AlphaVC: High-Performance and Efficient Learned Video Compression

TL;DR

AlphaVC is a novel learned video compression scheme that surpasses traditional standards in quality and speed by introducing key techniques like conditional-I-frames, pixel-to-feature motion prediction, and entropy skipping.

Contribution

It presents the first end-to-end AI video codec that exceeds VVC in PSNR and MSSSIM metrics while maintaining fast encoding and decoding speeds.

Findings

Outperforms VVC with -28.2% BD-rate in PSNR

Achieves -52.2% BD-rate in MSSSIM

Offers significantly faster encoding and decoding speeds

Abstract

Recently, learned video compression has drawn lots of attention and show a rapid development trend with promising results. However, the previous works still suffer from some criticial issues and have a performance gap with traditional compression standards in terms of widely used PSNR metric. In this paper, we propose several techniques to effectively improve the performance. First, to address the problem of accumulative error, we introduce a conditional-I-frame as the first frame in the GoP, which stabilizes the reconstructed quality and saves the bit-rate. Second, to efficiently improve the accuracy of inter prediction without increasing the complexity of decoder, we propose a pixel-to-feature motion prediction method at encoder side that helps us to obtain high-quality motion information. Third, we propose a probability-based entropy skipping method, which not only brings performance gain, but also greatly reduces the runtime of entropy coding. With these powerful techniques, this paper proposes AlphaVC, a high-performance and efficient learned video compression scheme. To the best of our knowledge, AlphaVC is the first E2E AI codec that exceeds the latest compression standard VVC on all common test datasets for both PSNR (-28.2% BD-rate saving) and MSSSIM (-52.2% BD-rate saving), and has very fast encoding (0.001x VVC) and decoding (1.69x VVC) speeds.