Enhanced Hybrid Cat Swarm Optimization Based on Fitness Approximation Method for Efficient Motion Estimation

Abstract

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the mean square error (MSE). Unfortunately, the MSE evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. Recently, several fast BM algorithms have been proposed to reduce the number of MSE operations by calculating only a fixed subset of search locations at the price of poor accuracy. The parallel cat swarm optimization (PCSO) & enhanced parallel cat swarm optimization (EPCSO) methods are an optimization algorithms designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. In this paper, a new algorithm based on Hybrid Cat Swarm Optimization (HCSO) is proposed to reduce the number of search locations in the BM process. In proposed algorithm, the computation of search locations is drastically reduced by adopting a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational time and find the optimal solutions in a very short time.