9ef170nl6L7BXn\npfPIw9WMX9jvQbR/o9GwTqeTHXPx5Zdf2p07d2xzc9POzh59GZ3bCH2DckXHKLBO4/bXqHE+sJ4X\nPeGbYRxiDPJCnadX2L/gMqOdtAza7ynijn9rWfm3ZwMU+6XshJa/CJkY6V5uGy2bYk9uA42M07J5\n5Y3qzGWN0qiui+rkvUPr621f9d7FeaV0fgqHF31G213rkScTRXxNkqQG2vMskZOmBJjnmA2Hw5HQ\nanaa1BhPmozTj+PUsVQq2eLioq2vr499XhaAER9SGSlvj0Qwe7Tvv9Pp2Kuvvmrvvfee/eAHP7C3\n3nrLrly5kkV4TeXJBeB3ZWXF3nrrLWu1Wnbjxg37wx/+YO+//77dunXLdnd3k+lVn3hz1Aujh0Rz\nEGd8NRqNQlGECuyhF3h7Bp8nwSBZ66R5q3GNQJCXj4Igvq/kWJG6aaQrA18P9HuEE+tHBT7Ih8/a\nQB9GW4w070gipyxyGqL0+t5Js2dTmWzRsRYRVHzPm+ceaPfSaDrci/SK6oInmR8K4vkn0umpsniR\nG9781/rh73EIHtZXigtTbfI02irKS51j7x6nixxXrqsnujCMd/KCAbb+1+v17PgT4GhPrzNm9nAb\n95naDAgWb46Ojuzk5MQqlYrNzc1l2xpBeM3NzY1EosEmqd327J464tF17Z/oepS2aLoUxvDKkHo2\nGjdKFOG6fgyn0+lkJOPi4qJtbm7a9va27e3tWa/Xy/oK7a5fUOa2122vHI0HnMXHJTCGA25B1CHG\nn0dm6rlf3kKEzm3va5KcrxJp3LZ4niOrihAj/JxHgnh9mhqTOu8Z56me4/ZI5a/5Mf6L/GzdMZWq\nS1R3r9ya3vtf70XthLp75dC8Ixsc4XHVuak2eBKZSOLraVX+siRSzM+7eKAF13UC8RZILw8oau/8\ngUkUVQQ6KYso60iwDQ7nwhUVEF+6IueVGcqQI+7a7bYtLS3ZjRs37Mc//rH94z/+o928edPW1tYm\nbuw+7wKAsLi4mEX4fetb38rO1pqdnbVbt27ZwcGBHR0dnduimNcfmGu8tYLHqweksUo5NzdnCwsL\nVq1Wk9FnHhnCc5rPDWHiK3Ioijgmebo0Mpqewc4zpEWJJe89KScRdWGHAtd4pZQdnjzSa1yJAEyR\ncVXk/VN9MZWnITpPn9a4UqIj5RBomhTp5Dk53nN5onpwHAefnUclJqBXPKLey9cjv3Tu83MeQZZy\n3NRhivDURdqQ68RlKppP1H+Mi7UeUX3ZNirxBZIJ+YAcU+c6Os5C243T8d8gvc7OHn1BvNVq2dLS\nkl25csVWV1dtZWUlI+LYOfcid5QMKNJu3lj20nj9kJeOn/PuRe/y5ryXT57ofOCF/uHw0cJfo9Gw\narVq8/Pz1mq1rN1uZ5FWZpZF4JmZ28eIlleSgHEDMJaexcblw1ZM3mrLHx3ivteIfO5vj7jSaC9N\nyz9nZ2cjBJe2s2671PZIkSuejvLyiMaptgX/1nspHBm1qRc55Ym2X1QfL5/UvNF0+nzUFnk2TXE6\nX/fK7aUtks6zxU+ChyeS+JoEiZTz8yzqeJnZCLnF15it1kHIoMA7o2bSJVJO4wiTgsvLy3b9+nVr\ntVq57+U2PDk5sX6/P3KAOJ5TBcxnSuCZt99+237yk5/YO++8Y6+//rp94xvfsHa7faH6TGU8aTQa\ntra2Zj/72c/slVdese985zv2u9/9zn7729/a3bt3bW9vz8xspP+8MGn8z1GWAEQ87/Tg2+FwaM1m\n027evGk3b960er0+AuAgOneV8NJtzfx+ACwAnrywdxWeXwAcRYlztBuvzvL/niOsdY5W31j3efWI\nwAD60eyxXmUirGikF78vT6cqaEmlierpgcpJsmtTmUxhoOsBYu9eRNB46VQix4rLUbTc+J1yGoq8\nx7vHdp3Lrdc5D402QLqIcEq1Bb8TknLkijhQ3rvGua79670/ErUFuqUw0oFMdkCvo+0YSyO6Bud+\nMtmk5Bi/C+dyYRcF7AX6V4kxvPvk5MR6vV6G4+fn521+ft7W1tZsdXXVlpaWrNVqWb1ed7fLeXYL\n7aJjVAlVbUvvnnc9r6+K3POu8/1orPM4iModXdforFLpceT70dFRFmW1trZmc3NzNjc3Z51Ox27f\nvm07Ozu2u7s7EiWPPi6ib5TMYiyBH0SA4QuUzWbT6vV6tiDJfcIETUROcdth3PBXIrUtGfMhXyVV\nU4RVdM/T85E+0zKrDvUILk3nYSjueyYCI52nz3p1i/JhnR3Vj+vIusQbo17dojnkpYPwWNG+Kdq/\neeNcJUWOFZUp8XWJUtTQPy/Czhf+58njAS1Nj98MGKJw7UkUNv7cJipF+hxtMz8/b1euXLGlpSVr\nNpvW7/fdFSD+DYAzGAys0WhYpVI59xwrLTMbMcLXrl2zX/ziF/azn/3Mvvvd79ra2lrhck/lyQXE\nUKvVstXVVVtYWLBOp2ONRsP+/Oc/28cff2y7u7t2cHAQGlEWNS46Tnm1Dc/WajW7fv26ffOb3xwh\nyjyJyBie6yBV8aUogDEzcw05G3Rv3HlGkcmrSBRgKxjPEyWuPP1VJJ+ofpi7fJ2jZPMIr8i55/wi\nYq7INX0Hl2OqH6Zy2aJjOAWO+Z4+p/c4f+9enk6N5r+nl4viPm8+enl6pFc0l7Vsw+HwnFPr1U+v\neXVX5w9pUvrKK2PUDkXbLK+8eelT9zwyRxd7IYqPFQMznoZdbDQa2eHxiPrhMxSRHhH9IFLUhrIo\n2WZm2btWVlZsdXXV1tbWrNPpZGd5MVmnZ4hx23pb9LWttG2jckYYJm/e6Fzwrkf96Y0tTXdRB1zn\ngOfnzMzMjJzthr+bzaZVq1Xb39+3w8PDkY/pQPgMMCZFEMUFHInti2aP8QqeRaRhs9nMPlzAZzHj\nfanD6XVuI28mvXRrtdaF07HomYzcx9GuIm8cqA7y+jf1w2lSz/A7L5Jey875eWXle/pu/M1EZdEx\n7+WbVwcVxcp6z3tHiszMm4PRM2iDohzDlPi6JBlXiT4PAodMnWSOAPOMLw82LwqEjfEkC+qsAAf3\nxhW0U71ezw4aXV1dtbt372aHYHLeqiRwWObR0dHIig+Xk89fwiHmv/rVr+yf/umf7MaNG3b16tWx\nt1hO5elKvV63V1991Tqdjt28edN++9vf2m9+8xt7//337ZNPPjGz0ZUcb1UJz/BXBHkVGYaBSaNa\nrWYvvfSSbWxsjBhPNR6Rg8NgjwE+gBa/PwXakbdej8LKOforcmA1XQQgPGcNZcTKK9dPQZ4neL+e\nB4EfbEFhAMfRA/p8nnj15bbzQJPW1VvV88owiXZtKpMjurjm6YWIkPL0gCee4+SJ51xFjkOk16L3\n571Xy596D8rJTqWmjUiNVPm8d6V0UlF9lZJxCIhxnSatu0fmeX+znfMWO/meXseXGUFcVatVazQa\n2Ta4Xq9nR0dHmX3hqJ2zs7Ps+AAcgM9l0+2Rx8fH2dbGWq1mS0tLtrq6auvr67a6upp9qIhxBJMd\n3H5RlEc0xrz202e8scS2x+ur6D3RvehaXhmKOvVKfuI++oDbB75Pv9/PcEOz2bRKpZKd+zU/P29f\nfvml3bt3z/b390dwW7lctsFgMLIopqQXovTwP3CcmWVjbnZ21ur1ujUaDavX61apVEbwAdrf21qp\n2xw1gp4jBRVzeD5iRKxGOt0TD5MUxXtcdh17kc7kceCRf0x6j2MHPPH0c1QHLhPrJK0Pz9e8ucZ5\nR36Gjn+1p3i2yO4Mrx+jPCO7zfeKypT4ukSZNCeBSS52Lr1BrhNczxdAPuwQQ5FMsgCY4GsukUIs\nqriHw2F2/sIrr7xi169ftwcPHowQX56wsTg6OspWE1VZgcjsdDp248YNe/fdd+2nP/2pvf3227aw\nsJC7vXIqly/lctkajYatrq5as9k0s0dbIdvttlUqFdvc3LRer3fuXAaz0bHEX+nhuRgZ40qlYqur\nq7a8vGxmdo744jHMwEe3TDJxUy6XM5ClB9t7wJhlHOPFoNlzfPl9HPYdvdfrE2/rgLaptpH+4Lqe\n48WfludIr6g8eZIH9lPtnmqLJynTVKYyruSB2DyHII8o03dEDhOny7PnKecgb75FZEGUzntG36Xp\nPbzmvV+F577349kCTqdl5ntF+s5zAqPyaf09gqLIu7hNWMdzPbXtcc3bFcFfMy+VShnxhS1nBwcH\nI5iZ88Y1EFpmlm2bnJ2dzfLGfXwBstls2vz8fEZ6dTodm5+fH4nyYvJLMXlqkSp1ne9797x+SNmm\nlM3y7qXek5dfdD0aQ5FDzuOOxwhwOCKk5ufnrVQqZSSU2aN239/fD8cYxhcWFvlQe10QVXITXxRl\n4otJLD4bjAmLqE2ZfEMZ9L6OZc4n1V6YS17be7qF8+I6eRhN7+licpHtgJE+itKnRN+jdU7p7ejv\nvDR5+aXyidKqPo3sdJ79jp5JpUvZlEimxNdUMmGiqlwun3PIUkaLo8L0MHtEe+VFSkyC6HYBbz9+\nUQeeJ3O9XrfXXnvNPvnkE/vzn/+cnfHkPa+K+Pj4+BwxgjIeHR1ZqVSyK1eu2N/93d/Zv/zLv9j1\n69dtcXHxSZtiKk9ZKpWKLS4u2ve//327ceOGNRoNK5VK9u///u/W7XatWq2eS8OAAiHv/CUnEKtM\nTkHwlclOp5Pl5REfkbPjkTwzMzMZ8cVfhvJWmszOk1VF5k5kGNWwQ9doKD3+VtIK11Fm/fIR9CM/\nw+/XtuB8ASyxdQH6NS/CKwW08iTljOgzEYGnMiXApnLZkjfmdcxGTtGTvCN65zjgmkVJodR81B++\nPm4a7/645fUWOBjbRV8L9sqSIqM0rVemyOmJ0uXZk8i5U3vFds9bEOH/2e7g+ZOTEyuVHhEdIL74\niArYBbybsfTx8bENBoMRu8P2ZDAY2GAwsJOTE2s0GtkB9uvr67a2tmbVajU7ysDMRtKrpCK9PELA\n+51q37y+jxztcR351PWiaZRY1ev4m7cHYm7wwlmpVMpISnxds9FoWKfTsbm5uZFnTk9P7fDw8Bxe\nw984rqTVamURX7odkMcIfuNrjhgLGiHE0U38Xq+d8Cy2WXJ+PEaVBEbaVB9HfcrXmRTjtvfaAe3D\nZxtznbW8Ol7V/9U28nyyvHzy6s1EoDd3PClqK6L3a/6eTeGyoU5e2YrYVc2raH6RXUjd92RKfF2i\nTJqTgJUJVeZmo86jGnfPOea/AYr4zJ9JFFaQXli793x0jwXg6Nq1a/byyy9brVY7l3ekhNBnZmb9\nfn/k7Iher2fXrl2zmzdv2k9/+lP70Y9+ZFevXrVms1lYQUzl6xP0CULh33nnHWs2mzY3N2e///3v\n7aOPPrKjoyOr1+vZ8yBe+QBcEKCzs7PZNonBYJBFGZVKj4jQmzdv2vLystVqNev1em5oss5tJb8Y\nJAFkgfiC8Iokyq2OYLSt0RMGVB5AUycrBZA94wv9hjM5GBQx+aV5KrjBuxGNWSqVRr7kxFtgtEze\n31o//l/fmwcGPNCT0gns4E1lKpcleWOZr0NS5AfrBwXtPOa9sc121rte1CnxypZH2Oi7ov85rZbL\nww3Ru7x6atvl/ejugOg9KV0z7j2tk/ZjKi887zm22o78rOp5tWXqFAMDw5bUarXsK3+7u7tWLpcz\nW2Bm5774iO1uKB9ILLa/2DqJ7Y2rq6s2Pz9vjUZjJJpFo1vUcfcWpqM+i8ZTaqx5Tr6Xl97Lm4ue\n6PVobGj/peaJls17H88Hs1EyEVtSmXzsdDpZ2er1um1ublq3282+8A2yDNFi2K6KSC7uM+SP8VKv\n121ubs5arVYWhc8RZh6+0bHBbcNbLXmLoy4Qar94kfloD8aF+rym4fLhOu6xT4pn+FkPD2obXCRi\nS9+lWFG/zJoayyhjETInsnMp3cvEo2c38rZCevOShedY6l5RLOk9V9R2pmRKfF2yTBL5xQ40KwAY\n5EhxKRhgpxgrHWdnZ5nynmRR4ivVvwwYUzIcPlrJ+cY3vmEvv/yyzc/PW6VSsePjY/f9mhZlGQwG\nI/eq1aq98cYb9vOf/9x+/etf240bNzKjOZXnVwBwXn/9ddvY2MhCyvf29mxra2uEhACRzHMUxhwk\ndq/XG/k61MzMjF29etVef/11W1xcHIkIU2OaIrt47qMsWM3EVyJRHgUUbOQjEjll0HhLJz+rhreo\nIeb3mj3+EAQfAIxn9MyNCEAjH/SnmVmv18vy5S0wXl09Ik0lD6Tn3cP9FGCKHL6pTOWyJJqXOtf0\nWdxTgJ66xv+PW0Zvzms5i7wjRRB4z+r9aA7r9UiHsDOmuh3i3VPbwA6p6sa8sqQcnKhu/LfXx1Fe\n3nvYgVYH0CP48sg/pOMoXxBfzWbT2u12FhkNW8BnJsFxhr3hrYrI0+wRUdZsNm1hYcGuXLmSRXHD\nzihpodE+UX9BijjDes/rL48Q8Zzw6F70Lu+6J+ro8/Wicz96j4czNFgAz4HMxPbDdrud4XJ8Bfv4\n+Nj29vay/ofvxAQYYwtEkGNhDdikXq9bu93OvuKJRVHGbDomUOZogVG/5KjRXR6B4ukklNGLyI/a\nncdnNJ5026LZeeINPzwnlCjTMRyNEy8f9L8uDqd0ML+jqM5GW+h1vaf19d6tf3vvy9Ox3r0Iz6ds\nYSpNag4WlclmIZ5j8djy512wNQ5GGitPiBrRgzBVCSko4vMJhsPhC0F88SefFexBihhhFkzmxcVF\nu3btmn3729+2/f19+/zzz8Oxw8YIBgih1IeHh3bz5k1788037Ve/+pW99957trGxkfXnVCZHGo2G\nvf3229nXH//zP//T/uu//iuLFOLzG05OTrKDVPlz1Rq9WS6X7fr16/bGG29YtVod+YpoBOajH97i\nMjs7mx3ci6hFBdYsDH70fSiLGmUmyRhs8eoq/21mI/+zjko5ZXxmHteTQS07P/weLi/6aDgc2vHx\nse3v71ur1bJWqzVS53HnpQdW8vKJwFAEriKZFHs2lckUD0zz/6l0HgGVysMD87ju4RzPGcqbc94z\nqfSaf5Rf5CSl5jmue1t1+J4XtcT38CxwHkcsqSPoOWgXlYvoyrx03j2ug9ZdMR/uof56D3gahAG2\nqiESp1arZRgbz3okW6/XG/n643A4tGazacvLy7a8vGydTsc6nU52gLpu5dIxg7J688Yju9AuRcaY\nZ5+iBddUfvjbK19eXlF+el3z8+qktlrLFp0tBbvP/WD2+MuL+DBVuVy2paWlEb/i8PDQut2unZ2d\njRxOz1GJKLuePYz+q9frGd4AnuFxpRFKkU5g7IgxDH8uiiJifOa1ufYlX+dxr2XBnFQdyu/h9o5w\nmfcT6QjNn8eCR3qpHuVyal298R/dU52PZzSiTCP28uaT6ghNlyoH1y9PP0ekmF7z7LHmoeLVK5LJ\nZiHGlKghL/N9k+QkQBFDefJXPnhbjjfJ1fgDEPH5D9iGN6mCya8rcmY2soXTU5CRsCKoVqu2urpq\nb775pt2/f9+++OKLc2nViLDROz4+zhz2119/3X7961/bj3/8Y7t582b2jqlMjmBMvPTSS9nWiFKp\nZJubm3b//v2RrwBVKpXsC59YDeYzWGAYa7WaLS4u2je/+U17+eWXs+2QOk7zCC9d9UdZ8bnsSqUy\nAsJSoEJFiTC0hZJkcEAAgtjwKfnlpfHaGz/4EhJ/AUkdG81DwS8WD2ZmZrLtptiO7DmYeU69lpP/\nL+oQRs6M1w7eM6p/pjKVpylFxnDkQPEcGIfsin7nlU2f0zk8rr31nEfvnjoxnnPC91j/qg738vSI\nL7UHeI4jkqKIryI656JtlkqbGh8qbMeQ1iMFQDyp85kiPhDBwwfT4+M1iMY5OjrKonUQ6a/EA87x\ngl3BNraVlRVbW1uzpaWl7GvKbJv4PC8mPbiOGr0dEZZ6Tx17j0Diex4Jkhoj3hz22thL4zn3el37\n3Cu3loGxt1cGbR8lmSDAaMPhMDuwfnFx0Y6OjqzX69ne3p6VSiXrdrtWr9etXq9bs9nMoru8BTyU\nGYuf/CGFarU6Ul4lfIBvuC28PtHIMw/beRFfeXOb72uEFj8DPcNzUJ9V0tfbaoi665cp8Q7VsWhj\nxaUR6YV8Um3g6XrkEd3z8vIi2ri9PBvhzafIlmg51F5qmfPE69tx7HeUZ9H3Ty4L8ZzLpJFeZo/3\nh/OqE6KJhsNhtjedr5mNrnix8uTrpVIpc4gnXVAXM8vCzbk9IsML8cgstO/c3Jy9++67dvv2bfuP\n//iPc6ur/DdWXRiQtttte+WVV+y9996zX/3qV7awsPD0G2AqX7u02217/fXXs687/eu//qv97ne/\nyw4sxVkew+HQDg8Prdfrmdmj8YnzvYbDoS0tLdmrr75qV69ezT5w4G01ZNDuRXnpuQVmj0i1ubm5\nDGAxqPCcoCL60QOWEAAaBqDsfHmRXrpayO9ggz8zM5N9Balardpw+PgsPYBG/O3Nd6zug+zv9Xo2\nGAzO6cyTk5OR6DyvbRQAadn1WtS+qWf5GQ/wPIlDOpWpPIl4jo1ZcfI15VTk5atjv8g8YP0ZOS2q\nbzR/dV40vToInmNtNrptj51xdpy9+jPBhTRK1vPCphfZMM5iR1Ep4gSm0hZ9DuTRzMzMyDmMaJRh\nxcMAACAASURBVIOIMOF+5/Y2e2SLYQur1arNzc3ZwsKCLS4uZmeyoi1RBrZdIMU4aqzT6dj6+rot\nLS3Z3NxcZt/Y2eWFWi1fkTGI9/M1zyZ57es549p2njPu9Ym2q7a9Pq9lyEujdfb6UnG4956ozkqA\nYRzhTC5sd8QXOfv9/sguGYwHEE8ciQ6/LfqIQrVaHdnmmOozFq0ryq9bHb1nvHexKCkUSWrsaNpo\nXHvPmD0OHOD66LxNCdIzYabkWMpOFdHzRa/zff7NixGRfWEb4T0X2UCvX/Lqy6K2Ns+mp54pkh4y\nMcQXDnR+EinCGj4NwfkwUDTPu2CSnp6ejnwNDhOiWq2OhGPzWQRIr6uBbKxBoEEBT7qgTUAqpA7s\nj5xZ/h+/T05OrFar2Y0bN+xb3/qWXb9+3TY3N0e+8AjnnQkFkI3tdttu3rxpv/zlL+1HP/qRra+v\nFwZ7U3m+ZXZ21ubm5uzGjRtWKpXs4cOHtrOzY91uN4v0whxD1Ca+7ghgZWa2vr5ub731lq2vr2fn\nyLGBL0J4eatepVLJms2mdTqd7PBdPfxUxyLnw+AaooZMASmv/CnJxe9I3eN68CoiwCbOYgFp5ZXX\nA+8Ah2dnj766dXBwkC0omD0+o4W3kHM9vb/5XXqtqCg4yQOc/E6UE1trQdhN9ctUnpZgYSzlOOi8\n5WcYf+Td43ms11QHqhOMv0GO6HaflAOWIh48JwTCTrOXRokv6CEvckm34QBT8KKAp++4LXQRBPXy\nzgDytuJwW3K7eVFDfM8jJj0dVPQ6yq2OO0eC6GIJl0vJRha+h/aCbWi329bpdGxlZcV6vZ4dHBzY\n4eFh1s54N2wurtXrdVtYWMgOscf2Ruhjs8c7ELxo68gR5t86xqI0arci26RzIyJfPBIFz2sZPDvp\n1SkqR1SGlNPvRdZwmbXumA/ePSYjmaxE5Bd/5fPw8DDzy8ws6+uZmZksyp8/XgRiFNFe7Ivq/FNR\n3MV9gd0FIOQYu/AiZLQYqRhJcSSEsZW2u6cDPEJSx5W+B+UEkYhAAsbEnr7mtKz7PXuiY9izT177\n89hKjWdNx2XT96Rsiqf30A/AsR52VjvC9yI7Hdl2tcEsXl5F7kUyMcRXu91+1kUoLOVyOYtuuoxV\nr8uQ4fDxoYscal0ul63ZbNrZ2ZkdHBxkSoHPulLlpSsZIIra7fa5LxZOoqB/YWgYOI0z+cxGJ/vp\n6alVKhXb2NjIzuj64x//OEJ84f188DYOPl1aWrK3337b/vmf/9muXbs2EeNuKsWlVCrZ+vq6rays\n2Pb2tnW7XfvDH/5gt2/fNjPLtkPCUCHKiGVjY8PeeecdW1tbMzPfAHur+xwlEBn2VqtlS0tLmfPK\nRlSBLd7DwtFYPC/4moIpNaKqi/jw+EhPRca4VCplq/IPHjzIPinP5eKyM7iE83d0dGT9ft+63a6V\ny2VrtVrZu0F8AVDinQweue9TTp4nqecU3ELYwdfnUSdvO81UpvI0BNuA80gvvZcHqKP7rN/0eSZ0\n+Br0GR8L4TmSeXNW76vD481/7zBqvFsJdDiXqoOVsGL9oxGznjOnTg8veLIjyWcAsV5RMoYdWvyo\nHoocddWTXG99XsvA7aFEFvJElBbXz+tj/p//VnID+WEhC/Zhf3/fdnd3s0Usbksu38zMTBYRdPXq\nVVteXraFhYVzC0Dob21j5KvtFRE/Wke95tU9ctRT7aRplGxQe+WlYdG6K17hNtF8IqLKwxD4zf3M\nwgSE9oGOP/hVlUrFFhYWMjxRKpUyvIe5i7lVKpWyLZPwt/jQ/OhQe28O6jjQujLpxcQXt79HfHm+\nEV/z8AbaRfuM03p5pfrMm+OoE3ZOoF7s13lYSYMPuA89XYf29BZfonnhkdY69rw2UbJM6x3NZW0T\nJaGZPFQboPaW+y/PFnv3vOuaV969lEwM8TVJkUKlUinc+/y8iA4YnDHAn8I1ezxZsJ3q9PTUer1e\ntu3HAyPIGwZ8cXHRWq3WCFkzycJGoNFomJmdi9yInEpPPMf92rVr9vd///fW7Xbt448/dtNhBajZ\nbNo3v/lN++Uvf2k///nP7cqVK9ZoNJ7bsTeV8YUdgHK5bG+88UZGeFYqFbt79651u107PT21fr9/\nLj1/OOGll17KiFsvSlMdI/6bDTvKhc+zz83NWa1Wy3QGnwHBdYCwg8NgQedD6rnIaWXQpc4rriko\n4nQAM/Pz87a0tGS7u7sjJJXqRwUR0KccccdgEGcmYlWXD4ZmJ5YdXQ+8e6JgSsFSKh91aPh/BkVo\nw6lM5WmKbvv1ALV3P/rNzyuw9nQLrkM/eES/mY3oTVz35oznvHtOv84zvYZnNYIL19kJ4zpzec0s\n0znQP3w0hTpzSPf/2fvyH7mO6+rTs/X0Ojs3UaQsS5Ztyo4TxHKQGE7wGcgPAfIHB4kDAwFiw4ss\nUZFsSTTXoTj79PS+DKe/H4j7ePrMrXqvhzOcGaov0OjuV3u9qlv3nrp1iwFA5qWaFx/Fso/H80P9\noe1VPpUWpm3mMJVptV/5ubXdeF1IIdM28dix9Dq+DPiyUxSVSgUrKyvY399HrVZLrHcsrtXdNlur\n1SquXr2abIAVi8UEmGNwToEYVl5DQIS3pmj/evFDa4hHsXL421ujYvULlWXfWfLTeRlK4wEsXhpO\nq/1tv3mM2PPhcJhYBPIlRtPT02g2myMgF4MuudxL36TlchkLCwuJU3vmZSzDeDzO8uT6WN75fD4B\niBSYVxAs1DeWLz/z+k1lnhiPD5XD81mtyLjO+Xz+mLGK1snqo3zaeKTxwZB8xWPA+lxl49h4jsXz\ndHavjzjMiNcU5v/D4ctTG6oncJlqgadyu74z77fXR5pO2xMLS6NLA3ydhlN0HYRnRWoCGRvIr5u8\nQXd0dJQoarZ7wIzRhJlisYjDw0N0Op3kTDkzAM3XbpgrFouoVCrHmOVJ6s10Xv1q/WLAlym48/Pz\nQWYeE5wsrglNz58/x7Vr1/Av//IvuHv3Ln7zm9+g2+2O+Biy3cPhcIgbN27gRz/6Ef793/8dP/nJ\nT7CwsPBGAIwTOk425r/zne9gaWkJnU4HnU4H+/v72N3dHZnDPN5WVlbwt3/7t/jggw+wurqajFng\n+JEf3sVnPy4MfnFd5ufnsby8nFibqSKWJpx6iqe3AOqHBT6dd6zsaBneM1Wa7P/CwgJWVlbw+PFj\n9Pv9EYEA8C3Ypqen0e120e12E0XGbmOyeNy33JdWN7MK4LKsPFXumLQNGidNSdBnKuTZ2sZ9NKEJ\nnRap79CQUMvzX8lLp/Pce+aBBDZPlRexQgD4Fiwe31MlwZtjaR8FvhQgN9INAZMZlJ9zPVR2HQ6H\n7qYHW6zaemObHCFrEG0nU6i/NB2/05ASGOpfVVpDZVhezIN5XbQyGCjg9YSVUOb5fOGMgRvFYhHL\ny8tYWVlBrVZDu90eueHRFEizEFtdXcXVq1dx5coVLC4uJrzY8larOU8RZ7CE2x+yTtI+DCn2Gt+j\nrGEhAOWk+SmfCIXF1kqtm5dO57cRA6Cchp/rkVbz+WVGHxbHxoiBYvbuASQ6SblcRrVaRbFYxNzc\n3Mh81/nNczs0l2wMmmUUg+OxuWtpLS9PJvFkMyPvdAHrrfp+uE3KM725YPPHgC+Pb2l8rqf1nW0A\nxCwudY3RjZMQ4Ka/uT5eeFY+qmXrx/qYQTFui8cTtK2hdVh/a1r+H4sbCkujSwN8nQa9LiE9JFBd\nFNJFnCduiDnbToLFA3BscTZGYGV0u93kuuXV1dVXAmNsl8PqZAzqPMkEF6tb6F3HxoA30c3h9cLC\nAv7hH/4BBwcH+N///V/cv38fwMu+ODo6QqFQwM9//nP827/9G959912USqUTg4sTujw0NfXi2NyP\nf/xjDAYD1Go1/N///R/W19eTBdjmKfAC+PrpT3+KmzdvJkd0AH9B5g+DYJ7gYvW4cuUKisUigJc7\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/2Htg2ZH7WS0urV/sEwJAtc9C60kW0NF+67cn5/EJBe9Gy1A99X3qWPDih0AqTZtWtpXp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\nrIE9DfGk0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QZlhUIhkDMLCwsYj8d4/PgxHjx4gOPjY1y9ehUbGxv4xS9+gY8//hg3b95E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cDJNzACIGhKx4GGxZu9kqmV8sgJkzackhlzhdKxy749s7tpgixSw39tO3P3rPhN/WpLNWe2n7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\nYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy import signal\n",
"\n",
"img = mpimg.imread('i/super_mario_head.png')\n",
"kernel = np.outer(signal.gaussian(3, 2), signal.gaussian(3, 2))\n",
"blurred = signal.fftconvolve(img[:,:,0], kernel)\n",
"\n",
"# image in frequency domain\n",
"fft_original = np.fft.rfft2(img)\n",
"\n",
"# discarding some coefficients\n",
"for x in range(300):\n",
" for y in range(300):\n",
" discard_x = x > 80\n",
" discard_y = y > 80\n",
" \n",
" if discard_x and discard_y:\n",
" fft_original[x,y] = 0\n",
" \n",
"# reverse from frequency to spatial\n",
"i_fft_original = np.fft.irfft2(fft_original)\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2, figsize=(15,15))\n",
"\n",
"#plt1.axis('on');plt1.set_title('Spatial domain');plt1.imshow(kernel);\n",
"plt1.axis('off');plt1.set_title('Original');plt1.imshow(img[:,:,0], cmap='gray');\n",
"\n",
"\n",
"plt2.axis('off');plt2.set_title('Reversed');plt2.imshow(i_fft_original[:,:,0], cmap='gray');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: s/cut_smaller_video.sh
================================================
#!/bin/bash
#./s/ffmpeg -y -i /files/v/bunny_1080p_60fps.mp4 -ss 00:01:24 -t 00:00:10 /files/v/small_bunny_1080p_60fps.mp4
#./s/ffmpeg -y -i /files/v/bunny_1080p_30fps.mp4 -ss 00:01:24 -t 00:00:10 /files/v/small_bunny_1080p_30fps.mp4
echo "These files are already downloaded"
================================================
FILE: s/download_video.sh
================================================
#!/bin/bash
# the links aren't work anymore :(
# wget -c -O v/bunny_1080p_60fps.mp4 http://distribution.bbb3d.renderfarming.net/video/mp4/bbb_sunflower_1080p_60fps_normal.mp4
# wget -c -O v/bunny_1080p_30fps.mp4 http://distribution.bbb3d.renderfarming.net/video/mp4/bbb_sunflower_1080p_30fps_normal.mp4
echo "we already provide the smaller versions at https://github.com/leandromoreira/digital_video_introduction/tree/master/v"
================================================
FILE: s/ffmpeg
================================================
#!/bin/bash
docker run --rm -v $(pwd):/files jrottenberg/ffmpeg:3.3 $@
================================================
FILE: s/mediainfo
================================================
#!/bin/bash
docker run --rm -v $(pwd):/files leandromoreira/mediainfo $@
================================================
FILE: s/start_jupyter.sh
================================================
#!/bin/bash
# Run Jupyter Notebook (Python 3.5.2, scipy 0.17.1)
docker run --rm -p 8888:8888 -v "$(pwd)":/home/jovyan/work jupyter/scipy-notebook:387f29b6ca83
================================================
FILE: s/vmaf
================================================
#!/bin/bash
docker run --rm -v $(pwd):/files nonatomiclabs/vmaf:1.0 $@
================================================
FILE: setup.sh
================================================
#!/bin/bash
check_cmd()
{
if ! which $1 &>/dev/null; then
error "$1 command not found, you must install it before."
fi
}
error()
{
echo "Error: $*" >&2
exit 1
}
check_cmd docker
check_cmd wget
./s/download_video.sh
./s/cut_smaller_video.sh
================================================
FILE: uniform_quantization_experience.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Uniform quantization in frequency domain"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import fftpack\n",
"from skimage.util import img_as_ubyte\n",
"import matplotlib.image as mpimg"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# loading image\n",
"img = img_as_ubyte(mpimg.imread('i/super_mario_head.png'))\n",
"choosen_y_x = 90\n",
"resolution = 128\n",
"\n",
"img_slice = img[choosen_y_x:(choosen_y_x + resolution), choosen_y_x:(choosen_y_x + resolution), 2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Distribution: Frequency vs Spatial"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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qHAOcl+QtpZRVwFLgYcCrmpWkhpM8BXgTcGbbNkuSJEmSJEnTZVpX+UzyaGBP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\nSZIkSZI0gP8PK1MQS91PjJwAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# transform: 2D DCT\n",
"dct_slice = fftpack.dct(fftpack.dct(img_slice.T, norm='ortho').T, norm='ortho')\n",
"\n",
"dct_hist, dct_bin_edges = np.histogram(dct_slice, bins = range(100))\n",
"img_hist, img_bin_edges = np.histogram(img_slice, bins = range(100))\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"\n",
"plt1.set_title('Frequency histogram')\n",
"plt1.bar(dct_bin_edges[:-1], dct_hist, width = 1)\n",
"\n",
"plt2.set_title('Spatial histogram')\n",
"plt2.bar(img_bin_edges[:-1], img_hist, width = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantize by dividing and requantize by multyplying"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"block = img_slice[80:88, 40:48]\n",
"\n",
"quantize_step = 5\n",
"\n",
"dct_slice = fftpack.dct(fftpack.dct(block.T, norm='ortho').T, norm='ortho')\n",
"\n",
"\n",
"dct_slice_quantized = np.divide(dct_slice,[quantize_step])\n",
"rounded_quantized = np.around(dct_slice_quantized)\n",
"\n",
"dct_slice_requantized = np.multiply(rounded_quantized,[quantize_step])\n",
"\n",
"idct_slice = fftpack.idct(fftpack.idct(dct_slice_requantized.T, norm='ortho').T, norm='ortho')\n",
"\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"\n",
"plt1.axis('off');\n",
"plt1.set_title('Original')\n",
"plt1.imshow(block, cmap='gray',interpolation='nearest')\n",
"\n",
"plt2.axis('off');\n",
"plt2.set_title('Quantized')\n",
"plt2.imshow(idct_slice, cmap='gray',interpolation='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DCT Coefficients"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JSyQtlnSZpElVdbaXdIek1yU9JenkzPef9QAzM1tlTQL+BBxPMvN6CEmnAF8EPg/sCiwD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\nogAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(dct_slice, interpolation='nearest', cmap=plt.cm.Paired)\n",
"plt.colorbar(shrink=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Discarding based on coefficient importance"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"It compressed 67.1875 % of the block.\n"
]
}
],
"source": [
"# a 8x8 block\n",
"block = img_slice[80:88, 40:48]\n",
"\n",
"# a 2D DCT\n",
"dct_slice = fftpack.dct(fftpack.dct(block.T, norm='ortho').T, norm='ortho')\n",
"\n",
"original_dct_slice = np.copy(dct_slice)\n",
"\n",
"# keeps only the top left 5 element triangle\n",
"for u in range(8):\n",
" for v in range(8):\n",
" if (u + v) > 5:\n",
" dct_slice[u, v] = 0\n",
"\n",
"print(\"It compressed \", 100 - ((np.count_nonzero(dct_slice)/64) * 100), \"% of the block.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pixel Original"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[227, 255, 255, 155, 146, 158, 169, 178],\n",
" [247, 255, 255, 161, 171, 186, 194, 197],\n",
" [255, 255, 246, 193, 198, 197, 194, 192],\n",
" [226, 206, 193, 193, 192, 189, 189, 188],\n",
" [191, 191, 190, 190, 190, 190, 189, 190],\n",
" [191, 191, 190, 191, 190, 191, 191, 191],\n",
" [191, 191, 192, 192, 193, 193, 193, 193],\n",
" [192, 193, 193, 195, 195, 195, 196, 197]], dtype=uint8)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.set_printoptions(precision=1, linewidth=140, suppress=True)\n",
"block"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DCT Original"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1582. , 87.2, 51.8, -15.9, -34.2, -12.8, 14. , 17.4],\n",
" [ 38.5, 96.1, 56. , -24.7, -44.4, -15.5, 17.1, 20.9],\n",
" [ -3.2, 24.8, 20.3, -26.2, -28.9, -9. , 8.6, 9.8],\n",
" [ -47.1, -9.9, -1.5, -15.9, -8.3, -1.2, 0.9, -1.4],\n",
" [ -33. , -8.3, -5. , -3. , 7.8, 3.5, -4.2, -6. ],\n",
" [ -8.3, 8.5, -3.5, 2.3, 7.9, 2.9, -3.7, -4. ],\n",
" [ 8.6, 12.1, -5.1, -3.3, -0.5, -1.5, -2. , -1.8],\n",
" [ 7.4, 5.8, -6.7, -5.9, -5. , -3.8, 0.4, 0.9]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"original_dct_slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quantized"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1582. , 87.2, 51.8, -15.9, -34.2, -12.8, 0. , 0. ],\n",
" [ 38.5, 96.1, 56. , -24.7, -44.4, 0. , 0. , 0. ],\n",
" [ -3.2, 24.8, 20.3, -26.2, 0. , 0. , 0. , 0. ],\n",
" [ -47.1, -9.9, -1.5, 0. , 0. , 0. , 0. , 0. ],\n",
" [ -33. , -8.3, 0. , 0. , 0. , 0. , 0. , 0. ],\n",
" [ -8.3, 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dct_slice"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idct_slice = fftpack.idct(fftpack.idct(dct_slice.T, norm='ortho').T, norm='ortho')\n",
"\n",
"\n",
"f, (plt1, plt2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"\n",
"plt1.axis('off');\n",
"plt1.set_title('Original')\n",
"plt1.imshow(block, cmap='gray', interpolation='nearest')\n",
"\n",
"plt2.axis('off');\n",
"plt2.set_title('Quantized')\n",
"plt2.imshow(idct_slice, cmap='gray', interpolation='nearest')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# JPEG quantization table"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JSVEURXEqKjEpiqIoTkUlJkVRFMWpqMSkKIqiOBWVmBRFURSnohKToiiK4lRU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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mpimg.imread('i/jpeg_quantization_table.png')\n",
"plt.axis('off');\n",
"plt.imshow(img)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}