Coding-Information Based Improvement For In-Loop Filters Beyond VVC

The in-loop filter tools in video coding, such as De-Blocking Filter (DBF) and Adaptive Loop Filter (ALF), can significantly improve the video compression performance of the VVC standard. In this paper, the coding-information based improvements for DBF and ALF are proposed to further increase the coding efficiency beyond VVC. The coding-information can be referred to the partitioning information which is not fully utilized by in-loop filters in the VVC standard. Two improvements are included in this paper. The first improvement is the coding-information based classification for ALF. In the current design of ALF, the partitioning information has not been considered for the classification of both online and offline filters. To further increase the classification diversity and efficiency, the coding-information is introduced into classification of both online and offline filters through an always applied rule. The second improvement is the boundary-aware offset refinement. In current DBF and ALF design, the offset generated by DBF and ALF cannot be further adjusted after the filtering process. To provide more possibilities for the filtering offset, an offset refinement process is proposed. The offset can be refined by a non-linear function with the help of the coding-information. The proposed two improvements are implemented on top of ECM-14.0 software which is published by JVET. Experimental results show that the classification method can achieve -0.08% and -0.12% while the offset refinement method can achieve -0.05% and -0.11% BD-rate reduction on the luma component in Random Access and Low-Delay B configurations respectively. Both proposed improvements have been adopted into ECM.