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Fast block-matching algorithm using selective integral projections

Existing fast block motion estimation algorithms, which reduce the computation by limiting the number of search points, utilize the motion vector (MV) characteristics of high spatial correlation as well as center-biased distribution in predicting an initial MV. Even though they provide good performance for slow motion sequences, they suffer from poor accuracy for fast or complex motion sequences. In this paper, a new fast and efficient block motion estimation algorithm is proposed. To find an initial search point, in addition to the predictors of zero MV and neighboring MVs, the algorithm utilizes another predictor obtained from one-dimensional feature matching using selective integral projections. This low complexity procedure enables the selection of a better initial search point so that a simple gradient descent search near this point may be enough to find the global minimum point. Compared to recent fast search algorithms, the proposed algorithm has lower computational complexity and provides better prediction performance, especially for fast or complex motion sequences.