Learned Video Compression with Residual Prediction and Loop Filter
This work addresses video compression efficiency for applications like streaming, but it is incremental as it builds on existing learned video compression methods.
The paper tackles video compression by proposing a learned codec with a residual prediction network and a feature-aided loop filter, achieving about 10% BD-rate savings and faster coding speed compared to previous learned frameworks.
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.