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

Authors: G. Vimala Kumari; G. Sasibhushana Rao; B. Prabhakara Rao

Addresses: M.V.G.R. College of Engineering, Vizianagaram-535005, India ' Andhra University College of Engineering, Visakhapatnam-530003, India ' Jawaharlal Nehru Technological University, Kakinada, Kakinada-533003, Andhra Pradesh, India

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.

Keywords: image compression; particle swarm optimisation; quantum particle swarm optimisation; flower pollination algorithm.

DOI: 10.1504/IJAIP.2021.112903

International Journal of Advanced Intelligence Paradigms, 2021 Vol.18 No.2, pp.171 - 192

Received: 22 Feb 2017
Accepted: 28 Oct 2017
Published online: 09 Feb 2021 *

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