Title: Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimisation

Authors: Karri Chiranjeevi; Umaranjan Jena; M.V. Nageswara Rao

Addresses: Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhrapradesh, India ' Department of Electronics and Tele-communication Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla-768018, Odisha, India ' Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhrapradesh, India

Abstract: In this paper, teaching leaning based optimisation (TLBO) is used for maximising Shannon entropy or fuzzy entropy for effective image thresholding which leads to better image compression with higher peak signal to noise ratio (PSNR). The conventional multilevel thresholding methods are efficient when bi-level thresholding. However, they are computationally expensive extending to multilevel thresholding since they exhaustively search the optimal thresholds to optimise the objective functions. To overcome this drawback, a TLBO based multilevel image thresholding is proposed by maximising Shannon entropy or fuzzy entropy and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, PSNR, weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.

Keywords: image compression; image thresholding; Shannon entropy; fuzzy entropy; bat algorithm; teaching learning based optimisation.

DOI: 10.1504/IJAIP.2021.112904

International Journal of Advanced Intelligence Paradigms, 2021 Vol.18 No.2, pp.193 - 231

Received: 07 Mar 2017
Accepted: 28 Oct 2017
Published online: 09 Feb 2021 *

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