Convolutional Neural Network-Based Residue Super-Resolution for Video Coding

Abstract

Inspired by the progress of image and video super-resolution (SR) achieved by convolutional neural network (CNN), we propose a CNN-based residue SR method for video coding. Different from the previous works that operate in the pixel domain, i.e. down- and up-sampling of image or video frame, we propose to perform down- and up-sampling in the residue domain. Specifically, for each block, we perform motion estimation and compensation to achieve residual signal at the original resolution, then we down-sample the residue and compress it at low resolution, and perform residue SR using a trained CNN model. We design a new CNN for residue SR with the help of the motion compensated prediction signal. We integrate the residue SR method into the High Efficiency Video Coding (HEVC) scheme, providing mode decision at the level of coding tree unit. Experimental results show that our method achieves on average 4.0% and 2.8% BD-rate reduction under low-delay P and low-delay B configurations, respectively.