Measuring Color-Match Perception

Automotive "aftermarket" paint is used to repaint damaged cars. Customers must be assured that the paint matches their car. Spectroscopic analysis and expert judgment are used for good matching, but using judges is expensive. So The Sherwin-Williams Co. decided to investigate how successfully spectroscopic analysis predicts the customer perception of color matching. To do this, for each test color, the Delta E spectral index was compared with the Rasch color-match difference measure.

15 judges rated the differences between 67 test colors and the corresponding standard colors in 5 color groups: white, beige, blue, red, and green. Spectral data for each standard-to-test color-match difference were summarized in a Delta E index. The judges rated the goodness of the color match on a 4 point scale: 1. Obvious difference. 2. Noticeable difference. 3. Disguiseable difference. 4. No noticeable difference. From these ratings were constructed measures of standard-to-test color- match difference and measures of judge color-match perception sensitivity.

Difficulty of matching color-groups

There were consistent differences across color groups in the difficulty of matching the colors. White, beige, and red were easiest to match. Blue was intermediate. Green was hardest to match and hardest for judges to agree on. Though the finding for green is based only on two green shades and may be an artifact of the chosen standard and test colors, it challenges the common conviction that blues are hardest to match.

Color-group effects on judgments

Each judge was found to have a substantively different overall color-match sensitivity. This sensitivity must be calibrated and adjusted for in order to place all color-match differences on a common scale. But any considerable idiosyncratic change in a judge's color-match sensitivity across color groups would invalidate such an overall adjustment and limit the generality of any resulting color-match criteria. Consequently, each judge's overall sensitivity calibration was compared to that judge's sensitivity for each color-group. Few judge color-group sensitivity measures were significantly different from the judge's overall measure, indicating that color- match perception within judges is usefully stable across color groups. In practice, judge performance must be continuously monitored to insure that consistency is maintained.

Improving spectral criteria

Comparisons of the Delta E and judged color-match difference measures are in the Figures for four of the five color groups. The fifth group, green, is represented by only three data points, too few to make a picture. In each plot, the horizontal axis is the Delta E index of the similarity of the test color and the standard color, values to the left of 0.5 are generally thought to be acceptable. The vertical axis is the judged measure of the same difference.

Judge perception is intended to resemble customer perception, so the vertical placement of points is decisive. Above 1.5 logits, there is no perceptible difference between the colors. Since customer complaints are more expensive than blending a better matching color, optimal Delta E values would eliminate judged differences below the 1.5 logit line.

The plots show that the conventional 0.5 Delta E criterion for acceptable color- match is inadequate. Though perception measures and the Delta E index are correlated in all plots, 0.5 Delta E rejects effectively perfect perceptual matches for all four color-groups. Perceptually imperfect matches are accepted in two groups. For red, 0.5 Delta E rejected all matches except one, and so had little opportunity to yield false matches. Delta E is most satisfactory for white, and not grossly misleading for beige. It accords with the common belief that a blue match is hardest to detect.

It was concluded that 1) an improved spectroscopic technique is needed before expert judgment can be replaced, and 2), since expert judgment remains crucial, adjustment must be made for judge color-match sensitivity, and its variation monitored.

Thomas K. Rehfeldt

Measuring color-match perception. Rehfeldt TK. Rasch Measurement Transactions 1993 7:3 p.304

Measuring color-match perception. Rehfeldt TK. … Rasch Measurement Transactions, 1993, 7:3 p.304

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Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn. George Engelhard, Jr. & Jue Wang	Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene	Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver	Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone	Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes	Statistical Analyses for Language Testers (Facets), Rita Green	Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind	Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M	Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
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Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar	Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch	Rasch Models for Measurement, David Andrich	Constructing Measures, Mark Wilson	Best Test Design - free, Wright & Stone Rating Scale Analysis - free, Wright & Masters
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