Adaptive Post Filter for Reducing Block Artifacts in High Efficiency Video Coding
Antoine Chauvet, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi · 2016
This paper describes an adaptive deblocking postfilter based on neural networks for use in H.265 High Efficiency Video Coding (HEVC). Blocking noise is a common problem in video coding caused by the division of the frame into blocks. The filter is adaptive because it uses different filter parameters depending on block characteristics. We use a modified HEVC decoder to export the block information to the filter. Blocks are put in different categories according to transform and prediction parameters. We train the filter in each case to optimize the internal parameters. We find that a 2-layer convolutional neural network is able to outperform the in-loop deblocking filter for a moderate processing cost. We propose to apply the filter after the HEVC in-loop deblocking filter. We demonstrate that our filter helps further reduce visual artifacts in video.