1 October 2005 Assessing information content in color images
Author Affiliations +
J. of Electronic Imaging, 14(4), 043007 (2005). doi:10.1117/1.2137641
Abstract
The Kullback-Leibler (KL) divergence, which is a fundamental concept in information theory used to quantify probability density differences, is 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 3-D 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), which we define. Additionally, 1-D 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 various color images, pseudocolor representations and different renderings of the same scene. Test images and a MATLAB implementation of the measure are available online at http://www.ellab.physics.upatras.gr/PersonalPages/VTsagaris/research.htm.
Vassilis Tsagaris, Vassilis Anastassopoulos, "Assessing information content in color images," Journal of Electronic Imaging 14(4), 043007 (1 October 2005). http://dx.doi.org/10.1117/1.2137641
JOURNAL ARTICLE
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KEYWORDS
3D image processing

RGB color model

Image fusion

Image processing

Distortion

Synthetic aperture radar

3D modeling

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