CNN based post-processing to improve HEVC

In this paper, we propose a frame-based dynamic metadata post-processing scheme in HEVC. Video sequence is classified into different categories contains complexity of video content and quality indicator for each frame, an up-to-one byte flag embedded in the bitstream is transferred as side information. Meanwhile dynamic metadata contains classification information indicates the offline training of separate network models. Specifically, we adopt a 20-layers CNN (Con-volutional Neural networks) model to extract more meaningful information from the reconstructed error and improve the filtering performance. Experimental results shows that our proposed post-processing scheme leads on average 1.6% BD-rate reduction compared with HEVC baseline on the six sequences given in 2017 ICIP Grand Challenge.