### Collaborating with YouTube to Improve Video Compression with MuZero
In 2016, AlphaGo, an AI program developed by DeepMind, made history by defeating humans in the ancient game of Go. Since then, DeepMind has continued to advance AI with programs like AlphaZero and MuZero, which have mastered various games with minimal prior knowledge. Now, DeepMind is taking a significant step towards solving real-world tasks by collaborating with YouTube to optimize video compression using MuZero.
In a preprint published on arXiv, DeepMind details its partnership with YouTube to explore how MuZero can enhance video compression. As streaming video accounts for the majority of internet traffic, improving video compression becomes crucial. DeepMind is applying reinforcement learning techniques to enhance the current state-of-the-art video codecs. Through this collaboration, they have achieved an average 4% reduction in bitrate across a wide range of videos.
Most online videos rely on codecs, which compress and encode videos for transmission and decode them for playback. These codecs make decisions for each frame in a video, and DeepMind aims to use RL algorithms to optimize these decision-making processes. The initial focus is on the VP9 codec, widely used by YouTube, and DeepMind has demonstrated promising results in reducing bitrate while maintaining video quality.
In the VP9 codec, bitrate optimization is primarily achieved through the Quantization Parameter (QP) in the rate control module. The QP determines the compression level for each frame, with higher bitrates allocated for complex scenes and lower bitrates for static scenes. DeepMind’s MuZero-RC replaces the default rate control mechanism in VP9 and decides the compression level for each frame, achieving similar quality at a lower bitrate.
MuZero’s ability to learn and plan in combinatorial action spaces makes it an ideal solution for rate control in video compression. However, applying MuZero to real-world applications presents new challenges, such as adapting to diverse video content and generalizing across different videos. DeepMind tackles these challenges by using a mechanism called self-competition, which simplifies the video compression objective into a win/loss signal that the agent can optimize.
By learning the dynamics of video encoding and optimizing bit allocation, MuZero is able to reduce the bitrate without sacrificing quality. DeepMind aims to develop a single algorithm that can automatically make encoding decisions for optimal rate-distortion tradeoff. This application of MuZero to video compression is just the beginning, as DeepMind envisions using RL agents to solve various real-world problems and improve computer systems across domains.
This collaboration between DeepMind and YouTube represents a significant milestone in the application of AI to real-world tasks. By equipping AI agents like MuZero with new abilities, we can optimize and automate systems in an array of domains, making them more efficient and less resource-intensive. DeepMind’s long-term vision is to develop a single algorithm capable of optimizing numerous real-world systems in various domains.
To learn more about MuZero and its potential impact, listen to Jackson Broshear and David Silver discuss it in Episode 5 of DeepMind: The Podcast.
**Keywords:** AI, MuZero, DeepMind, YouTube, video compression, reinforcement learning, bitrate, codec, VP9, rate control, optimization.
1. Collaboration for Video Compression Optimization
2. Applying RL to Video Compression Codecs
3. The Future of MuZero and Real-World Problem Solving