A comparison of constant stimuli and gray‐scale methods of color difference scaling

Two psychophysical techniques, the method of constant stimuli and the gray‐scale comparison method, were used to determine color tolerances for three different color centers in the hue, chroma, and lightness directions in CIELAB color space. The same color‐difference pairs were used as the stimuli in both experiments. Although the results followed the same trends, they were different for the two techniques. Based on comparison of the validity and precision of the results, as well as the ease of implementation, use, and analysis, the method of constant stimuli is the preferable method. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 36–44, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Investigation of texture effect on visual colour difference evaluation

The texture effect on visual colour difference evaluation was investigated in this study. Five colour centers were selected and textured colour pairs were generated using scanned textile woven fabrics and colour‐mapping technique. The textured and solid colour pairs were then displayed on a characterized cathode ray tube (CRT) monitor for colour difference evaluation. The colour difference values for the pairs with texture patterns are equal to 5.0 CIELAB units in lightness direction. The texture level was represented by the half‐width of histogram, which is called texture strength in this study. High correlation was found between texture strength and visual colour difference for textured colour pairs, which indicates that an increasing of 10 units of texture strength in luminance would cause a decreasing of 0.25 units visual difference for the five colour centers. The ratio of visual difference between textured and solid colour pairs also indicates a high parametric effect of texture. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 341–347, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Correlation between visual and colorimetric scales ranging from threshold to large color difference

Psychophysical experiments of color discrimination threshold and suprathreshold color‐difference comparison were carried out with CRT‐generated stimuli using the interleaved staircase and constant stimuli methods, respectively. The experimental results ranged from small (including threshold) to large color difference at the five CIE color centers, which were satisfactorily described by chromaticity ellipses as equal color‐difference contours in the CIELAB space. The comparisons of visual and colorimetric scales in CIELAB unit and threshold unit indicated that the colorimetric magnitudes typically were linear with the visual ones, though with different proportions in individual directions or color centers. In addition, color difference was generally underestimated by the Euclidean distance in the CIELAB space, whereas colorimetric magnitude was perceptually underestimated for threshold unit, implying the present color system is not a really linear uniform space. Furthermore, visual data were used to test the CIELAB‐based color‐difference formulas. In their original forms CIEDE2000 performed a little better than CMC, followed by CIELAB, and with CIE94 showing the worst performance for the combined data set under the viewing condition in this study. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 349–359, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10081     

Determination of constant Hue Loci for a CRT gamut and their predictions using color appearance spaces

A colorimetrically characterized computer-controlled CRT display was used to determine 24 loci of constant perceived hue for pseudo-object related stimuli, sampling the display's interior color gamut at constant lightness and the edge of its gamut at variable lightness. Nine observers performed three replications generating matching data at 132 positions. the constant hue loci were used to evaluate the correlation between perceived hue and hue angle of CIELAB, CIELUV, Hunt, and Nayatani color appearance spaces. the CIELAB, CIELUV, and Hunt spaces exhibited large errors in the region of the blue CRT primary, while the Nayatani and CIELUV spaces produced large errors in the region of the red primary for constant lightness stimuli. Along the edge of the CRT's color gamut (variable lightness stimuli), all the spaces had a similar trend, large errors in the cyan region. the differences in performance between the four spaces were not statistically significant for the constant lightness stimuli. For the variable lightness stimuli, CIELAB and CIELUV had statistically superior performance in comparison with the Nayatani space and equal performance in comparison with the Hunt space. It was concluded that for imaging applications, a new color appearance space needs to be developed that will produce small hue error artifacts when used for gamut mapping along loci of constant hue angle. © 1995 John Wiley & Sons, Inc.     

Visual determination of hue suprathreshold color‐difference tolerances using CRT‐generated stimuli

Suprathreshold hue color‐difference tolerances were measured at four color centers using CRT‐generated stimuli. The tolerances, defined using CIELAB, were measured using two different methods of presentation. In the Absolute Experiment, the stimuli were presented at luminance levels that matched those of the previous object‐color experiments, so that the CRT stimuli were nearly metameric to the originals. In the Relative Experiment, the white point of the monitor was defined as L* = 100 at a corresponding chromaticity to the object‐color viewing environment, but at a lower luminance level. The results from these two experiments followed the same general trends; however, they were significantly different from each other for three of the four color centers. The same trends were seen in the object‐color results, although neither CRT experimental condition produced tolerances that were conclusively more similar to the object‐color results than the other. The feasibility of the use of the CRT has been demonstrated. It is likely that parametric effects of stimulus presentation are the cause of the differences in results among the different experiments, as opposed to differences in the mode of appearance. These parametric effects can be studied more quickly and economically using a computer‐controlled CRT display. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 164–176, 1999     

On the relations between color,gloss, and surface texture in injection‐molded plastics

The relation between color and gloss of injection‐molded plastic specimens was evaluated by means of a 45°/0° geometry spectrophotometer and a glossmeter. The specimens were plaques having one smooth, glossy field and one rougher, textured region made of two different polymeric materials (acrylonitrile‐butadiene‐styrene and polypropylene) in a range of colors. A significant influence of the lightness L* of the specimens on the measured gloss of the textured field, with a low gloss appearance, was found. This can probably be attributed to a contribution from bulk scattering, which is linked to the reflectance from within the specimen. The influence of texture on the measured color of the plaques was assessed by evaluating the color difference between the smooth field and the more textured area on the specimens. When the surface was textured, the color changed; in general it became lighter and less saturated. The magnitude of the change depended on the color of the material. The darker the material was, the larger was the increase in lightness L* as the surface became rougher. The shift in chroma C* was determined not only by the saturation but also by the lightness of the specimens. Darker specimens exhibited a larger decrease in chroma when the surface was textured. The change in the measured color caused by increasing the surface roughness could be predicted in a satisfactory manner using a model developed for xerographic printing paper. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 291–298, 2009     

Setting tolerances on color and texture for automotive coatings

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 dE_ab 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     

Whiteness,chromaticness, and blackness symmetries for CIELAB,with a numerical example

Whiteness, chromaticness, and blackness are defined for CIELAB. These NCS‐like color attributes offer an alternative to lightness and chroma for describing color. Their hue‐preserving symmetries are derived for tristimulus color space. A numerical example provides what theory predicts are visually uniform sequences of colors with constant lightness, whiteness, chromaticness, or blackness. Numerical approximation is unnecessary. Such sets of symmetric colors in one hue are visually interesting, and useful for computer aided design. The appropriateness of such attributes for CIELAB is briefly discussed. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

The ULAB colour space

A new colour space, named ULAB, is developed. It is derived from the CIELAB colour space and can be converted to and from CIELAB. Unlike modified CIELAB colour‐difference formulae, ULAB incorporates corrections for lightness, chroma, and hue differences into its colour coordinates. For the small magnitude colour difference data, it shows the performance as good as more complicated formulae such as CIEDE2000. ULAB shows another chance of developing a colour space approximately more uniform than CIELAB. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 17–29, 2015     

Visual determination of suprathreshold color-difference tolerances using probit analysis

As part of a research program to improve the relationship between visual and numerical color-difference evaluation for industrial colorimetry, a color-difference tolerance data set for fitting and testing of color-difference metrics has been extended to include 156 individual color-tolerance determinations. These tolerances were designed to sample 19 color centers over a surface color gamut with balanced sampling of lightness and chromaticness differences. The tolerance determination procedures emphasized accurate estimation of population visual color-difference response and rigorous estimation of tolerance precision. Tolerance accuracy was confirmed by excellent agreement of these results and the majority of previous experiments on five color centers selected for CIE color-difference evaluations. The average uncertainty of the tolerance determinations was ± 11% of the tolerance value at a 2 ó level (95% confidence interval). The completed data set is suitable for estimating the parameters of color-difference metrics or testing the performance of such metrics. The color tolerances indicated the systematic lack of uniformity of the CIELAB space, in general agreement with previous experiments. A simple modification of the CIELAB color-difference metric was shown to account for much of the systematic lack of uniformity.     

Research on color harmony of building façades

In this study, the 28 primary colors and 11 complementary colors suggested by Chang et al in their investigation on building colors in Wanhua District of Taipei City were taken as color samples. The two-color combination mode was adopted to obtain 308 simulation photos, and two-color harmony was discussed from the perspective of visual evaluation using psychophysical tests. This study explored building façade color harmony in the CIELAB color space, and the relationship between the color attributes (hue, lightness, and chroma) and the color harmony, and between the differences of the color attributes and color harmony. It found that a high lightness of a building's primary color is associated with a high level of building color harmony, while the color harmony is reduced when the color falls in the green or blue sector in the CIELAB color space; a greater lightness difference between building façade colors is associated with a higher level of building color harmony, while the colors are disharmonized when they tend to the blue sector in the CIELAB color space. The contribution of this study is to summarize the principles for the application of building color harmony in urban renewal, and proposed suggestions on building color harmony in the urban renewal process.     

A preliminary comparison of CIE color differences to textile color acceptability using average observers

Ninety‐six nylon pairs were prepared, including red, yellow, green, and blue standards, each at two lightness levels with CIE94 ΔE units ranging from 0.15 to 4.01. Visual assessments of acceptability were carried out by 21 females. Logistic regression compared visual results to four color‐difference equations, CIELAB, CMC, CIE94, and CIEDE2000. It was found that CMC most closely represented judgments of average observers. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 288–294, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20124     

Setting up acceptability tolerances: A case study

Current acceptance of goods for color by the United States Army depends on visual comparison against a standard and as many as eight limit samples. The Army wished to have a numerical method of setting color tolerances to be used with instrumental measurement. Preliminary work with the standards and limit samples indicated that acceptability ellipsoids oriented in the hue, chroma, and lightness directions in CIELAB color space should be set up. To establish the tolerances, we selected pairs of samples from a large number of previous submissions by industry. These pairs represented four graduated lightness steps, four graduated chroma steps, and four graduated hue steps. Six observers looked at each pair ten times, randomly interspersed with other pairs, and issued a pass-or-fail judgment each time. From these data we established lightness, chroma, and hue tolerance limits. For an olive green and a tan shade, these tolerances were roughly in the ratio 3:2:1; for a dark blue, the ratios were roughly 2:2:1. We wrote simple equations that can be used in order to determine quickly whether a sample passes or fails.     

Geodesic calculation of color difference formulas and comparison with the munsell color order system

Riemannian metric tensors of color difference formulas are derived from the line elements in a color space. The shortest curve between two points in a color space can be calculated from the metric tensors. This shortest curve is called a geodesic. In this article, the authors present computed geodesic curves and corresponding contours of the CIELAB ( ), the CIELUV ( ), the OSA‐UCS (ΔE_E) and an infinitesimal approximation of the CIEDE2000 (ΔE₀₀) color difference metrics in the CIELAB color space. At a fixed value of lightness L*, geodesic curves originating from the achromatic point and their corresponding contours of the above four formulas in the CIELAB color space can be described as hue geodesics and chroma contours. The Munsell chromas and hue circles at the Munsell values 3, 5, and 7 are compared with computed hue geodesics and chroma contours of these formulas at three different fixed lightness values. It is found that the Munsell chromas and hue circles do not the match the computed hue geodesics and chroma contours of above mentioned formulas at different Munsell values. The results also show that the distribution of color stimuli predicted by the infinitesimal approximation of CIEDE2000 (ΔE₀₀) and the OSA‐UCS (ΔE_E) in the CIELAB color space are in general not better than the conventional CIELAB (ΔE) and CIELUV (ΔE) formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 259–266, 2013     

Color errors of digital cameras

The mean color errors of a high‐quality digital camera are defined in CIELAB and CIEDE2000 ΔE units by using 16 ceramic color samples, whose accurate CIELAB values have been measured by a calibrated spectrophotometer. The bandwidths of CCD's color filters are evaluated by taking photographs of CRT‐display primaries. The lowest mean color errors were 13.1 CIELAB ΔE units and 8.1 CIEDE2000 ΔE units before corrections. Large color errors are decreased successfully by using three different methods: simple photoeditor, gamma correction, and multiple regression. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 217–221, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20007     

An investigation of how the texture surface of a fabric influences its instrumental color

In this article, the influence of texture surface of a fabric on its instrumental color is investigated. While former studies have found it is difficult to establish a quantitative relationships between texture of fabric and its instrumental color (color difference and color attributes, such as lightness, chroma, and hue), this article investigates from a theoretical and empirical perspective the interaction between texture and color. Eighty four knitted cotton yarn dyed fabric samples in four color centers and 21 texture structures were used in this study. It is revealed that fabric samples with different texture surfaces define a set of lines with identical direction in the reflectance space, and thus the normalized reflectance curves of these samples are identical. In the CIEXYZ space, tristimulus values of these fabric samples define a line, and thus their chromaticity coordinates are constant. In the CIELAB space, however, linearity is lost due to the non‐linear transformation from the CIEXYZ space to the CIELAB space. The finding of this article has the potential to discount the influence of texture of a fabric on its color. Experiments show that the influence of texture on color for samples in the four color centers can be reduced by 79, 55, 71, and 57%, respectively comparing to the real measured color difference. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 472–482, 2015     

Performance of advanced color difference and CAM02‐based formulas in prediction of the Crispening effect for reflective samples

The Crispening effect is defined as an increase in the perceived color difference of the two stimuli, when their color (chromaticity or luminance) is close to the background on which the two stimuli are compared. In this study, the amount of the Crispening effect for three achromatic backgrounds and also the performance of six different color difference formulas (CDFs) for prediction of this effect have been investigated, by preparing 85 sample pairs in 9 CIE's recommended color centers. Regarding the results, the maximum (50%) and the minimum (4%) amount of the Crispening effect belong to the gray and the purple centers, respectively. According to the results of a comparative test, the Crispening intensifies when two stimuli have just lightness difference instead of just chromaticity difference. The highest variation was for the gray samples, in which the amount of the Crispening effect increased from 35% to 65%. By using PF/3 and STRESS index, it is also concluded that CMC and CIEDE2000 perform better than CAM02‐SCD and CAM02‐UCS in prediction of the Crispening effect on the dark gray and gray backgrounds. According to the results, the significant differences between the performances of the CDFs disappear when the luminance of the background increases. Huang's power functions also do not improve these results significantly. Furthermore, the results indicate that the traditional L* equation used in CIELAB performs similar to the Whittle's formula in prediction of the Crispening effect for reflective samples, and no significant difference was obtained.     

Practical demonstration of the CIEDE2000 corrections to CIELAB using a small set of sample pairs

A set of 10 color pairs was proposed and produced in 2002 to show the advantages of the CIEDE2000 color‐difference formula with respect to CIELAB. These 10 color pairs illustrated each of the five corrections to CIELAB proposed by CIEDE2000. The 10 color pairs were visually assessed, under reference conditions close to those proposed by CIEDE2000, by two groups of 31 and 21 inexperienced observers, using two different gray scales. Average visual results in these experiments fitted CIEDE2000 predictions much better than CIELAB, as shown by a decrease of Standardized Residual Sum of Squares values of about 20 units. Current visual results showed only the improvement of CIEDE2000 upon CIELAB in predictions of perceived color differences, but they are not recommended for testing new advanced color‐difference formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 429–436, 2013.     

Color tolerance study on white in practical aspect: Perceptibility versus acceptability

This study investigates how the color tolerance for whites is affected by color category and context. Two experiments with different contexts, perceptibility or acceptability, were conducted using 27 color variation samples from six color categories (Neutral‐White, Red‐White, Yellow‐White, Green‐White, Blue‐White, and Blue‐Vivid). The results indicated that the color tolerance for Green‐white is dominantly higher relative to the other white‐based colors, whereas the color tolerance for Yellow‐White and Neutral‐White are relatively lower. However, contrary to the hypothesis, no significant differences in the level of color tolerances could be found between the two context of perceptibility and acceptability. The color tolerance thresholds for each color category groups in both experiments were similar. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 582–588, 2014     

Visual evaluation at scale of threshold to suprathreshold color difference

Visual evaluation experiments of color discrimination threshold and suprathreshold color‐difference comparison were carried out using CRT colors based on the psychophysical methods of interleaved staircase and constant stimuli, respectively. A large set of experimental data was generated ranged from threshold to large suprathreshold color difference at the five CIE color centers. The visual data were analyzed in detail for every observer at each visual scale to show the effect of color‐difference magnitude on the observer precision. The chromaticity ellipses from this study were compared with four previous published data, of CRT colors by Cui and Luo, and of surface colors by RIT‐DuPont, Cheung and Rigg, and Guan and Luo, to report the reproducibility of this kind of experiment using CRT colors and the variations between CRT and surface data, respectively. The present threshold data were also compared against the different suprathreshold data to show the effect of color‐difference scales. The visual results were further used to test the three advance color‐difference formulae, CMC, CIE94, and CIEDE2000, together with the basic CIELAB equation. In their original forms or with optimized K_L values, the CIEDE2000 outperformed others, followed by CMC, and with the CIELAB and CIE94 the poorest for predicting the combined dataset of all color centers in the present study. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 198–208, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20106