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

摘要

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.