Motion Estimation Algorithm Using Block-Matching and Harmony Search Optimization

Abstract

Motion estimation is one of the major problems in developing video coding applications. Motion estimation is one of the major problems in developing video coding applications. On the other hand, 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 can be modeled as a translation of pixels contained in the previous frame. During this procedure is obtained a motion vector by minimizing a certain matching metric that is produced between the current frame and the previous frame. However, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. Harmony search (HS) algorithm is a metaheuristic optimization method inspired by the music improvisation process, in which a musician polishes the pitches to obtain a better state of harmony. In this chapter, a BM algorithm that combines HS with a fitness approximation model is presented. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to minimize computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such a fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The presented method has been compared to other BM algorithms in terms of velocity and coding quality and its experimental results demonstrate that the algorithm exhibits the best balance between coding efficiency and computational complexity.