A Computationally Adaptive Block-Matching Motion Estimation Algorithm

This paper describes a computationally adaptive motion estimation algorithm by exploiting the spatial-temporal motion correlation characteristics of the video sequence. In our approach, a set of initial candidate MV’s include center block and four neighboring blocks at the same location from the previous frame are used to track the motion of the current block. Among the set of initial MV candidates, the one with the smallest MAD is chosen as the reference MV candidate VR of the current block. We use the spatial correlation property between zero motion vector and reference vector VR for finding the best candidate MV and viewed as a starting point (i.e., new search center) for MV searching process. The proposed approach has been successfully tested with integer, as well as half-pixel accuracy and the results are encouraging. The experimental results reveal that the proposed algorithm saves up to 95 % of computations, as compared to FSBM and the estimation accuracy is very close to that of FSBM