Video Compression with CNN-based Post Processing

Di Ma

Chen Feng

University of Bristol


In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content. Among various compression tools, post-processing can be applied on reconstructed video content to mitigate visible compression artefacts and to enhance overall perceptual quality. Inspired by advances in deep learning, we propose a new CNN-based post-processing approach, which has been integrated with two state-of-the-art coding standards, VVC and AV1.

Source code

Source code from github will be avaliable very soon.


The results show consistent coding gains on all tested sequences at various spatial resolutions, with average bit rate savings of 4.0% and 5.8% against original VVC and AV1 respectively (based on the assessment of PSNR). This network has also been trained with perceptually inspired loss functions, swhich have further improved reconstruction quality based on perceptual quality assessment (VMAF), with average coding gains of 13.9% over VVC and 10.5% against AV1.


  title={Video compression with CNN-based post processing},
  author={Zhang, Fan and Ma, Di and Feng, Chen and Bull, David R},
  journal={IEEE MultiMedia},

  title={Enhancing VVC through CNN-based post-processing},
  author={Zhang, Fan and Feng, Chen and Bull, David R},
  booktitle={2020 IEEE International Conference on Multimedia and Expo (ICME)},