Learned Video Compression with Residual Prediction and Loop Filter

Chao Liu; Heming Sun; Jiro Katto; Xiaoyang Zeng; Yibo Fan

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arXiv · 2108.08551

Learned Video Compression with Residual Prediction and Loop Filter

Chao Liu·Heming Sun·Jiro Katto·Xiaoyang Zeng·Yibo Fan

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Abstract

In this paper, we propose a learned video codec with a residual prediction network (RP-Net) and a feature-aided loop filter (LF-Net). For the RP-Net, we exploit the residual of previous multiple frames to further eliminate the redundancy of the current frame residual. For the LF-Net, the features from residual decoding network and the motion compensation network are used to aid the reconstruction quality. To reduce the complexity, a light ResNet structure is used as the backbone for both RP-Net and LF-Net. Experimental results illustrate that we can save about 10% BD-rate compared with previous learned video compression frameworks. Moreover, we can achieve faster coding speed due to the ResNet backbone. This project is available at https://github.com/chaoliu18/RPLVC.

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Chao Liu, Heming Sun, Jiro Katto, Xiaoyang Zeng, Yibo Fan. 2021-08-19. Learned Video Compression with Residual Prediction and Loop Filter. https://arxiv.org/abs/2108.08551

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