RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding

Abstract

Integrating deep learning techniques into the video coding framework gains significant improvement compared to the standard compression techniques, especially, applying super-resolution (up-sampling) in down-sampling based video coding as post-processing. However, different from the conventional super-resolution works, the output can be distorted by the various compression artifacts in the decoded frames. The straightforward solution is to integrate the artifacts removing techniques before super-resolution. But some helpful features may be removed together with the artifacts, which will degrade the performance of super-resolution. To address this problem, we proposed a restoration-reconstruction deep neural network (RR-DnCNN) using the degradation-aware techniques. Moreover, to prevent the loss of essential features in the very deep network from restoration to super-resolution, we leverage up-sampling skip connections to compensate for the lost information from restoration layers. It is called restoration-reconstruction u-shaped deep neural network (RR-DnCNN v2.0). As a result, our RR-DnCNN v2.0 can achieve 17.02% BD-rate reduction on UHD resolution compared to the standard H.265/HEVC. The source code is available at https://minhmanho.github.io/rrdncnn/.

Tasks

Reproductions