An Efficient Bidirectional Frame Prediction Using Particle Swarm Optimization Technique

This paper presents a Novel Bidirectional motion estimation technique, which is based on the Particle swarm optimization algorithm. Particle swarm optimization (PSO) is a population based optimization technique which has the potentiality to avoid local minima solution which is usually encountered by the traditional block matching algorithms (BMA) such as the three step search (TSS) and the diamond search (DS). To speed up the search, static macro blocks are found in our method, which is particularly beneficial to those video sequences containing small motion contents. Skipping such static macro blocks from processing can save the computation time and memory also. In the proposed method each time we are finding the best matching macro block in two frames at a time so we can reduce the number of error function calculations and it is faster than if we apply PSO technique to find forward motion vector and backward motion vector separately. The proposed method is applied to a number of benchmark video sequences and the results are compared with those obtained by applying the existing methods. Simulation results shows that the proposed algorithms gives the close match of PSNR values when compared to joint search algorithm with DS. Thus, PSO algorithm for Bidirectional motion estimation is empirically given to reduce computational complexity.