Convolutional neural network-based post-filtering for compressed YUV420 images and video

Images and videos compressed with lossy compression algorithms usually suffer from visible distortions, especially when the bitrate is low. To improve the quality without spending extra bitrate, many image and video codecs have built-in filters to mitigate these artifacts. However, most of them are only applied on the luminance channel, while the chrominance channels remain unmodified. While this is partly justified by the observation that the luminance channel usually contains more details and has higher-resolution than the chrominance channels. We observe that the luminance and chrominance channels still have latent correlations. Therefore, the post-filtering of the chrominance channels is also beneficial and can be driven by the information from the luminance channel. In this paper, we propose a 3-stage YUV post-filtering network for compressed YUV420 images and video. The proposed 3-stage structure not only improves the quality of the luminance channel, but also exploits the luma-chroma correlations to improve the quality of the chrominance channels. Our experimental results show that the proposed approach achieves 3.60%/12.75%/14.93% Bj⊘ntegaard Delta bitrate improvement for the Y, U and V channels over the VVC 10.0 codec for All-Intra configuration.