Kavli Affiliate: Jing Wang
| First 5 Authors: Yibo Shi, Yunying Ge, Jing Wang, Jue Mao,
| Summary:
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.
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