Multi-Scale Grouped Dense Network for VVC Intra Coding

Xin Li, Simeng Sun, Zhizheng Zhang, Zhibo Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 158-159

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codecs, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams ""IMC"" and ""haha"" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.