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Block Matching with Particle Swarm Optimization for Motion Estimation

Block matching algorithms successfully reduce the computational load of the motion estimation. Block based motion estimation algorithms rely on block matching of the macro blocks defined in the search windows. In this paper, particle swarm optimization (PSO) is applied on block-matching problem. Particle swarm optimization is a computationally efficient iterative search algorithm that finds the best matching block between two consecutive frames. Finding the moving block in the image sequence requires a minimization algorithm. Using particle swarm optimization, the computational cost of the block matching is aimed to decrease. Particle swarm optimization is a heuristic algorithm and necessarily dependent on the selections of the parameters assigned. In our case, the particle swarm algorithm is designed to work on a small subset of the population of blocks. The position of a macro block in the estimated image can be revealed by the motion vectors generated by the particle swarm optimized block.