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

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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.