Modified Cuckoo Search Algorithm for Motion Vector Estimation

Abstract

Motion estimation and motion compensation are the accepted process in H.264 and H.265 video coding standard to reduce temporal redundancy. Several fast block matching algorithms have been developed to reduce the calculation cost in the motion estimation process. But quick block matching algorithms often lead to a local minimum. Several researchers used different population-based nature-inspired algorithms to perform block matching. Algorithms like genetic algorithm, differential evolution, particle swarm optimization were used in numerous motion estimation algorithms. Different algorithms used a fitness approximation strategy to reduce computation cost. Jaya algorithm-based block matching is the most efficient block matching algorithm in the available literature. Jaya algorithm is free from algorithmic specific parameter which speeds up the process. This article proposes a few modifications to the traditional cuckoo search algorithm and then, a block matching algorithm was proposed based on the modified cuckoo search algorithm. Fitness approximation, adaptive termination, and zero motion prejudgment modules were used with the modified cuckoo search algorithm to reduce the number of redundant calculations. The performance of the proposed algorithm was compared with the exhaustive search algorithm and other benchmarking algorithms in terms of Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM), and average search point required to calculate a motion vector for a block. The proposed algorithm delivers better performance compared to the benchmarking algorithms.