Paper
18 March 2005 Can gamut mapping quality be predicted by colour image difference formulae?
Author Affiliations +
Proceedings Volume 5666, Human Vision and Electronic Imaging X; (2005) https://doi.org/10.1117/12.587641
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
Abstract
Modern digital imaging workflows typically involve a large number of different imaging technologies and media. In order to assure the quality of such workflows, there is a need to quantify how reproduced images have been changed by the reproduction process, and how much these changes are perceived by the human eye. The goal of this study is to investigate whether current color image difference formulae can be used to this end, specifically with regards to the image degradations induced by color gamut mapping. We have applied image difference formulae based on CIELAB, S-CIELAB, and iCAM to a set of images, which have been processed by several state-of-the-art color gamut mapping algorithms. The images have also been evaluated by psychophysical experiments on a CRT monitor. We have not found any statistically significant correlation between the calculated color image differences and the visual evaluations. We have examined the experimental results carefully, in order to understand the poor performance of the color difference calculations, and to identify possible strategies for improving the formulae. For example, S-CIELAB and iCAM were designed to take into account factors such as spatial properties of human vision, but there might be other important factors to be considered to quantify image quality. Potential factors include background/texture/contrast sensitivity effect, human viewing behaviour/area of interest, and memory colors.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eriko Bando, Jon Yngve Hardeberg, and David Connah "Can gamut mapping quality be predicted by colour image difference formulae?", Proc. SPIE 5666, Human Vision and Electronic Imaging X, (18 March 2005); https://doi.org/10.1117/12.587641
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Cited by 9 scholarly publications.
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KEYWORDS
Image quality

Cameras

CRTs

Image processing

Eye

Colorimetry

Digital image processing

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