Abstract
Block-matching motion estimation algorithms (BMAs) are widely used to eliminate temporal redundancies for video coding. For BMAs, there is an implicit assumption that the motion within each block is uniform. It is not always valid if the fixed block size is not approximate to the real object in an image. Then the block effect will be noticeable and the quality of the prediction suffers. In this paper, the block-classified motion estimation algorithm is presented. The proposed algorithm classifies the frame into stationary and moving object blocks. The object blocks are then adaptively segmented into different regions according to their motion and edge characteristics. The proposed method can estimate the edge blocks accurately. Experimental results show that this scheme has better performance in terms of objective and subjective measures than the full search and variable block-size quadtree segmentation motion estimation algorithms.
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