MuZero with Self-competition for Rate Control in VP9 Video Compression (2202.06626v1)

Abstract: Video streaming usage has seen a significant 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.

• The paper introduces a self-competition reward mechanism that guides MuZero in optimizing quantization parameters for effective video rate control.
• It formulates rate control as a constrained Markov Decision Process, achieving a 6.28% reduction in bitrate for equivalent PSNR quality.
• The approach circumvents traditional Lagrangian multipliers, offering a practical, real-world solution for improved VP9 video encoding.

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

This paper presents a novel application of the MuZero algorithm, an advanced reinforcement learning (RL) framework, to address the rate control problem in the VP9 video compression process—specifically within the libvpx encoding library. The authors confront the challenge of optimizing quantization parameters (QPs), a crucial aspect that dictates the balance between bitrate and video quality (measured by metrics such as PSNR and BD-rate). The primary contribution is introducing a unique self-competition-based reward mechanism that enhances constrained reinforcement learning capabilities, effectively overcoming the intricate issues of constraint satisfaction posed by variable bitrate requirements in video encoding.

The authors formulate the rate control challenge as a constrained Markov Decision Process (CMDP), where the targeted objective is to maximize encoding quality subject to a bitrate constraint. The self-competition mechanism emerges as a key innovation, enabling the tuning of the model to learn from its past performance, steadily advancing the encoding efficiency in a cyclical fashion. Importantly, this approach bypasses the conventional reliance on Lagrangian multipliers, which are often fraught with tuning complexities and may necessitate individualized configurations across varying conditions.

The empirical results underscore that MuZero-based rate control attains substantial improvements over the standard libvpx's two-pass VBR approach. Specifically, the average bitrate reduction sits at 6.28% for equivalent PSNR quality levels, a notable achievement reflecting significant encoding efficiency gains. This metric is not trivial—such enhancements across numerous video streams can precipitate shifts in storage and bandwidth usage towards more sustainable ends.

Furthermore, the paper demonstrates notable improvements in constraint satisfaction, with the MuZero-RC agent showing fewer instances of bitrate overshooting, thereby aligning with practical bitrate limits more consistently than libvpx's native implementation. This suggests strong practical adaptability and relevance for real-world scenarios where achieving fine-grained bitrate control is critical across diverse video content types.

The theoretical ramifications of integrating reinforcement learning in a tightly constrained CMDP environment are profound. The proposed self-competition scheme could very well extend beyond video compression, providing a robust framework for other complex system optimizations where conventional approaches may falter in scalability or adaptability.

In future developments, the scope of the proposed methods could transcend to broader video formats or even entirely different encoder features such as block partitioning strategies or reference frame selection. Additionally, there is potential to explore alternative video quality metrics like VMAF or SSIM as objective functions, further aligning the encoding output with perceptually motivated quality assessments.

Overall, this work is an exemplary paper of leveraging advanced AI techniques in practical engineering domains, presenting both a methodological blueprint and tangible improvements in video encoding that could incite significant impact on codec development and deployment strategies.