  Motion estimation is one of the major problems in developing video coding
applications. Among all motion estimation approaches, Block matching (BM)
algorithms are the most popular methods due to their effectiveness and
simplicity for both software and hardware implementations. A BM approach
assumes that the movement of pixels within a defined region of the current
frame (Macro-Block, MB) can be modeled as a translation of pixels contained in
the previous frame. In this procedure, the motion vector is obtained by
minimizing the sum of absolute differences (SAD) produced by the MB of the
current frame over a determined search window from the previous frame. The SAD
evaluation is computationally expensive and represents the most consuming
operation in the BM process. The most straightforward BM method is the full
search algorithm (FSA) which finds the most accurate motion vector, calculating
exhaustively the SAD values for all elements of the search window. Over this
decade, several fast BM algorithms have been proposed to reduce the number of
SAD operations by calculating only a fixed subset of search locations at the
price of a poor accuracy. In this paper, a new algorithm based on Differential
Evolution (DE) is proposed to reduce the number of search locations in the BM
process. In order to avoid computing several search locations, the algorithm
estimates the SAD values (fitness) for some locations using the SAD values of
previously calculated neighboring positions. Since the proposed algorithm does
not consider any fixed search pattern or other different assumption, a high
probability for finding the true minimum (accurate motion vector) is expected.
In comparison to other fast BM algorithms, the proposed method deploys more
accurate motion vectors yet delivering competitive time rates.
