Asymmetric Motion Vector Refinement for Future Video Coding
Yunrui Jian, Yue Huang, Zhenan Lin, Lei Meng, Yi Xue, Lei Guo, Chao Zhou · 2024
Efficiently improving the accuracy of motion vector (MV) in merge candidate list is a critical issue in terms of the advanced inter coding technologies. The decoder side motion vector refinement (DMVR) and merge mode with motion vector differences (MMVD) are utilized to refine the MV obtained from merge mode in Versatile Video Coding (VVC). Nevertheless, both DMVR and MMVD operate under the assumption of symmetric motion when adjusting bi-prediction MV. This assumption may result in inaccuracies adjusting where the motion is asymmetric, leading to imprecise MV. To address this issue, we propose the asymmetric motion vector refinement (ASMVR) approach to refine asymmetric motion more accurately for future video coding in this paper. Specifically, ASMVR is formulated by the asymmetric MMVD (asy-MMVD) and asymmetric DMVR (asy-DMVR) schemes, which are compatible with MMVD and DMVR in VVC respectively. Four asymmetric MV refinement templates are devised to capture varying motion scenes, and the optimal one is derived through bilateral matching and rate-distortion optimization. Moreover, meticulously designed fast algorithms are implemented to bypass unnecessary candidate evaluations, thereby effectively reducing both encoding and decoding complexities. The simulation result shows that on top of the VVC Test Model (VTM-22.1), ASMVR achieves 1.52% BD-rate saving for random access (RA).