Abstract
In this paper, we proposed multilevel image thresholding for image compression using Shannon entropy which is maximized by the nature-inspired Bacterial Foraging Optimization Algorithm (BFOA). Ordinary threading methods are computationally expensive, while extending for multilevel image thresholding, so there is a need of optimization techniques to reduce the computational time. Particle swarm optimization undergoes instability when particle velocity is maximum. So we proposed a BFOA-based multilevel image thresholding by maximizing Shannon entropy and the results are compared with differential evolution and Particle swarm optimization and proved better in Peak signal-to-noise ratio (PSNR), Compression ratio and reconstructed image quality.
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Chiranjeevi, K., Jena, U.R., Harika, A. (2017). Image Compression Using Shannon Entropy-Based Image Thresholding. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_10
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DOI: https://doi.org/10.1007/978-981-10-3874-7_10
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