Optimal Image Compression based on Hybrid Bat Algorithm and Pattern Search


V. Manohar,G.Laxminarayana,




Bat algorithm,Pattern Search,Image compression,Thresholding,Shannon entropy,Fuzzy entropy,


In this paper, multilevel image thresholding for image compression is proposed for the first time using Shannon entropy and Fuzzy entropy, which are maximized by the nature-inspired hybrid Bat algorithm and Pattern Search (hBA-PS).The ordinary thresholding method gives high computational complexity, but while extending for multilevel image thresholding, the optimization techniques are needed in order to reduce the computational time. Particle Swarm Optimization (PSO) and FA (Firefly Algorithm) undergo instability when the particle velocity is maximum. It is evident that Bat Algorithm (BA) is good in exploitation whereas Pattern Search (PS) is good in exploration. We hybridized the BA and PS based on their strengths and weaknesses. The proposed technique (hBA-PS) is compared with Differential Evolution (DE), PSO and BA for which the experimental results are compared in terms of Standard deviation, Computational time, Peak Signal to Noise Ratio (PSNR), Weighted PSNR and Reconstructed image quality. The performance of the proposed algorithm is found to be better with Fuzzy entropy compared to Shannon.


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