A measure for evaluation of the information content in color images

Abstract

The Kullback-Leibler (KL) divergence, a fundamental concept in information theory used to quantify probability density differences, is herein employed in assessing the color content of digital images. For this purpose digital images are encoded in the CIELAB color space and modeled as discrete random fields, which are assumed to be described sufficiently by three-dimensional probability density functions. Subsequently, using the KL divergence, a global quality assessment of an image is presented as the information content of the CIELAB encoding of the image relative to channel capacity. This is expressed by an image with "maximum realizable color information (MRCI)" defined in this paper. Additionally, one-dimensional estimates of the marginal distributions in luminance, chroma, and hue are explored, and the proposed quality assessment is examined relative to KL divergences based on these distributions. The proposed measure is tested using color images, pseudocolor representations and different renderings of the same scene.