Upgrading color-difference formulas

Lissner, Ingmar; Urban, Philipp

doi:10.1364/josaa.27.001620

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…Studies show no significant difference in performance between dE CMC and CIEDE2000 or CIE94. [12][13][14][15][16][17][18] A small preference for CIEDE2000 was found in some other studies, 8,[19][20][21][22] but a statistically significant improvement was only reported in Refs. 11, 23. Inconsistencies in these results are partly due to the different compositions of the sample sets the equations were tested on.…”

Section: Different Measures For Expressing Tolerancesmentioning

confidence: 99%

Setting tolerances on color and texture for automotive coatings

Kirchner

Ravi²

2012

Color Research & Application

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In the automotive industry, color quality control is increasingly done by reflection measurements. We discuss how color tolerances are set in specifications to suppliers of add‐on parts and to paint suppliers. We mention several factors that often lead to unrealistically tight settings, and therefore to incorrect rejections and unnecessary high productions costs. We show that this is likely to occur when the dEab color difference equation is used, or when a strict criterion separating pass from fail is used instead of specifying a “grey area” where instrumental monitoring needs to be followed by visual assessments. Unrealistically, tight tolerances also result from halving tolerances in the supply‐customer chain in an attempt to compensate color variations due to uncontrolled application conditions. Tolerances should be widened further when a gap separates an add‐on part from the car body, making visual discrimination of color differences less critical. Other common situations where tolerances should be widened are the presence of visual texture in effect coatings, the lightness of metallic coatings becoming very high (L*> 100) and measurement geometries close to the gloss angle. Finally, we address the issue that instrumental color tolerances should not be tighter than what is allowed by instrumental reproducibility, repeatability, and inter‐instrument agreement. Accounting for these factors, we provide a set of reasonable values for tolerances on color and on visual texture parameters, based on our own practical experience. But realistic tolerance values depend very much on actual conditions, and should be agreed in tripartite discussions among automotive industry, suppliers of add‐on parts, and paint supplier. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 88–98, 2014

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Section: Different Measures For Expressing Tolerancesmentioning

confidence: 99%

Setting tolerances on color and texture for automotive coatings

Kirchner

Ravi²

2012

Color Research & Application

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“…To determine whether two formulas ∆Ea and ∆Eb are significantly different, one has to take the squared ratio of the corresponding STRESS values Sa and Sb on the same test data [11]:…”

Section: Resultsmentioning

confidence: 99%

Color discrimination on the border of photopic/mesopic vision

Vik¹,

Viková²,

Pechová³

2015

Proceedings of the 21st International Conference LIGHT SVĚTLO 2015

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“…Many different color difference formulas have been proposed. A number of quantitative studies showed that the most recent formulas yield more or less equal performance [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

How psychophysical methods influence optimizations of color difference formulas

Kirchner

Dekker

Lucassen³

et al. 2015

J. Opt. Soc. Am. A

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For developing color difference formulas, there are several choices to be made on the psychophysical method used for gathering visual (observer) data. We tested three different psychophysical methods: gray scales, constant stimuli, and two-alternative forced choice (2AFC). Our results show that when using gray scales or constant stimuli, assessments of color differences are biased toward lightness differences. This bias is particularly strong in LCD monitor experiments, and also present when using physical paint samples. No such bias is found when using 2AFC. In that case, however, observer responses are affected by other factors that are not accounted for by current color difference formulas. For accurate prediction of relative color differences, our results show, in agreement with other works, that modern color difference formulas do not perform well. We also investigated if the use of digital images as presented on LCD displays is a good alternative to using physical samples. Our results indicate that there are systematic differences between these two media.

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Upgrading color-difference formulas

Cited by 15 publications

References 20 publications

Setting tolerances on color and texture for automotive coatings

Setting tolerances on color and texture for automotive coatings

Color discrimination on the border of photopic/mesopic vision

How psychophysical methods influence optimizations of color difference formulas

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