Fast Motion Estimation Based on Search Range Adjustment Using Neighboring MVDs

In this paper, we propose a new adaptive search range motion estimation method for H.264/AVC where search ranges are adjusted by the probabilities of motion vector differences (MVDs). The MVDs are modeled as a discrete Laplace distribution and then its parameter is estimated by the maximum likelihood estimator. The MVDs of neighboring blocks are employed as the samples for the estimation. With the estimated distribution, the search ranges which include the correct MVDs for a prefixed probability are analytically determined. Since the proposed method handles the search ranges instead of search point sampling patterns, it provides very flexible and hardware-friendly approach in motion estimation. Experimental results show that it is very similar to the optimal method (full search algorithm) in PSNR but gives significant reduction in the computational complexity.