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ABSTRACT
Image compression is very significant process in image transmission at high data rate over a communication channel and to increase the storage capacity of storage device. Ordinary image thresholding is a class of clustering technique used for image compression because of its simplicity, robustness and accuracy but it is computationally expensive when extending for multilevel image thresholding. An attempt is made in this paper to reduce the computational time of multilevel image thresholding using hybrid gravitational search algorithm and pattern search (hGSA-PS) by optimising a criterion such as Shannon entropy or Fuzzy entropy for seeking appropriate threshold values. From literature, gravitational search algorithm (GSA) is designed to explore the global search space (exploitation), and pattern search (PS) is designed to exploit a local search space (exploration), so we hybridise the GSA and PS to achieve exploitation and exploration of search space by incorporating strengths and weakness of both, and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, peak signal-to-noise ratio (PSNR), weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.
Acknowledgements
This work is supported by Ministry of Human Resource and Management (MHRD), Govt. of India and Management of GMR Institute of Technology, Rajam, Andhra Pradesh. The authors would like to thank the anonymous editor for accepting the manuscript and reviewers for their valuable comments and necessary suggestions those have helped to give more clarity to the paper.