Vector quantization using whale optimization algorithm for digital image compression

Today, much of the information is storing in images. To transfer information in the form of images, image compression is required. Compressing images reduces the size of images and sends them faster over the network. One of the most methods of image compression is the vector quantization. For vector quantization compression, the codebook is using in cryptography and decryption. The vector quantization compression method typically uses codebooks that are not optimized, which reduces the compression quality of the images. Choosing the optimal codebook makes compression of images with higher quality. Choosing the optimal codebook is a difficult optimization problem and therefore requires intelligent algorithms to solve it. In this paper, the whale optimization algorithm is used to find the optimal codebook in image compression. Whale Optimization Algorithm has different search strategies and is an ideal algorithm for finding the optimal codebook in images compression. Implementation of the proposed algorithm for compression on several standard images shows that the proposed method compresses images with appropriate quality. The proposed method performs more efficient compression than the proposed algorithms such as particle swarm optimization, bat, and firefly algorithms. The signal-to-noise ratio of the proposed method is higher than the compared methods. Experiments on a set of standard images show the proposed method compared to the Fire Fly, Bat, and Differential evolution, Improved Particle Swarm Optimization, and Improved Differential Evolution methods with a compression execution time of 60.48 and 10.21, respectively. , 4.79, 5.09 and 3.94 decreased. The proposed method in compression has a higher PSNR index of about 17 than the Linde-Buzo-Gray method.