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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Image Compression Using Shannon Entropy-Based Image Thresholding
verfasst von : Karri Chiranjeevi, Uma Ranjan Jena, Asha Harika
Erschienen in: Computational Intelligence in Data Mining
Verlag: Springer Singapore
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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.