  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.
