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arXiv · 2402.08397

A Neural-network Enhanced Video Coding Framework beyond ECM

Yanchen Zhao·Wenxuan He·Chuanmin Jia·Qizhe Wang·Junru Li·Yue Li·Chaoyi Lin·Kai Zhang·Li Zhang·Siwei Ma
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Abstract

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. Compared with ECM-10.0, our method achieves 6.26, 13.33, and 12.33 BD-rate savings for the Y, U, and V components under random access (RA) configuration, respectively.

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Yanchen Zhao, Wenxuan He, Chuanmin Jia, Qizhe Wang, Junru Li, Yue Li, Chaoyi Lin, Kai Zhang, Li Zhang, Siwei Ma. 2024-02-13. A Neural-network Enhanced Video Coding Framework beyond ECM. https://arxiv.org/abs/2402.08397

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