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In this paper, we propose a learned video codec with a residual predictionnetwork (RP-Net) and a feature-aided loop filter (LF-Net). For the RP-Net, weexploit the residual of previous multiple frames to further eliminate theredundancy of the current frame residual. For the LF-Net, the features fromresidual decoding network and the motion compensation network are used to aidthe reconstruction quality. To reduce the complexity, a light ResNet structureis used as the backbone for both RP-Net and LF-Net. Experimental resultsillustrate that we can save about 10% BD-rate compared with previous learnedvideo compression frameworks. Moreover, we can achieve faster coding speed dueto the ResNet backbone. This project is available athttps://github.com/chaoliu18/RPLVC.

Learned Video Compression with Residual Prediction and Loop Filter