A New HEVC In-Loop Filter Based on Multi-channel Long-Short-Term Dependency Residual Networks

In this paper, we propose a new HEVC in-loop filter based on a multi-channel long-short-term dependency residual network (MLSDRN). Inspired by the information storage and information update function of human memory cell, our MLSDRN introduces an update cell to adaptively store and select the long-term and short-term dependency information through an adaptive learning process. In addition, we leverage the block boundary information that recorded in the bit-streams to improve the filter performance, which also makes our MLSDRN to unequally treat the video content. Meanwhile, the multi-channel is introduced to solve the illumination discrepancy problem. We integrate the novel in-loop filter into HM reference software, and applying it to luma and chroma components, simulation results demonstrate that the proposed in-loop filter can save BD-rate reduction up to 15.9% with ALF off. For luma component, the novel in-loop filter achieves 6.0%, 8.1%, 7.4% BD-rate saving for all intra, low delay and random access configurations, respectively.