Flower pollination-based K-means algorithm for medical image compression

Abstract

Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.