Hierarchical motion estimation algorithm based on maximum a posteriori probability

Dooseop Choi, Taeg-Hyun An, Taejeong Kim

Abstract: In this paper, a hierarchical motion estimation (ME) algorithm is proposed for motion-compensated frame interpolation. The algorithm estimates the true motion vector field (MVF) of a video frame from its candidate MVFs, which are the results of full-search block-matching that utilizes multiple block sizes, by maximizing the posterior probability for the true MVF. Owing to probabilistic models utilized for defining the posterior probability, the estimate of the true MV of a pixel block is not only largely affected by the most reliable MV among the candidate MVs but also effectively corrected by its spatially neighboring MVs when all the candidate MVs turn out to be unreliable. Experimental results showed that the proposed algorithm outperforms existing hierarchical ME algorithms in terms of the quality of interpolated frames.