Proceedings of International conference on Intelligent Computational Systems (ICICS’2011) Abstract—Color quantization is a process that reduces the distinct colors used in an image. The main objective of quantization should be such that it must not cause the loss of visual information from the image but reduces its memory requirements. In this paper the color quantization in LAB color space using PSO is done. Particle swarm optimization (PSO) is an evolutionary computation technique developed through a simulation of simplified social models. PSO is based on swarms such as fish schooling and bird flocking. [2].The LAB color model based clustering is used. In our present work first of all we will find the color image which is to be quantized. Then color map will be created where a small set of colors is chosen from all possible combination in Lab color space. Then the proposed algorithm will be applied to get the optimized solution. Keywords— Color quantization, lab color space clustering, particle swarm optimization. I. INTRODUCTION OLOR image quantization is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image. Consider a color image I and let us denote by C the set of its color and by N the cardinal of C. The quantization of I into K colors, with K < N (and usually K << N) consists in selecting a set of K representative colors and replacing the color of each pixel of the original image by the “closest” representative color [1]. Color quantization is critical for displaying images with many colors on devices that can only display a limited number of colors, usually due to memory limitations, and enables efficient compression of certain types of images. The ultimate goal of quantization is to change the color resolution of an image (number of bits in the color representation) with minimum distortion. This problem involves a variety of theoretical issues, related with color perception and optimization methods. The complexity of the problem makes the computation of an optimal solution is not feasible in general. For this reason, existing quantization Ravneet Kaur is Student in Department of Computer Science, Rayat Institute of Engineering & Information Technology, Punjab, India. Dr. Parvinder S. Singh is Director-Principal in Rayat & Bahra Institute of Engineering & Biotechnology, Mohali, India; (e-mail: parvinder.sandhu@gmail.com). Ms. Surbhi Gupta is Senior Lecturer, Department of Computer Science & Engineering, Rayat Institute of Engineering & Information Technology, Punjab, India. methods usually produce only approximate results [11]. For color quantization one has to choose appropriate color space and metrics. The basic idea is that we should choose a color space and an appropriate metric which would avoid merging dissimilar colors or separating perceptually close colors. A. LAB color space A Lab color space is a color-opponent space with dimension L for lightness and a and b for the color-opponent dimensions, based on nonlinearly compressed CIE XYZ color space coordinates. Unlike the RGB and CMYK color models, Lab color is designed to approximate human vision. It aspires to perceptual uniformity, and its L component closely matches human perception of lightness. It can thus be used to make accurate color balance corrections by modifying output curves in the a and b components, or to adjust the lightness contrast using the L component. In RGB or CMYK spaces, which model the output of physical devices rather than human visual perception, these transformations can only be done with the help of appropriate blend modes in the editing application. Because Lab space is much larger than the gamut of computer displays, printers, or even human vision, a bitmap image represented as Lab requires more data per pixel to obtain the same precision as an RGB or CMYK bitmap. Additionally, many of the "colors" within Lab space fall outside the gamut of human vision, and are therefore purely imaginary; these "colors" cannot be reproduced in the physical world. Though color management software, such as that built in to image editing applications, will pick the closest in-gamut approximation, changing lightness, colorfulness, and sometimes hue in the process. B. Particle Swarm Optimization technique In PSO a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually the swarm as a whole, like a flock of birds collectively foraging for food, is likely to move close to an optimum of the fitness function. Each individual in the particle swarm is composed of three D-dimensional vectors, where D Optimization Color Quantization in L*A*B* Color Space Using Particle Swarm Optimization Ravneet Kaur, Surbhi Gupta, Parvinder S. Sandhu C