“MuZero With Self-Competition for Rate Control in VP9 Video Compression”, 2022-02-14 (; similar):
Video streaming usage has seen a substantial rise as entertainment, education, and business increasingly rely on online video. Optimizing video compression has the potential to increase access and quality of content to users, and reduce energy use and costs overall.
In this paper, we present an application of the MuZero algorithm to the challenge of video compression. Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.
We treat this as a sequential decision making problem to maximize the video quality with an episodic constraint imposed by the target bitrate. Notably, we introduce a novel self-competition based reward mechanism to solve constrained RL with variable constraint satisfaction difficulty, which is challenging for existing constrained RL methods.
We demonstrate that the MuZero-based rate control achieves an average 6.28% reduction in size of the compressed videos for the same delivered video quality level (measured as PSNR BD-rate) compared to libvpx’s two-pass VBR rate control policy, while having better constraint satisfaction behavior.
[DM: “Then we applied MuZero to some of YouTube’s live traffic. Since launching to production on a portion of YouTube’s live traffic, we’ve demonstrated an average 4% bitrate reduction across a large, diverse set of videos. Bitrate helps determine the computing ability and bandwidth needed to play and store videos—impacting everything from how long a video takes to load to its resolution, buffering, and data usage.”]