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Video streaming usage has seen a significant rise as entertainment,education, and business increasingly rely on online video. Optimizing videocompression has the potential to increase access and quality of content tousers, and reduce energy use and costs overall. In this paper, we present anapplication of the MuZero algorithm to the challenge of video compression.Specifically, we target the problem of learning a rate control policy to selectthe quantization parameters (QP) in the encoding process of libvpx, an opensource VP9 video compression library widely used by popular video-on-demand(VOD) services. We treat this as a sequential decision making problem tomaximize the video quality with an episodic constraint imposed by the targetbitrate. Notably, we introduce a novel self-competition based reward mechanismto solve constrained RL with variable constraint satisfaction difficulty, whichis challenging for existing constrained RL methods. We demonstrate that theMuZero-based rate control achieves an average 6.28% reduction in size of thecompressed videos for the same delivered video quality level (measured as PSNRBD-rate) compared to libvpx's two-pass VBR rate control policy, while havingbetter constraint satisfaction behavior.

MuZero with Self-competition for Rate Control in VP9 Video Compression