Adaptive Multi-Reference Downhill Simplex Search Based on Spatial-Temporal Motion Smoothness Criterion

Wei-Hau Pan, Chen-Kuo Chiang, Shang-Hong Lai

Abstract: Multi-reference frame motion estimation improves the accuracy of motion compensation in video coding. However, it also increases computational complexity dramatically. In this paper, we propose a different approach for multi-reference motion estimation via downhill simplex search. Additionally, an adaptive reference frame selection algorithm is developed based on spatial and temporal smoothness of motion vectors. We first apply single-reference downhill simplex search to the previous frame. Then, temporal smoothness of motion vectors in collocated blocks is calculated to decide the number of reference frames to be included for motion estimation. Spatial smoothness of motion vectors in the neighboring blocks is used as a criterion for termination. Experimental results show that the proposed algorithm provides better PSNR than that of original multi-reference downhill simplex search in all testing sequences with similar computational speed. In addition, it outperforms several representative single-reference frame block matching methods in terms of estimation speed and coding quality.