Phase Information and Space Filling Curves in Noisy Motion Estimation
Due to the temporal and spatial correlation of the image sequence, the motion vector of a block is highly related to the motion vectors of its adjacent blocks in the same image frame. If we can obtain useful and enough information from the adjacent motion vectors, the total number of search points used to find the motion vector of the block may be reduced significantly. Using that idea, an efficient gray prediction search (GPS) algorithm for block motion estimation is proposed in this paper. Based on the gray system theory, the GPS can determine the motion vectors of image blocks quickly and correctly. The experimental results show that the proposed algorithm performs better than other search algorithms, such as 3SS, CS, PHODS, 4SS, BBGDS, SES, and PSA, in terms of six different measures: 1) average mean square error per pixel; 2) average peak signal-to-noise ratio; 3) average prediction errors per pixel; 4) average entropy of prediction errors; 5) average percentage of unpredictable pels per frame; and 6) average search points per block.