Analysis of five sets of color difference data

Kuehni, Rolf G.

doi:10.1002/1520-6378(200104)26:2<141::aid-col1005>3.0.co;2-m

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Unfortunately, the issue of how to account for sample separation when deriving visual thresholds of color differ-ences is still not resolved. 37 In the past, several studies have been published on this issue, but no consensus was reached. One common result of the studies that has been published, however, is that the presence of a gap generally leads to less tight color tolerances.…”

Section: The Gap Effectmentioning

confidence: 99%

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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: The Gap Effectmentioning

confidence: 99%

“…Unfortunately, the issue of how to account for sample separation when deriving visual thresholds of color differences is still not resolved . In the past, several studies have been published on this issue, but no consensus was reached.…”

Section: Some Current Issues In Setting Qc Color Tolerancesmentioning

confidence: 99%

Setting tolerances on color and texture for automotive coatings

Kirchner

Ravi²

2012

Color Research & Application

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“…Textile samples, typically compared juxtaposed, exhibit a moderate level of lightness crispening. The effect has recently been described quantitatively for colour difference data by Lübbe [23] and Kuehni [24] but has not yet become a component of commonly used colour difference formulas despite the fact that there is no effective local or global industry standardisation of viewing surround conditions.…”

Section: Lightness Contrastmentioning

confidence: 99%

Colour difference formulaes; accurate enough for objective colour quality control of textiles?

Kuehni

2003

Coloration Technology

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The best formulas for predicting mean perceived small colour differences of industrially produced coloured goods have an average accuracy of about 65%. This should be considered unsatisfactory. Factors that influence accuracy are discussed in this paper and a proposal is made for determining if the accuracy can be significantly improved as a preliminary step toward developing more reliable data and a formula to predict it.

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“…Since the late 1980s, visual experiments have been performed to develop different suprathreshold color-difference datasets to be used for improving the relationship between visual and numerical color difference evaluation [1][2][3][4][5][6] . Based on the results of these experiments several datasets have been developed such as: the CMC dataset (MacDonald, 1984), the BFD dataset (Luo et Rigg, 1987), the RIT-DuPont dataset (Berns, 1991), the LCD dataset (Kim et Nobbs, 1997) and the Witt dataset (Witt, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Experimental studies showed that LCD and CRT display can be used for virtual proofing and that they can be accurately colorimetrically characterized. 6,11 Our experimental tests were done under illuminating/viewing conditions close to the ''reference conditions'' suggested for the CIEDE2000 color-difference formula (CIE Publication 142-2001) and by the ISO standard 3668:2003 (ISO standard 3668:2003 Paints and varnishesVisual comparison of the colour of paints). In our study we controlled viewing conditions including the spectral power distribution of the viewing environment and the spectral reflectance factors of surface specimens.…”

Section: Introductionmentioning

confidence: 99%

Supplementary dataset for color difference evaluation

2010

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The Technical Committee 1-55 of the CIE on ''Uniform color space for industrial color difference evaluation'' requested in 2007 reliable experimental datasets (that is, color pairs assessed by a considerable number of observers with nondefective color vision, under well controlled experimental conditions and using proper experimental methods). The aim was to use these datasets to develop/test new color spaces with Euclidean color-difference formulas more robust than the CIEDE2000 color-difference formula. The objective of this paper is to propose a new dataset more reliable than previous datasets and to evaluate from this dataset more accurate just noticeable differences. In our study we have considered not only surface specimens (reflective objects), but also mainly color pairs assessed using visual displays (e.g., self-luminous LCD). Our experimental results have been obtained under illuminating/viewing conditions close to the ''reference conditions'' suggested for the CIEDE2000 color-difference formula (CIE Publication 142-2001) and by the ISO standard 3668:20033668: (ISO standard 3668:2003 Paints and varnishes -Visual comparison of the colour of paints).

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Analysis of five sets of color difference data

Cited by 9 publications

References 26 publications

Setting tolerances on color and texture for automotive coatings

Setting tolerances on color and texture for automotive coatings

Colour difference formulaes; accurate enough for objective colour quality control of textiles?

Supplementary dataset for color difference evaluation

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