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.     

Preference for accent and background colors in interior architecture in terms of similarity/contrast of natural color system attributes

Color combination criteria are said to entail an affective response in interior design. We investigated the color combination criteria that orient the preference of current observers, after Le Corbusier's 1931 Salubra keyboards. We explored the similarity/contrast in Natural Color System (NCS) hue, blackness, and chromaticness in 312 combinations with four colors, two backgrounds and two accent colors, coming from 43 individual colors, on the walls of a simulated interior of a bedroom from the Swiss Pavilion (Le Corbusier, 1930-1931). Participants were 644 students of architecture and interior design in Western Europe and Near East, who evaluated with a Likert scale their preference for virtual images via an online survey. Results indicate that the most preferred color combinations are those with hues closer in the color wheel, being the similarity between hues in the backgrounds more important than in the accent colors, and with NCS B30G to G as the most preferred hues. Observers preferred color compositions with blackness under 10% and similar blackness between the two background colors, together with a certain blackness contrast between these background colors and the two color accents. Similarly, observers liked color compositions with low chromaticness and low chromaticness difference among the four colors of the composition.     

Emotional response to color across media

In this study, the characteristics of emotional responses to color are explored in two empirical studies. In particular, the relationship between color attributes and emotional dimensions—valence, arousal, and dominance—is analyzed. To account for the cognitive quantity of color, 36 color stimuli were selected following hue and tone categorizations and based on the CIELAB LCh system. In one experiment, the colors were presented on A5‐size glossy paper whereas the identical colors were displayed on CRT monitors in the other experiment. In both experiments, the subjects assessed the emotional responses to each color stimulus using a Self‐Assessment‐Manikin (SAM), which consists of three rows of five pictograms illustrating the three dimensions of emotion, respectively. The empirical results provide evidence that the patterns of affective judgment of colors can be profiled in terms of the three dimensions of emotion (Reliability coefficient, Cronbach's alpha > 0.7). All three attributes of colors, i.e. hue, Chroma, and Lightness, influenced the emotional responses (repeated measurement One‐way ANOVA, P < 0.05), and especially, Chroma was always positively correlated with each of the three emotional dimensions (r > 0.60 P < 0.01). Moreover, the results indicate that emotional responses to color vary more strongly with regard to tone than to hue categories. Comparing the SAM ratings between the two experiments, a systemic explanation has yet to be found to conclude that there is a media effect on the emotional responses to colors. Furthermore, the process of affective judgment of colors and the limit of color as an emotion elicitor are discussed. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

建筑涂料色彩的设计方法研究——基于中国颜色体系国家标准

介绍了中国颜色体系国家标准,以及中国颜色体系国家标准在建筑涂料色彩应用中的设计方法、未来建筑涂料色彩的发展趋势等。     

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.     

The feasibility of establishing new color image scales using the magnitude estimation method

Color image is one of the most important factors in art and design. In general, artists and designers apply their own personal image meanings into their work. However, the image meaning for a specific work is frequently in conflict with those of the general observer. Thus it is necessary and important to derive one set of merit color image scales which can be utilized to predict the color image meanings of works in parallel with the average person's perception and which can also serve as a guide for artists and designers. In this study, the psychophysical method (magnitude estimation method), usually used in visual measurement of color appearance, was employed to attempt to establish new color image scales to evaluate the color image meanings of works matching those of the average person. The results show that new color image scales WIP are developed, and the relativity between the latest color image scales WIP and the color attributes (say Lightness L*, Hue h, and Chroma C*) of the CIELAB color space is also discussed. The total mean value of coefficient of variation for the results of visual assessment in the experiment of evaluating the color image meanings of the 207 color specimens used, in general, is about 36, similar to that for those experiments conducted using the psychological method. Also, a good relationship between the new color image scales and the color attributes of the CIELAB color space can be found. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 463–468, 2007     

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     

Influence of basic color categories on color memory discrimination

Two color-memory experiments were performed to investigate whether observers tended to confuse colors with a smaller color difference in memory or colors in a same color-category region. We made color stimuli on a color CRT. Color difference was determined by a simultaneous color discrimination experiment. Color-category regions were obtained by a categorical color-naming experiment using the 11 basic color names: white, black, red, green, yellow, blue, brown, orange, purple, pink, and gray. The results show that two colors with a certain color difference can be confused more easily when they are in a same color category than in different color categories, and that colors identified with memory tend to distribute within their own color-category regions or their neighbor color-category regions, depending on their positions in a color space. These findings indicate that color memory is characterized by the color categories, suggesting a color-category mechanism in a higher level of color vision. © 1996 John Wiley & Sons, Inc.     

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     

Describing natural colors with Munsell and NCS color systems

The Munsell Color System and the Natural Color System are widely used but they have some limitations due to the manufacturing process and sampling choices. To estimate quantitatively these limitations we compared the colors of natural scenes with the colors represented by these systems under a wide range of illuminants. Spectral data from the two systems and from natural scenes were used in the analysis. It was found that a considerable portion of natural colors are not accounted by these systems, mainly colors with low lightness levels. Under D65 the Munsell Color System color volume corresponds to 72% of the Natural Color System color volume which in turn represents only 53% of the natural scenes color volume. If individual colors are considered, less than half are contained within these systems. To obtain a complete match to the natural colors contained by the color systems thresholds of 7 and 5 CIELAB units would be required for Munsell Color System and Natural Color System, respectively. Variations with the illuminant are generally modest showing that both system work similarly across different illuminations. Although these Color Systems have limitations in describing low lightness colors they perform quite well for medium to high levels of lightness.     

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 novel Color Rendition Chart for digital tongue image calibration

Digital tongue images are usually acquired by a camera under specific illumination environments. In order to guarantee better color representation of the tongue body, we propose a novel tongue Color Rendition Chart acting as a color reference to be used in color calibration algorithms to standardize the captured tongue images. First, based on a large tongue image database captured with our digital tongue image acquisition system, we establish a statistical tongue color gamut. Then, from the first step, different quantities of colors in the Color Rendition Chart are determined via experimentation. Afterwards, results using X‐Rite's ColorChecker® Color Rendition Chart (a standard in the color calibration field) are compared with the proposed tongue Color Rendition Chart by applying the color difference calculation formula of CIELAB and CIEDE2000 as a reference for the mean color calibration error. The results show that the proposed tongue Color Rendition Chart, which has 24 colors, produces a much smaller error (CIELAB —8.0755/CIEDE 2000—6.3482) compared with X‐Rite's ColorChecker® Color Rendition Chart (CIELAB 1976—14.7836/CIEDE 2000—11.7686). This demonstrates the effectiveness of the novel tongue Color Rendition Chart.     

Proposal of a uniform color scale for virgin olive oils

A new color scale was developed from a broad data set of 1700 virgin olive oil samples over four crop seasons, which can be considered highly representative of the whole color range of virgin olive oils available in Spain. This color scale provides a new set of 60 color standards, improving the results achieved by the old 60-color standards proposed by the bromthymol blue method. Seeking the greatest possibility of including a near match between colors of virgin olive oils and proposed standards, we developed our new color scale using a recent uniform color space, with standards placed in a regular rhombohedral lattice like the one employed by the Uniform Color Scales of the Optical Society of America. The average color difference between each of the 1700 virgin olive oils and its nearest standard is reduced from 8.17 CIELAB units, using the bromthymol blue standards, to 3.99 CIELAB units using the new standards. Within a color tolerance of 7.0 CIELAB units, 93.2% of our virgin olive oils can be classified with the new standards, but only 59.1% with the bromthymol blue ones. In the interest of future adoption, the performance of the new color standards should be tested by industry and researchers.     

Colored apparel and its potential influence on heterosexual attraction

The objective of this study is to determine if men would follow the “red effect” when choosing colors for women to wear on a date, and also to determine if the colors that men would wear when going on a date would be the same as the colors that females (their date) would wish them to wear. A set of psychophysical data was generated from this experiment, where participants were asked to rank a set of 10 colored samples based on preference for each question asked. There were three different sets of colored samples. The set of colored samples given to the participant depended on the question. A total of five questions were asked. Scaling analysis was done on the data to organize a set of items according to preferences providing values, an interval scale (Z values), that correspond to the relative perceptual differences among the stimuli. The Z values were graphed to show the general preference of colors for women to wear, and the preference of colors for men to wear. A Spearman's rank-order correlation coefficient (SRCC) was calculated comparing each individual's rank order with the mean rank order for that specific question. An average Spearman's rank order was calculated for each question and each gender in order to determine the variability in answers. Scaling results indicate that men follow the “red effect,” but women preferred to wear other colors such as turquoise, blue, or yellow depending on the outfit. Males and females agreed that no matter the colored bottoms (denim or black), blue was the preferred color top for men to wear. SRCC results showed a lot of variability between individual answers and the mean answer indicating that participants' rankings did not necessarily agree with general color preferences presented in the scaling analysis. While scaling analysis might suggest certain color preferences such as men following the “red effect” and women preferring to wear blue, the poor correlation found using SRCC between the individual answers and the mean rank orders suggests that color preferences for each individual are inherently unique.     

An online color naming experiment in Russian using Munsell color samples

Russian color naming was explored in a web‐based experiment. The purpose was 3‐fold: to examine (1) CIELAB coordinates of centroids for 12 Russian basic color terms (BCTs), including 2 Russian terms for “blue”, sinij “dark blue”, and goluboj “light blue”, and compare these with coordinates for the 11 English BCTs obtained in earlier studies; (2) frequent nonBCTs; and (3) gender differences in color naming. Native Russian speakers participated in the experiment using an unconstrained color‐naming method. Each participant named 20 colors, selected from 600 colors densely sampling the Munsell Color Solid. Color names and response times of typing onset were registered. Several deviations between centroids of the Russian and English BCTs were found. The 2 “Russian blues”, as expected, divided the BLUE area along the lightness dimension; their centroids deviated from a centroid of English blue. Further minor departures were found between centroids of Russian and English counterparts of “brown” and “red”. The Russian color inventory confirmed the linguistic refinement of the PURPLE area, with high frequencies of nonBCTs. In addition, Russian speakers revealed elaborated naming strategies and use of a rich inventory of nonBCTs. Elicitation frequencies of the 12 BCTs were comparable for both genders; however, linguistic segmentation of color space, employing a synthetic observer, revealed gender differences in naming colors, with more refined naming of the “warm” colors from females. We conclude that, along with universal perceptual factors, that govern categorical partition of color space, Russian speakers’ color naming reflects language‐specific factors, supporting the weak relativity hypothesis.     

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     

Why does the CIELAB a* axis point toward magenta instead of red?

Red is a basic color. The work of Kay and Berlin on the evolution of color names in languages shows that, after white and black, red is the most basic of color terms. Red plays a key part in Hering's color opponent theory, and is one of the elementary colors in both the Munsell Color system and in the Natural Colour System (NCS). The CIELAB color system has an axis which is sometimes referred to as the red-green axis. But unique red is about 25° degrees counterclockwise from the positive a* axis. This article answers the question of why this is.     

Investigation for the color samples with equi-depth in Natural Color System

The color depth, an important attribute of color, can reflect the amount of dye partly, which has important functions on the evaluation of color-fastness and strength of dyes, dyeing effect of fabric, computer color matching, and so on. Natural Color System, an internationally accepted color system, orders colors by three parameters (blackness, chromaticness, and hue). The color depth has not been specified within the Natural Color System. This article tries to find the regularity of the sample with equal-depth in Natural Color System. Firstly, 1950 color samples in Natural Color System were measured using an X-Rite Color i7 Spectrophotometer, and their color depths were calculated by five color depth formulas. Then, trend analysis and mathematical modeling methods were used to achieve the connection between the color depth and the notations of Natural Color System basing on color depth theories. Results show that, in Natural Color System, the color samples with the same distance to pure white do not have equal depth; but the color samples with the same nuance (equal blackness, whiteness and chromaticness) have broadly equal color depth, and their average coefficient values are lower than that of Society of Dyers and Colourists Standard Depths. Besides, regressive formulas were built, with which the color depths of any chips in Natural Color System can be calculated broadly by their notation.