Performance analysis of MATLAB parallel computing approaches to implement genetic algorithm for image compression

Abstract

This chapter presents how to use parallel computing approaches from MATLAB Parallel Computing Toolbox to implement genetic algorithm for fractal image compression. These approaches are: ParFor, CoDistributor and Parallel Cluster. This is done to decrease processing time as possible as and maintaining reconstructed image quality. Many experiments were executed with comparisons between the three approaches. The experimental results showed that decreasing the GA population size and increasing number of workers used for the three parallel computing approaches can reduce the compression time. Best results obtained from implementing parallel approaches with 6 workers and 150 population size. The execution speed reached 4, CR reached 90.97 % and PSNR reached 34.98 db. At the same time, best results obtained from Parallel Cluster approach and then from CoDistributor approach.