Unique hue data for colour appearance models. Part I: Loci of unique hues and hue uniformity

Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour‐normal observers (n = 185). These data were then used to evaluate the most commonly used colour appearance model, CIECAM02, by transforming the CIEXYZ tristimulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is important. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

Unique hue data and CIECAM02: A comment on Xiao et al

This letter to the editor notes the offsets from the origin of unique hue loci in the recent article, Unique Hue Data for Colour Appearance Models. Part III: Comparison with NCS Unique Hues by Xiao et al. [Color Res Appl 2015;40:256–263] and suggests that modifications to the cone response function in CIECAM02 be explored. © 2015 Wiley Periodicals, Inc. Col Res Appl, 2015     

Unique hue data for colour appearance models. Part II: Chromatic adaptation transform

Unique hue settings of 185 observers under three room‐lighting conditions were used to evaluate the accuracy of full and mixed chromatic adaptation transform models of CIECAM02 in terms of unique hue reproduction. Perceptual hue shifts in CIECAM02 were evaluated for both models with no clear difference using the current Commission Internationale de l'Éclairage (CIE) recommendation for mixed chromatic adaptation ratio. Using our large dataset of unique hue data as a benchmark, an optimised parameter is proposed for chromatic adaptation under mixed illumination conditions that produces more accurate results in unique hue reproduction. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013     

Unique hue judgments using saturated and desaturated Munsell samples under different light sources

Past studies investigating the unique hues only used samples with a relatively high saturation levels under standard illuminants. In this study, 10 observers selected the four samples with unique hues from 40 V6C8 (Value 6 Chroma 8) and 40 V8C4 (Value 8 Chroma 4) Munsell samples under six light sources, comprising three levels of D_uv (i.e., 0, ?0.02, and ?0.04) and two levels of correlated color temperature (i.e., 2700 and 3500 K). Significant differences were found between the two chroma levels for unique blue and yellow, with the hue angles of unique yellow and blue judged using the desaturated samples being significantly different from those defined in CIECAM02. The iso‐lines of unique yellow, blue, and green did not always go through the origin of the a*‐b* or a′‐b′ planes in CIELAB and CAM02‐UCS. Thus, the problems of CIECAM02, CIELAB, and CAM02‐UCS identified in this study need further investigations.     

Experimental object color unique hue data for the mean observer for color appearance modeling

Color appearance models, among other things, predict the hue of a stimulus when compared with defined stimuli that represent the four unique hues. Recent studies have indicated that the stimuli representing with high reliability unique hue (UH) percepts vary widely for different color‐normal observers. The average yellow and blue UH stimuli for 102 observers, as determined in a recent experiment at medium chroma, differ considerably from the CIECAM02 defined unique hues, based on the Swedish NCS. Wide inter‐observer variability precludes color appearance models from accurately predicting, for individual observers, all four unique hue stimuli. However, models should predict accurately those of a well‐defined average observer. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 505–506, 2008     

The CRI‐CAM02UCS colour rendering index

A new colour rendering index, CRI‐CAM02UCS, is proposed. It predicts visual results more accurately than the CIE CIR‐R_a. It includes two components necessary for predicting colour rendering in one metric: a chromatic adaptation transform and uniform colour space based on the CIE recommended colour appearance model, CIECAM02. The new index gave the same ranks as those of CIE‐R_a in the six lamps tested regardless the sample sets used. It was also found that the methods based on the size of colour gamut did not agree with those based on the test‐sample method. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2012     

Chromatic luminance,colorimetric purity,and optimal aperture‐color stimuli

Chromatic luminance (i.e., luminance of a monochromatic color) is the source of all luminance, since achromatic luminance arises only from mixing colors and their chromatic luminances. The ratio of chromatic luminance to total luminance (i.e., chromatic plus achromatic luminance) is known as colorimetric purity, and its measurement has long been problematic for nonspectral hues. Colorimetric purity (p_c) is a luminance metric in contrast to excitation purity, which is a chromaticity‐diagram metric approximating saturation. The CIE definition of p_c contains a fallacy. CIE defines maximum (1.0) p_c for spectral stimuli as monochromatic (i.e., optimal) stimuli, and as the line between spectrum ends for nonspectrals. However, this line has <0.003 lm/W according to CIE colorimetric data and is therefore effectively invisible. It only represents the limit of theoretically attainable colors, and is of no practical use in color reproduction or color appearance. Required is a locus giving optimal rather than invisible nonspectral stimuli. The problem is partly semantic. CIE wisely adopted the term colorimetric purity, rather than the original spectral luminance purity, to permit an equivalent metric for spectrals and nonspectrals, but the parameter of equivalence was never clear. Since 1 p_c denotes optimal aperture‐color stimuli for spectrals, arguably 1 p_c should denote optimal stimuli consistently for all stimuli. The problem reduces to calculating optimal aperture‐color stimuli (“optimal” in energy efficiency in color‐matching) for nonspectrals, shown to comprise 442 + 613 nm in all CIE illuminants. This remedy merely requires redefinition of 1 p_c for nonspectrals as the line 442–613 nm, and gives meaningful p_c values over the hue cycle allowing new research of chromatic luminance relations with color appearance. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 469–476, 2007     

Irregularity in CIECAM02 and its avoidance

Were it not for an algorithm patch, the color‐appearance model CIECAM02 would sometimes be forced to evaluate fractional powers of negative numbers. The artifact arises because the red and green primaries of the initial CAT02 chromatic adaptation lie outside the positive gamut of the Hunt–Pointer–Estévez (HPE) primaries that subsequently convey the color signal in the model. Relocating the chromaticities of the HPE red and green primaries so as to lie on the CAT02 red–green line alleviates the problem, but adds a bit of X and Y to the revised HPE blue. An (x,y) diagram aids in visualizing the CIECAM02 gamut overlaps, with an extension that accommodates the HPE RGB triangle's enclosure of the point at infinity. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 142–145, 2006     

Proposed modification to the CIECAM02 colour appearance model to include the simultaneous contrast effects

A modified CIECAM02 colour appearance model, named CIECAM02‐m2, is proposed to enable CIECAM02 to predict the simultaneous contrast effect. The structure of the CIECAM02‐m2 is a development from CIECAM02, and contains two different procedures for modifying the reference white; one is for lightness and the other is for hue. The model was tested using a data set accumulated in this study and the LUTCHI data. The CV values for three colour attributes between predictions and experimental data were used to evaluate the accuracy of the model. The low CV values obtained show the performance of the CIECAM02‐m2 model to predict the simultaneous contrast effect satisfactorily. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 121 – 129, 2007     

Preprocessing to CIECAM02 input color with chromatic background

A preprocessing to CIECAM02 input color for color appearance prediction was proposed. In this study, 8640 color appearance matching pairs (NCS color charts with red, green, yellow, and blue backgrounds in a light booth and their reproductions with gray background on a CRT screen) were obtained by psychophysical experiment using the simultaneous‐binocular technique. Because only the lightness of background is included in CIECAM02, a color inducing vector based on opponent‐colors theory was introduced to preprocess CIECAM02 inputs, so that CIECAM02 may predict the corresponding color of an input color with chromatic background as well. By data fitting, a color preprocessing formula describing a relationship between the color inducing vector and the NCS chromaticness was conducted. Furthermore, the formula's performance was tested and the results showed that it was good for implementing the color appearance prediction of input colors with different chromatic backgrounds.© 2006 Wiley Periodicals, Inc. Col Res Appl, 32, 40–46, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20287     

Revision of the chroma and hue scales of a nonlinear color-appearance model

The following questions were raised to chroma and hue scales of the nonlinear color-appearance model: 1) significant nonuniformities of the chroma scales for different hues, and 2) deviations of hue scale between the model and the Munsell and the NCS schemes. It was suggested that the problems were caused by the use of the coefficient e_s(0) proposed by Hunt. Instead of e_s(0), a new coefficient E_s(0) was proposed, which corresponds to the chromatic strengths of spectral colors (including colors on the redpurple locus). By using E_s(0), the nonlinear color-appearance model could predict the hue and chroma scales of the Munsell and the NCS schemes quite nicely. the method in the present study is generally applicable for determining hue and chroma perceptions of object colors irrespective of the color-appearance model used.     

Accuracy of approximations for CIELAB chroma and hue difference computation

The transformation in CIELAB from differences in the L^*, a^*, b^* coordinates to those in lightness, chroma, and hue, ΔL^*, ΔC_ab^*, ΔH_ab^*, can be approximated by a rotation in 3-space. Expressions for the error in the approximation of chroma and hue differences are developed. Significant errors are introduced if either the hue angle or chroma difference between reference and sample colors are large. A computed example illustrates the use of the analysis. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 61–64, 1997.     

An investigation of colour appearance for unrelated colours under photopic and mesopic vision

Data were obtained for the colour appearance of unrelated colours under photopic and mesopic conditions. The effects of changes in luminance level and stimulus size were investigated. The method used was magnitude scaling of brightness, colourfulness, and hue. Two stimulus sizes (10° and 0.5°) and four starting luminance levels (60, 5, 1, and 0.1, cd/m²) were used. The results at 0.1 cd/m² had large variations, so data were obtained for two additional stimulus sizes (1° and 2°) at this luminance level. Ten observers judged 50 unrelated colours. A total of 17,820 estimations were made. The observations were carried out in a completely darkened room, after 20 min adaptation; each test colour was presented on its own. Brightness and colourfulness were found to decrease with decreases of both luminance level and stimulus size. The CAM97u model predicted brightness more accurately than CIECAM02 but gave worse performance in predicting colorfulness. For hue, CAM97u and CIECAM02 both gave satisfactory predictions. Using the brightness correlate from CAM97u, a new colour‐appearance model based on CIECAM02 was developed specifically for unrelated colours under photopic and mesopic conditions, with parameters to allow for the effects of luminance level and stimulus size. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011;     

Cognitive comparison of unique and intermediate hues

Eight panels were prepared using the most highly chromatic Natural Colour System (NCS) samples for each hue. Each panel consisted of a test hue and two reference hues, the samples representing either mean unique hues (uH) R, Y, B, or G as determined in earlier experiments or intermediate hues half way between them, (iHs) Or, Pp, YG, and BG. Subjects were asked to determine whether they considered it possible to match the test hue with the reference hues. A total of 45 color‐normal subjects participated in the study in three separate trials with at least 24 h gap between consecutive trials. The results, with very few exceptions, showed that a match was considered possible for the four iHs based on a mixture of adjacent pairs of uHs, while the majority did not believe uHs could be matched based on amounts of adjacent iHs. Statistical analysis of the test results indicates that responses for the two sets of panels were significantly different (P ～ 0, df = 539). The results provide support for Hering's uHs concept. However, a number of subjects believed that a mixture of adjacent iHs can result in a match of the reference uHs. Potential causes may include the use of experimental mean unique and intermediate hues and learned facts of colorant mixture. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 264–269, 2015     

A shade guide model based on the color distribution of natural teeth

The objectives were to determine the color distribution of natural teeth sorted by the parameters of Value, Chroma, and hue angle measured with a colorimeter, and to suggest a shade guide model. The color of maxillary and mandibular 12 anterior teeth was measured with a tristimulus colorimeter for 47 subjects (n = 564). The color of teeth was grouped initially by Value (CIE L*) by the interval of 3.3 units. After then, within each main group, the color of teeth was subgrouped by Chroma by the interval of 3.3 units. Chroma was calculated as C*_ab = (a*2 + b*2)^1/2. Since the hue angles were in the first or fourth quadrant, subgroups were further sorted by the first or fourth quadrant hue angles. Hue angle was calculated as h° = arctan (b*/a*). Mean color difference (ΔE*_ab) between the color of an individual tooth and the mean color of each main group was 2.5–3.3, which was lower than acceptable limit (ΔE*_ab < 3.3), and that in each subgroup was 0.9–3.1. The number of subgroups was 22, which was comparable to those of conventional shade guides. A shade guide model based on the color distribution of natural teeth sorted by Value in six main groups, three or four subgroups within each main group sorted by Chroma, and further sorted by hue angle (first or fourth quadrant values) was suggested. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 278–283, 2007     

Some concerns regarding the CAT16 chromatic adaptation transform

In recent literature, a new chromatic adaptation transform, CAT16, has been published to improve upon the widely used CAT02 model. The CAT16 model is based on the form of the CAT02 transform adopted in CIECAM02, but uses a slightly different sensor space to fix some gamut problems plaguing CIECAM02 and adopts a two-step CAT to ensure symmetry and transitivity. CAT16 is included in CAM16 but is also being promoted as a stand-alone CAT, one that can be used outside of the CAM16 model. However, the use of CAT16 as a stand-alone model can cause inconsistencies in the calculated corresponding colors due to the presence of the relative luminance of the adapting white (Y_w) in the von Kries-Ives gain control factors. Such inconsistencies are not present for the stand-alone version of the CAT02 model, which, unlike the version adopted in CIECAM02, does not include the Y_w factors. CAT16 should therefore be modified by omitting the Y_w factors. In this article, we will briefly discuss these issues in more detail and provide a consistent two-step CAT adopting the CAT16 sensor space.     

Colorimetric study of SCOTDIC colour specifier

In this study, SCOTDIC cotton standard colours (a physical exemplification of the Munsell system) were studied extensively. L*, a*, b* values were measured and plotted to check the uniformity of the Munsell (SCOTDIC) hue, value and chroma values in a CIELAB diagram. Although for some borderline hues the hue angles were quite different than expected (around 0° or 360°), the correlation between SCOTDIC hue and CIELAB hue angle was fairly good and the correlation between SCOTDIC value and CIELAB lightness was also quite high. However, the correlation between SCOTDIC chroma and CIELAB chroma was only moderate. In the CIELAB diagram, the constant SCOTDIC hue and constant chroma loci took the shape of approximately linear radial lines starting from the origin and approximately concentric circles with the origin as their centres, respectively. However, some deviations were observed for high chroma colours and yellow hues in the respective cases. The instrumentally predicted Munsell notations were compared with the actual SCOTDIC notations. Some deviations of the SCOTDIC system from the Munsell system were observed.     

A test of uniformities in the OSA‐UCS and the NCS

In the OSA‐UCS (Optical Society of America–Uniform Color Scales), except for colors on the boundary of the three‐dimensional solid (L, j, g), each color is surrounded by the 12 nearest neighboring colors that are supposed to be perceptually equally different (local uniformity). In the Swedish NCS (Natural Color System), colors are arranged so as to gradually vary in each of the three perceptual attributes: hue, ?; blackness, s; and chromaticness, c. The gradual change in an attribute may correspond to change of color difference from one to the next with a constant step (local uniformity). In each of these color‐order systems, the uniformity was tested by a color‐difference formula d? based on color‐component differences. When a coordinate is fixed (e.g., j in OSA‐UCS, or c in NCS), d? for neighboring pairs turned out fairly constant. However, systematic differences were found between d? in one coordinate and d? in another coordinate. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 277–283, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10162     

A comparison of colour appearance for surface colours between outdoor and indoor environments

Psychophysical experiments of colour appearance, in terms of lightness, colourfulness, and hue, were conducted outdoors and indoors to investigate whether there was any difference in colour appearance between outdoor and indoor environments. A panel of 10 observers participated in the outdoor experiment, while 13 observers took part in the indoor experiment. The reference white had an average luminance of 12784 cd/m² in the outdoor experiment and 129 cd/m² in the indoor experiment. Test colours included 42 colour patches selected from the Practical Coordinate Color System to achieve a reasonable uniform distribution of samples in CIECAM02. Experimental results show that for both outdoor and indoor environments, there was good agreement between visual data and predicted values by CIECAM02 for the three colour appearance scales, with the coefficient of variation values all lower than 25 and the R² values all higher than 0.73, indicating little difference in the three dimensions of colour appearance between indoor and outdoor viewing conditions. Experimental data also suggest that the observers were more sensitive to variation in lightness for grayish colours than for highly saturated colours, a phenomenon that seems to relate with the Helmholtz-Kohlrausch effect. This phenomenon was modeled for predicting perceived lightness (J′) using the present experimental data. The new J′ model was tested using three extra sets of visual data obtained both outdoors and indoors, showing good predictive performance of the new model, with an average coefficient of variation of 14, an average R² of 0.88, and an average STRESS index of 14.18.     

Effect of mincing degree on colour properties in pork meat

Although the colour of different meat products has been studied, particularly in the final product , these studies do not separate the influence of degree of mincing from other factors such as additives, spices, manufacturing process, etc. The effect of degree of mincing on colour (CIELAB colour space) in pork meat was studied. Three mincing processes were studied, two using a grinder with 10 and 20 mm diameter holes in the plate, and a third in which a cutter was used to obtain a finely minced product. As control, intact meat was used. Colour parameters [lightness (L*), redness (a*), yellowness (b*), chroma (C*), hue (H*), a*/b* ratio, and colour differences], pH, and water holding capacity were determined. Mincing, regardless of the type used, increased the values of L*, b*, and H*, but decreased the values of a* and a*/b* ratio. The L* values increased with mincing degree. The H* values and a*/b* ratio of plate minced meats (10 and 20 mm) differed from that which had been finely minced. The mincing process did not modify the saturation values of the batters. Only the fine mincing process modified (increased) the water holding capacity of the batters. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 376–380, 2000