Quality improvement of compressed color images by model-based chrominance estimation

Abstract

In compressed color images, colors are usually represented by luminance and chrominance components. Considering characteristics of human vision system, chrominance components are generally represented more coarsely than luminance component. Aiming at possible recovery of chrominance components, we propose a model-based chrominance estimation algorithm where color images are modeled by a Markov random field. Chrominance components of a pixel are estimated by maximizing a conditional probability density function given its luminance component and its neighboring color vectors. Experimental results show that the proposed chrominance estimation algorithm is effective for quality improvement of compressed color images such as JPEG and JPEG2000. © 2009 Springer-Verlag Berlin Heidelberg.