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

Implementation of an Image Compression ALgorithm

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 code, a histogram based image compression technique is implemented 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.
The algorithm is proposed in :
Paul, S.; Bandyopadhyay, B., "A novel approach for image compression based on multi-level image thresholding using Shannon Entropy and Differential Evolution," Students' Technology Symposium (TechSym), 2014 IEEE , vol., no., pp.56,61, Feb. 28 2014-March 2 2014
doi: 10.1109/TechSym.2014.6807914
Please cite this paper, if you use this code.