Residual-Based Video Restoration For Hevc Intra Coding

Inspired by the great success of convolutional neural network (CNN) in computer vision, CNN-based post-processing methods at the decoder side have achieved significant advances in improving the video quality. However, these methods only learn a mapping from the decoded frame to an artifact-free reconstruction, which ignores that the distortion comes from quantization of the prediction residual transform coefficients. In this paper, we propose a residual-based video restoration network (Residual-VRN) to improve the quality of decoded video, in which the coded prediction residual is combined with the prediction frame as the input of the network. Meanwhile, the activation function, the residual learning framework and the loss function can also be optimized to achieve better quality enhancement. Experimental results show that the proposed Residual-VRN leads to an average 7.41% BD-rate reduction compared to the HEVC intra coding baseline, outperforming the conventional CNN-based video restoration algorithms. In deeper CNN architectures, our method achieves 8.0%, 9.0% and 11.1% BD-rate savings, much higher than the conventional CNN-based post-processing methods.