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.
@article{arxiv.2108.08551,
title = {Learned Video Compression with Residual Prediction and Loop Filter},
author = {Chao Liu and Heming Sun and Jiro Katto and Xiaoyang Zeng and Yibo Fan},
journal= {arXiv preprint arXiv:2108.08551},
year = {2021}
}