A novel approach for image compression based on multi-level image thresholding using Shannon Entropy and Differential Evolution

Image compression is one of the most important step in image transmission and storage. Most of the state-of-art image compression techniques are spatial based. In this paper, a histogram based image compression technique is proposed based on multi-level image thresholding. The gray scale of the image is divided into crisp group of probabilistic partition. Shannon's Entropy is used to measure the randomness of the crisp grouping. The entropy function is maximized using a popular metaheuristic named Differential Evolution to reduce the computational time and standard deviation of optimized objective value. Some images from popular image database of UC Berkeley and CMU are used as benchmark images. Important image quality metrics-PSNR, WPSNR and storage size of the compressed image file are used for comparison and testing. Comparison of Shannon's entropy with Tsallis Entropy is also provided. Some specific applications of the proposed image compression algorithm are also pointed out.