A comparison of five color order systems

Five color order systems (Munsell Renotations, Munsell Re‐renotations, OSA‐UCS, NCS, and Colorcurve) have been compared by optimizing the powers applied to individual opponent‐color functions. The results indicate general similarities in that powers applied to the red and green functions tend to be closer to 1, while those applied to the blue function and the yellow function are generally smaller. Specifically, there are many individual differences that make each system unique. The results inspire confidence in the veracity of the opponent‐color system methodology. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 123–131, 2000     

Adequateness of a newly modified opponent‐colors theory

Two features of a newly modified opponent‐colors theory are examined for correctness: (1) The perceived chroma of pure color is different for different hues. This was confirmed by using Ikeda's UCS (Uniform Color Scales) formula and also by the maximum Munsell Chroma Values for different hues. (2) Chromatic colors with the same values of whiteness, blackness, grayness, and perceived chroma have the same perceived lightness and chromatic tone regardless of hue. This was confirmed by a theoretical analysis and observations of the color samples in the Practical Color Co‐ordinate System (PCCS) developed in Japan. Chromatic tone, a complex concept of object colors, is clarified. The structure of the newly modified theory and its corresponding color space were confirmed by observation of object colors. Furthermore, it was found effective for developing a color‐order system and its corresponding standard color charts to the modified theory. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 298–307, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10164     

A structural comparison of the Munsell renotation and the OSA–UCS uniform color systems

A structural comparison has been made of the lightness, chroma, and hue scales of the Munsell system, as expressed in the Munsell Renotations, and of the OSA‐UCS system. While the lightness scales are similar (except for the adjustment for the Helmholtz–Kohlrausch effect and the inclusion of a “crispening” effect in OSA–UCS), there are significant differences in the chroma scales along the major chromatic axes. Unlike in CIELAB, the increments in X and Z along these axes for equal chroma steps in both systems do not fall on a continuous function. In the two systems, as well as in CIELAB lines connecting colors of equal chroma differences at different Y values point to nonreal origins. These differ among the three systems. A major difference between Munsell and OSA–UCS is the size of the first chroma step away from gray. An experiment has been performed with the result that the OSA–UCS system is in much better agreement with the average observer in this respect than the Munsell system. OSA–UCS exhibits considerably more internal uniformity in terms of X and Z increments between steps than the Munsell system. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 186–192, 2000     

Color characteristics of Beijing's regional woody vegetation based on Natural Color System

Seasonality is a typical characteristic of Beijing's regional vegetation, and plant color is one of the most prominent visual factors of vegetation dynamic. In this research, we explored the composition and dynamic characteristics of plant color in Beijing's urban vegetation, involving the analysis of overall characteristics and respective features of leaf, flower, and fruit colors. Color data was collected from 177 woody plant species in Beijing Botanical Garden, spanning their annual life cycle, and identified with the colorimetry of the Natural Color System (NCS). Correlation and regression analyses were applied to reveal the temporal dynamic features of overall plant color richness. Cluster analysis was applied to categorize tree species based on typical colors of various plant organs. Color richness and color dispersion were introduced as two factors to measure color diversity of various tree species, applied in species evaluation by sorting and principal component analysis (PCA). Color dispersion of three‐dimensional NCS data was measured with a modified SD based on the calculation of mean spatial distance in the NCS space. Main results are as follows. The first part is plant color composition. The composition of all plant colors contains 862 NCS color species, 20 blackness species ranging from 3 to 90, 20 chromaticness species ranging from 0 to 90, 35 hue species ranging from G10Y‐B90G, and N. The second part is temporal dynamic of overall color richness. Leaf color richness and total color richness are significantly positively correlated with pentad (5‐day) sequence; flower color richness is significantly negatively correlated with pentad sequence; and fruit color richness first increases and then decreases over time. The third part is cluster analysis of tree species. Based on typical growing‐leaf color, various tree species were clustered into 6 categories; based on typical senescent‐leaf color, various tree species were clustered into 6 categories; based on typical flower color, various tree species were clustered into 15 categories; based on typical fruit color, various tree species were clustered into 7 categories. The fourth part is color diversity evaluation of various tree species with PCA. According to the PCA of flower‐leaf color diversity, the species with higher leaf color diversity and higher flower color diversity include Cotinus coggygria, Lagerstroemia indica, and Amygdalus triloba; the species with higher flower color diversity and lower leaf color diversity include Campsis radicans and Tamarix chinensis; the species with higher leaf color diversity and lower flower color diversity include Acer ginnala and Crataegus pinnatifida; the species with lower color diversity both for flower and leaf colors include Fontanesia fortune and Gleditsia sinensis. According to the PCA of leaf color diversity, the species with higher leaf color diversity in both leaf growth period and leaf senescence period include Diospyros kaki, Lagerstroemia indica and Paeonia suffruticosa; the species with higher leaf color diversity in leaf growth period and lower leaf color diversity in leaf senescence period include Amygdalus persica ‘Atropurpurea’ and Prunus virginiana ‘Canada Red’; the species with higher leaf color diversity in leaf senescent period and lower color diversity in leaf growth period include Quercus palustris, Armeniaca sibirica, and Metasequoia glyptostroboides; the species with lower leaf color diversity for the whole leaf development period include Gleditsia sinensis and Swida walteri.     

Exact location of consensus and consistency colors in the osa‐ucs for the italian language

In a previous work, the authors reported on the results of a color naming experiment performed on native Italian speakers regarding the location of focal colors and centroids in the Uniform Color Scales of the Optical Society of America color system. That work was aiming at comparing such data with those previously obtained by Boynton and Olson (B&O) accounting for the differences in the paradigm and the language. The number of consistency and consensus colors in the different lightness plans was also reported but no information was provided on their placement. Though, such information is very important for any subsequent modeling stage. The objective of this article is to fill such a gap and share such data with the scientific community to provide a reference database for future investigation. Three different datasets were considered: the extended OSA (E‐OSA), the reduced OSA (R‐OSA), and the B&O's (B&O) sets of reference colors. Results show a good overlap among the locations of the consensus colors in the {L, j, g} color model between B&O and the subset of E‐OSA colors overlapping with the B&O 424 colors (R‐OSA), as well as a strong agreement on consistency. Furthermore, a close proximity among the centroids of homologue regions for the majority of the classes was found. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 437–447, 2013     

Analyzing the natural color system's color-order system by using a nonlinear color-appearance model

The Natural Color System (NCS) is analyzed by using a nonlinear color-appearance model. Perceptual uniformity was examined for each of the NCS attributes of blackness (s), chromaticness (Cr), and hue. Some nonuniformities were found in chromaticness and hue spacing, which were probably the result of the assessing and scaling method used in developing the system. In addition, a colorimetric interpretation is given to a principal plane in the NCS consisting of the colors with (s,Cr) = (50,0) and (0,100). The colors with (0,100) have different values of Y for different hues. Based on the analysis, a method is developed to predict the NCS attributes from the values x, y, Y of an object color by using the nonlinear color-appearance model. The present analysis clarifies the importance of the NCS system in studying the color appearance of object colors.     

Color scales in simulated daylight

The pigmentation plan used for production of the color cards made available by the Optical Society of America (OSA) for its Committee on Uniform Color Scales (UCS) was designed in such a manner that the color scales should, within the production tolerances, appear uniform in all phases of daylight. The production specifications were based on D₆₅ of the Commission International de L'Eclairage (CIE) and the CIE supplementary observer (1964) for 10° visual-field subtense. To test for the intended invariance of uniformity of the scales in daylight, for normal observers, the effects on color differences between all nearest neighbors of the OSA colors have been studied for CIE Illuminant C with the 1931 observer, and for a “daylight” fluorescent luminaire (color temperature 6500 K) with both the 1931 and 1964 CIE observers. Although the colorimetric specifications (Y, x, y) of each color card are different for those three illuminant + observer combinations, the color differences computed with the formula of the OSA-UCS committee are, within the production tolerances, unchanged. The purpose of this article is to show how well the aim and expectation is fulfilled—that the uniformity of color differences between nearest neighbors in the scales of the OSA-UCS colors be essentially unchanged for normal observers and for ordinary variations of the quality of natural and artificial daylight. This invariance is found, even for daylight-quality fluorescent-lamp light.     

Evaluation of performance of various color‐difference formulae using an experimental black dataset

The objective of this study was to develop a specific visual dataset comprising black‐appearing samples with low lightness (L* ranging from approximately 10.4 to 19.5), varying in hue and chroma, evaluating their visual differences against a reference sample, and testing the performance of major color difference formulas currently in use as well as OSA‐UCS‐based models and more recent CAM02 color difference formulas including CAM02‐SCD and CAM02‐UCS models. The dataset comprised 50 dyed black fabric samples of similar structure, and a standard (L*= 15.33, a* = 0.14, b* = ?0.82), with a distribution of small color differences, in ΔE^*_ab, from 0 to approximately 5. The visual color difference between each sample and the standard was assessed by 19 observers in three separate sittings with an interval of at least 24 hours between trials using an AATCC standard gray scale for color change, and a total of 2850 assessments were obtained. A third‐degree polynomial equation was used to convert gray scale ratings to visual differences. The Standard Residual Sum of Squares index (STRESS) and Pearson's correlation coefficient (r), were used to evaluate the performance of various color difference formulae based on visual results. According to the analysis of STRESS index and correlation coefficient results CAM02 color difference equations exhibited the best agreement against visual data with statistically significant improvement over other models tested. The CIEDE2000 (1:1:1) equation also showed good performance in this region of the color space. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 589–598, 2014     

Color‐emotion associations,designing color schemes for urban environment‐architectural settings

Color in urban design has become an important issue, each city may present different colors which help to define and describe its architectural features. In the study, color in urban design with architectural setting is studied, façade colors are analyzed with a specific emphasis on the following research questions; “Can color schemes be designed in respect to color‐emotion associations? ” and “Are color‐emotion associations affective while designing architectural setting‐urban environment?.” Non‐color experts, 170 people, from different European and non‐European countries were asked to match the most appropriate adjectives with the given street views in accordance to their color schemes. In the first step, the effect of color is identified in relation to architectural environment‐urban setting, second the relative effect of color is studied as a component of the material. A categorical specification on color cognition and linguistic level of representation is attempted. The results can be a starting point to highlight the importance of preparing color schemes in regard to color‐emotion associations. Abstract color schemes may also provide us an idea about image setting, especially at design process stage. In the study, keywords are linked as environment‐response pairs; such as quiet, calming, lively, exclusive, reserved, and natural. Human psychophysical structure such as “warm‐cool,” “heavy‐light” in regard to visualizing certain colors are evaluated and described in terms of building materials.     

Some modifications to Hering's opponent‐colors theory

Some modifications are made to the achromatic color perceptions in Hering's opponent‐colors theory. They are the introduction of the reference color Gray and the use of the orthogonal coordinate system. The modified opponent‐colors theory has a symmetrical structure for the three opponent‐colors axes, whiteness‐grayness‐blackness, redness‐grayness‐greenness, and yellowness‐grayness‐blueness, and it unifies the Hunt and the Stevens and Jameson–Hurvich effects. It is also noted that two kinds of color‐appearance spaces exist. One is the color‐appearance space derived from color perceptions of object colors (called the CPS color‐appearance space). The other is that modeled from their colorimetric values for predicting color perceptions (called the UCS color‐appearance space). The CPS color‐appearance space is mainly described in this article. Scaling of the CPS color‐appearance space and the existence of the reference color perception Gray are discussed in detail. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 290–304, 2001     

Proposal of an opponent‐colors system based on color‐appearance and color‐vision studies

A new theoretical color order system is proposed on the basis of various studies on color appearance and color vision. It has three orthogonal opponent‐colors axes and an improved chromatic strength of each hue. The system has color attributes whiteness w, blackness bk, grayness gr, chroma C, and hue H. A method is given for determining Munsell notations of any colors on any equi‐hue planes in the system. A method is also given for determining grayness regions and grayness values on hue‐chroma planes in the system. It is concluded that colors with the same color attributes [w, gr, bk, C] but with different hues in the theoretical space have approximately the same perceived lightness, the same degree of vividness (“azayakasa” in Japanese), and also the same color tone. The tone concept, for example used in the Practical Color Coordinate System (PCCS), is clarified perceptually. The proposed system is a basic and latent color‐order system to PCCS. In addition, the concept of veiling grayness by a pure color with any hue is introduced. Further, relationships are clarified between generalized chroma c(gen) and grayness. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 135–150, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10234     

The representation of colors in spherical space

Color scaling experiments have established that perceived colors are distributed on the surface of a hypersphere in spherical space. A formal mathematical model of the color space is defined. Color differences as estimated by an observer are equal to the chord distance between corresponding points on the surface in spherical space. In one mathematical model are united: brightness, saturation, and hue as expressed in the empirical Munsell and NCS systems; complementary colors; large color differences; and contrast effects that are not represented in other models. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 113–124, 2008.     

Clarification of differences between variable achromatic color and variable chromatic color methods in the Helmholtz–Kohlrausch effect

The Helmholtz–Kohlrausch effect consists of two different approaches: the variable achromatic color (VAC) and variable chromatic color (VCC) methods. In this article the difficult conceptual difference between the methods is clarified using new explanations with their schematic figures. The concept of loci with various parameters on B / L or L / Y ratios is completely different between the two methods. The VCC method can determine perceived lightness values for achromatic and chromatic colors in the whole color space. The VAC method gives perceived lightness deviation between reference achromatic color and each of the various test chromatic colors both kept at the same Munsell Value. The VAC method can never give any information on equiperceived lightness to test chromatic colors. Despite the difference between the two methods, misuse of the VAC method is sometimes found for perceived lightness studies of various chromatic colors, because of its ease in observations. An example is shown for the L scale of OSA‐UCS. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 146–155, 2006     

Unique hue data for colour appearance models. Part III: Comparison with NCS unique hues

In this study, Swedish Natural Color System (NCS) unique hue data were used to evaluate the performance of unique hue predictions by the CIECAM02 colour appearance model. The colour appearance of 108 NCS unique hue stimuli was predicted using CIECAM02, and their distributions were represented in a CIECAM02 a_c–b_c chromatic diagram. The best‐fitting line for each of the four unique hues was found using orthogonal distance regression in the a_c–b_c chromatic diagram. Comparison of these predicted unique hue lines (based on the NCS data) with the default unique hue loci in CIECAM02 showed that there were significant differences in both unique yellow (UY) and unique blue (UB). The same tendency was found for hue uniformity: hue uniformity is worse for UY and UB stimuli in comparison with unique red (UR) and unique green (UG). A comparison between NCS unique hue stimuli and another set of unique hue stimuli (obtained on a calibrated cathode ray tube) was conducted in CIECAM02 to investigate possible media differences that might affect unique hue predictions. Data for UY and UB are in very good agreement; largest deviations were found for UR. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 256–263, 2015     

An inverse to the Optical Society of America‐Uniform Color System

The Optical Society of America Uniform Color Scales (OSA‐UCS) possesses many salutary properties, but it is presently under‐utilized. The authors believe the reason may be that the transformation from CIE notation to the OSA system presently has no known inverse. In an effort to rectify that lack, the OSA system is presented in its forward transformation, and then an inverse transformation using a Newton–Raphson iterative algorithm is described. The authors' experience with parameters associated with implementing the algorithm is related, and a few worked examples are provided to assist potential users in their implementation. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

Centroids of color categories compared by two methods

A procedure is described whereby the subset of Munsell colors of maximum chroma, which has been used by anthropologists to study color naming since Berlin and Kay in 1969, can be specified in the L,j,g coordinate system developed more recently by the Uniform Color Scales Committee of the Optical Society of America. The latter permits a meaningful specification of the centroid location of colors named by each basic term. The procedure is validated by comparing centroids obtained from six subjects who named samples of both the Munsell and OSA sets, and its usefulness is illustrated by comparing data from a four-year old who named only OSA samples and four- and two-year-olds who named only Munsell colors.     

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     

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     

Analyzing a decade of Colors of the Year

The Color of the Year was first introduced by Pantone in 2000, and recently (the last decade) we saw the trend of introducing a Color of the Year being picked up by more and more companies. Paints and coatings companies typically select their colors of the year by extensive research by designers and trend experts, resulting in a plurality of colors being introduced as Color of the Year, every year. In this article, we collated colors of the year of 15 different paints and coatings companies published in the past decade and we show that most colors of the year can be described as neutral or off‐white color (ie, the median value for NCS Chromaticness is low, 20%) although occasionally colors of the year have high NCS Chromaticness. We demonstrate that the distribution of colors of the year follow a certain narrative from year to year: The average Lightness and Chroma (averaged over all companies, per year) appear to follow a wavelike pattern, where the average Lightness appears to repeat itself every ~8 years and the average Chroma approximately every 4.5 years. Similarly, we can see a cyclic pattern in the hue: From mostly yellowish red or greenish blue in 2015, towards predominantly blue in 2017, to a wide variation in hues in 2020 suggesting a fragmentation in colors of the year preferences. In addition, we demonstrate that the colors of the year differ significantly from what can be expected if the colors would have been selected randomly. This could reflect the fact that paint companies use similar raw data to identify their color trends.     

Color managing the third edition of Billmeyer and Saltzman's Principles of Color Technology

Commercial ICC‐compliant color‐management software was used to produce color‐managed CMYK‐encoded images for the third edition of Principles of Color Technology. Custom profiles were created for a Scitex Eversmart Pro flatbed scanner and a Kodak Approval proofing device. This enabled objects such as color‐order systems and colorimetric‐encoded, computer‐generated graphical images to be reproduced with reasonable colorimetric accuracy. The GretagMacbeth ColorChecker Color Rendition Chart was used as an independent verification target. Its printed reproduction had an average error of 4.2 ΔE (6.4 ΔE^*_ab). Colorimetric‐rendering device profiles enabled the visualization of the book's color gamut and of a calibrated visible spectrum. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 360–373, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10083