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Motion estimation is one of the major problems in developing video codingapplications. Among all motion estimation approaches, Block matching (BM)algorithms are the most popular methods due to their effectiveness andsimplicity for both software and hardware implementations. A BM approachassumes that the movement of pixels within a defined region of the currentframe (Macro-Block, MB) can be modeled as a translation of pixels contained inthe previous frame. In this procedure, the motion vector is obtained byminimizing the sum of absolute differences (SAD) produced by the MB of thecurrent frame over a determined search window from the previous frame. The SADevaluation is computationally expensive and represents the most consumingoperation in the BM process. The most straightforward BM method is the fullsearch algorithm (FSA) which finds the most accurate motion vector, calculatingexhaustively the SAD values for all elements of the search window. Over thisdecade, several fast BM algorithms have been proposed to reduce the number ofSAD operations by calculating only a fixed subset of search locations at theprice of a poor accuracy. In this paper, a new algorithm based on DifferentialEvolution (DE) is proposed to reduce the number of search locations in the BMprocess. In order to avoid computing several search locations, the algorithmestimates the SAD values (fitness) for some locations using the SAD values ofpreviously calculated neighboring positions. Since the proposed algorithm doesnot consider any fixed search pattern or other different assumption, a highprobability for finding the true minimum (accurate motion vector) is expected.In comparison to other fast BM algorithms, the proposed method deploys moreaccurate motion vectors yet delivering competitive time rates.

Block matching algorithm based on Differential Evolution for motion estimation