Optimizing Video Quality Using Machine-Learning-Controlled Adaptive Resolution, Video Compression

数字视频需要巨大的数据量，并且必须在存储和/或传输之前进行压缩。电视广播公司、视频游戏制造商、视频内容发行商在其产品和/或服务中使用视频压缩。AMD为平板电脑、游戏机、嵌入式设备和云服务器设计和制造显卡和微处理器。这些产品必须提供尽可能最好的视频质量。在视频压缩中，比特率和视频质量之间存在内在的权衡。因此，获得给定比特率的最佳视频质量是一项至关重要的任务。可以通过在编码前降低视频序列的分辨率或在编码期间使用更大的量化参数来降低视频序列的比特率。这两个选项中哪一个质量损失较小取决于视频内容和可用的网络带宽。本研究项目的目标是设计一种机器学习算法，以便在运行时决定是以原始的高分辨率对视频图像(或图像组)进行编码，还是降低分辨率，以较小的量化步长对较低分辨率的版本进行编码，并在接收端进行解码和上采样，以期在给定的比特率下获得最好的质量。通过利用最佳分辨率/量化步长的组合，我们开发的自适应视频分辨率调整方案可以显著节省目标质量的比特率或显著提高目标比特率的质量。

Digital video requires a huge volume of data and must be compressed before it can be stored and/ortransmitted. TV broadcasters, video game manufacturers, video content distributors use video compression intheir products and/or services. AMD designs and manufactures graphics cards and microprocessors for tablets,gaming consoles, embedded devices and cloud servers. These products must yield the best possible videoquality. In video compression, there is an inherent trade-off between bitrate and video quality. Obtaining thebest video quality for a given bitrate is, therefore, a crucial task. The bitrate of a video sequence can be reducedby reducing its resolution prior to encoding or by using a larger quantization parameter during encoding. Whichof these two options yields less quality loss depends on the video content as well as the available networkbandwidth. The goal of this research project is to design a machine learning algorithm to make a run-timedecision of whether to encode a video picture (or group of pictures) at the original high resolution, or to reduceresolution, encode the lower resolution version with a smaller quantization step, decode and upsample atreceiver side with expectation to achieve the best quality for a given bit rate. By utilizing the optimalresolution/quantization step combination, our developed adaptive video resolution adjustment scheme canresult in significant bitrate savings for a target quality or significant quality improvements for a target bitrate.