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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 5 — Jun. 6, 2013

Color-difference evaluation for digital images using a categorical judgment method

Haoxue Liu, Min Huang, Guihua Cui, M. Ronnier Luo, and Manuel Melgosa  »View Author Affiliations


JOSA A, Vol. 30, Issue 4, pp. 616-626 (2013)
http://dx.doi.org/10.1364/JOSAA.30.000616


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Abstract

The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor kL=1 to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize kL in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor kL=1. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A 25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor kL=2 in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas.

© 2013 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(110.3000) Imaging systems : Image quality assessment
(330.1690) Vision, color, and visual optics : Color
(330.1710) Vision, color, and visual optics : Color, measurement
(330.1730) Vision, color, and visual optics : Colorimetry
(330.1715) Vision, color, and visual optics : Color, rendering and metamerism

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: October 8, 2012
Revised Manuscript: December 22, 2012
Manuscript Accepted: January 27, 2013
Published: March 11, 2013

Virtual Issues
Vol. 8, Iss. 5 Virtual Journal for Biomedical Optics

Citation
Haoxue Liu, Min Huang, Guihua Cui, M. Ronnier Luo, and Manuel Melgosa, "Color-difference evaluation for digital images using a categorical judgment method," J. Opt. Soc. Am. A 30, 616-626 (2013)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-30-4-616

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