Clustering based search algorithm for motion estimation

Motion estimation is the key part of video compression since it removes the temporal redundancy within frames and significantly affects the encoding quality and efficiency. In this paper, a novel fast motion estimation algorithm named Clustering Based Search algorithm is proposed, which is the first to define the clustering feature of motion vectors in a sequence. The proposed algorithm periodically counts the motion vectors of past blocks to make progressive clustering statistics, and then utilizes the clusters as motion vector predictors for the following blocks. It is found to be much more efficient for one block to find the best-matched candidate with the predictors. Compared with the mainstream search algorithms, this algorithm is almost the most efficient one, 35 times faster in average than the full search algorithm, while its mean-square error is even competitively close to that of the full search algorithm.