Deep In-Loop Filtering via Multi-Domain Correlation Learning and Partition Constraint for Multiview Video Coding
Bo Peng, Renjie Chang, Zhaoqing Pan, Ge Li, Nam Ling, Jianjun Lei
Abstract
The deep learning-based in-loop filtering methods have greatly improved the coding efficiency for High Efficiency Video Coding (HEVC). However, directly applying these HEVC-orientated in-loop filtering methods to multiview video coding may not obtain satisfactory performance due to the characteristics of multiview video. In this paper, a deep in-loop filtering method based on multi-domain correlation learning and partition constraint network (MDP-Net) is proposed to boost the multiview video coding performance. To the best of our knowledge, this work is the first attempt at deep in-loop filtering for multiview video coding. Specifically, a multi-domain correlation learning module is presented to restore the high-frequency details of the distorted frame by exploring the multi-domain correlations. Besides, based on the block partition information generated in video coding, a partition-constrained reconstruction module is proposed to better attenuate the compression artifacts by designing a partition loss. Finally, the proposed MDP-Net is integrated into 3D-HEVC reference software, and the experimental results demonstrate that the proposed method achieves considerable performance improvement compared with 3D-HEVC.