Variability in Unique Hue Selection: A Surprising Phenomenon

Data from ten different experiments involving nearly 600 observers of determination of unique hues are compared. Six experiments involve determination using spectral lights; two use desaturated monitor colors, and the remaining two use color chip sets. Except for unique green, color chips result in narrower ranges of results than spectral lights. Unique green has a surprisingly large range of variation in both spectral light and color chip experiments, followed by red. Comparison of spectral light data indicates that one observer's unique blue can be another's unique green and vice versa, and the same for yellow and green. This finding raises significant questions for color appearance and color space/difference models, as well as philosophy of color. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 158–162, 2004;Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10237     

Analysis of five sets of color difference data

In a systematic optimization process five sets of recent color difference data have been analyzed for commonalities. Adjustment of the X tristimulus values and application of a systematic, surround dependent S_L function was found to be beneficial in all cases. Other modifications of the CIE94 color‐difference formula were found to bring improvements only in some cases and may be spurious. Application of what seem to be nonsystematic scale factors in a range of 0.78–1.38 improve correlation between calculated and visual color differences in all cases. After optimization, calculated color difference values explain between 80–90% of the variation in visual color differences. Some of the datasets are shown not to be well suited for formula optimization. Optimization in all cases by set, for three sets of data by quadrant in the a^*b^* diagram, and for one set by subset did not reveal any additional systematic trends for improvement. It appears that the basic structure of CIE94, with the recommended modifications, is a good approximation as a model for color‐difference evaluation in the range from 0.5–10 units of difference. The model is surround dependent. A number of issues remain to be resolved. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 141–150, 2001     

Preference for color‐enhanced images assessed by color deficiencies

In this study, we propose color images with color enhancement for congenital red–green color deficiency on the basis of the model proposed by Mochizuki et al. [In Terrassa, Spain: Proceedings for CGIV, Springfield VA: IS&T 2008;208–213] with the aim of facilitating color customization in displays used by color‐deficient observers. Three types of color enhancements for deficiency were addressed in this study: colors were enhanced along the protan confusion line (i.e., P‐enhancement), deutan confusion line (i.e., D‐enhancement), and a line between the protan and deutan confusion lines (i.e., mix‐enhancement). We investigated the color‐enhanced image that is most preferred by each group of protan, deutan, and normal observers. Protan and deutan in this study indicate observers that are both dichromats and anomalous trichromats. Nine protan, nine deutan, and six normal observers participated in the experiment. The results showed that among the three types of color enhancements, the D‐enhancement provided the best performance for protan observers. For deutan observers, all three types provided effective enhancement, although the P‐ and mix‐enhancements were slightly better than the D‐enhancement. Our results indicated that color enhancement increases the preference evaluations of protan and deutan observers. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 234–251, 2014; Published Online 20 February 2013 in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/col.21795     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017     

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     

Ultra‐large color difference and small subtense

Color difference calculations are usually applied to match or tolerance of small differences between large (>2°) visual fields. In contrast, we examine here the application of ultra‐large color differences to enhance conspicuousness and discriminability of small (1° subtense or smaller) visual targets, e.g., in visual information displays. We show that CIEDE2000, and color difference metrics based on the OSA Uniform Color Space and CIECAM02 are superior to CIELAB and CIELUV. Considering gray scale only, we show that Whittle's JND metric of achromatic contrast is as good as L* for this purpose, while also modeling contrast polarity and “crispening.” Furthermore, using this JND metric, we replicate Highnote's finding that elongation of small targets affects their apparent contrast. We discuss the perceived fading of color differences when targets become smaller, and suggest practical methods to mitigate the adverse effect on color conspicuousness and discriminability. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

Color appearance of a large homogenous visual field

In this article, the color appearance of a large (85°) homogeneous self‐luminous visual stimulus was studied in a psychophysical experiment. Large stimuli were displayed on a plasma display panal (PDP) monitor. The large stimuli were viewed with a fixed viewing time (2 s). They were compared with 2° and 10° stimuli presented on a grey background on a CRT monitor. The so‐called “color size effect” was found to be significant. The color stimulus was perceived to be lighter when it was large compared with the 2° and 10° situation. But we did not find the general increase of chroma claimed in previous literature. We found only small hue changes. A model of the color appearance of large‐field stimuli is presented in terms of the CIELAB L*, a*, and b* values of the corresponding 2° and 10° stimuli. © 2007 Wiley Periodicals, Inc. Col Res Appl, 33, 45–54, 2008     

Measurement of memory of color

Colors produced by monochromatic wavelengths of light viewed in isolation have been used as the only visual variables in short‐term delayed matching (DM) and long‐term recall (LTR) protocols to quantify three types of color memory in individuals with normal color vision. Measurements were normally distributed, so that color memories of individuals could be compared in terms of means and standard deviations. The variance of LTR of colors of familiar objects is shown to be separable into two portions, one due to “preferred colors” and the other due to individuals' precisions of matching. The wavelength dependence of DM exhibited minima of standard deviations at the same wavelengths as those reported for color discrimination measured by bipartite wavelength matching, and these wavelengths were shown to occur at the wavelengths of the intersections of cone spectral sensitivities. In an intermediate “green” region of relatively constant color discrimination, it was possible to combine DM measurements for different wavelengths for statistical analysis. The standard deviations of DM for individuals of a healthy population were normally distributed, providing a 95% upper confidence limit for identifying individuals with possible short‐term memory impairment. Preliminary measurements of standard deviations of DM for delay times of ≤ 1 s were consistent with a proposed rapidly decaying color imagery memory. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 233–242, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10067     

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.     

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.     

Categorical formation of Mandarin color terms at different luminance levels

This study presents the categorical formation of a set of Mandarin color terms on the International Commission on Illumination (CIE) 1931 chromaticity diagram across six luminance levels. This article conducted a study that employed 44 native Mandarin speakers to perform a force–choice sorting task. The Mandarin color terms for sorting were determined by a free‐recall pretest and are consistent with basic color terms proposed by Berlin and Kay. The square‐sampled stimuli were generated by evenly sweeping the x–y diagram of 5, 10, 25, 50, 100, and 170 cd/m² planes. The categorical sorting results and response time (RT) measurements suggest that: (1) the concepts of green, blue, purple, and gray stably exist at most luminance levels. The voting RT for the green, blue, and purple categories is particularly short. (2) Red, orange, yellow, and pink are highly luminance‐dependent; these can be identified without difficulty only at some restricted luminance levels. (3) The chromaticity areas designated as orange, partial yellow, red, and pink are recognized as brown when the luminance level decreases. (4) Brown and gray serve as representations of two distinct tints in the low saturation condition. (5) The location of boundaries between blue and green are remarkably different than those in a similar study that employed Japanese speakers. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011.     

Crosscultural comparison of color terms and preference of persimmon‐dyed fabric,Galchon

This crosscultural study was aimed at correlating color emotions and preference for persimmon‐dyed cotton fabrics known as Galchon. Cotton fabrics were dyed with persimmon powder, in a range of shades, and in some cases were also iron mordanted. Textile and fashion students from Jeju National University in Korea and North Carolina State University (NCSU) in USA participated in the visual assessment of dyed samples and were asked to scale their visual experience and state their emotion and preference for the terms “Bright,” “Heavy,” “Soft,” “Strong,” “Deep,” and “Like.” Korean observers used “Strong” for iron‐mordanted Galchon, and American observers did not associate “Bright” or “Deep” with weakly dyed fabrics. In addition to the subjective terms described, the color preference for samples was quantified using their CIE colorimetric attributes. For Korean observers, the results indicate a correlation between L^* and “Bright,” whereas for Americans a stronger correlation was obtained against “Soft.” American observers' results also show a relationship between C^* and the term “Warm,” especially for dyeings of Galchon at high concentrations. It was also found that iron mordanting affected responses from both groups but only influenced the color preference of Korean observers. © 2015 Wiley Periodicals, Inc. Col Res Appl, 40, 592–604, 2015     

Design and study of a color sensitivity function

If we study color reproduction, such as computer color matching or the appraisal of metametric index, we wish to understand the characteristic of color differences that are caused by the object spectral reflectivity change at each wavelength. If we simulate the light source, we wish to know the characteristics of color differences that are caused by change in relative power distribution of the light source at each wavelength; if we simulate a human eye instrument, we wish to know the characteristics of color differences that are caused by change in visual sense of human eyes at each wavelength. So, we define the color‐sensitivity functions of an object, a light source, and human eyes. According to the chromatic theory, the color‐sensitive functions of an object, a light source, and human eyes are defined in the widely used CIE1976 (L*a*b*) color space and color difference.¹ Their mathematical formulae are deduced. The three kinds of color‐sensitive functions are studied systematically and comprehensively in the whole color space. The characteristics of the color‐sensitive functions are summarized, and the mathematical models of the three kinds of color‐sensitive functions can be utilized in some fields such as computer color matching, simulation of a standard light source, and humans viewing a colorimeter. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 118–124, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20089     

Correlations between color attributes and children's color preferences

This study examined the role of color attributes (lightness and saturation) on children's color preferences for interior room colors. It also investigated children's most preferred colors among each of the five major hue families in the Munsell color system using scale‐models. Previous color preference studies have typically been done with small color chips or papers, which are very different from seeing a color applied on wall surfaces. A simulation method allowed for investigating the value of color in real contexts and controlling confounding variables. Forty‐five color samples were displayed on scale‐models to 63 children ages 7–11 years old. This study identified children's most preferred colors among each of the five major hue families in Munsell color system. It also demonstrated that saturation was positively correlated with children's preferences in the red, green, blue, and purple hue families. In the yellow hue family, interestingly, lightness has a positive correlation with preferences. Children's gender differences were found in that girls prefer red and purple more than boys. These findings lead to color application guidelines for designers to understand better color and eventually to create improved environments for children and their families. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 452–462, 2014     

Applications of vectorial color

A companion article introduced a set of orthonormal opponent color matching functions (CMFs). That “orthonormal basis” is an expedient for plotting lights in Cohen's logical color space. Indeed, graphing the new CMFs together (as a parametric plot) gives Cohen's invariant locus of unit monochromats (LUM). In this article, the functions and related vector methods are applied to fundamental problems. In signal transmission and propagation‐of‐errors work, it is desirable to describe stimuli by decorrelated components. The orthonormal CMFs inherently do this, and an example is worked out using a large set of color chips. Starting with the orthonormal functions, related functions, such as cone sensitivities, are graphed as directions in color space, showing their intrinsic relationships. Building on work of Tominaga et al., vectorial plots are related to the problem of guessing the illuminant, a step toward a constancy method. The issue of color rendering is clarified when the vectorial compositions of test and reference lights are graphed. A single graph shows the constraint that the total vectors are the same, but also shows the differences in colorimetric terms. Since the LUM summarizes a trichromatic system by a three‐dimensional graph, dichromatic observers can be represented by 2‐D graphs, revealing details in a consistent way. The “fit first method” compares camera with human, applying the Maxwell‐Ives criterion in graphical detail. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011.     

Effects of cold plasma on the color parameters of Hyssop (Hyssopus officinalis L.) using color imaging instrumentation and spectrophotometer

This research was conducted to evaluate the effects of cold atmospheric plasma treatment on the color of Hyssop (Hyssopus officinalis L.) and also to compare the usage of the spectrophotometer vs the color imaging instrumentation for the evaluation of the treatment on the color parameters. The experiments were investigated at different treatment times of 1, 5, and 10 minutes and the voltage values of 17, 20, and 23 kV. Possible changes of color were evaluated by using CIE L*a*b* values obtained with HunterLab colorimeter and CIE L*a*b* values obtained with a digital still camera (DSC) using digital image processing (MATLAB software). The values of L*, a*, and b* of the samples were obtained using both the methods. The results revealed that the L*, a*, and b* values of the treated Hyssop samples changed with increasing the treatment time and the voltage applied. Evaluating the interaction effects revealed that there was a significant difference in the (−a^*/b^* ) ratio. In addition, the results showed that the effects of all variables on the color parameters were significantly different in the case of the DSC using digital image processing. However, these effects were not significantly different using HunterLab colorimeter except for time variable and interaction effects of a* and (−a^*/b^* ) ratio. The lightest green color and the maximum chlorophyll content loss were observed for 23 kV applied over 10 minutes. Based on the results, the digital image processing can be used as a practical tool to study the variations at the color of dried Hyssop leaves after cold plasma treatment.     

A general framework to study the price‐color relationship in paintings with an application to Mark Rothko rectangular series

We propose a general framework to study the relationship between the price of a painting and its color‐related attributes. To this end we focus on four key aspects: dominant colors, features of the color palette, color harmony, and color emotions. We demonstrate the usefulness of this approach with an example based on Mark Rothko's post‐1950 paintings (the “rectangular” series), and auction data from the 1994 to 2018 period. We identify two distinct price‐color regimes in Rothko's market: (a) [1994‐2005], a period in which prices are explained mainly by the growing popularity of the artist regardless of the color attributes of the paintings sold; and (b) [2006‐2018], a period in which color‐related attributes explain most of the prices. Furthermore, we find that in this second period, the dominant colors and the diversity of the color palette, are by far the most relevant attributes that influence the price; color harmony and color emotions hold almost no explanatory power during this period. Finally, we propose a new metric based on the Herfindahl Index to describe color diversity; this metric seems to be promising at characterizing the effect of the color palette on the price of a painting.     

Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

The sizes for the perceptible or acceptable color difference measured with instruments vary by factors such as instrument, material, and color‐difference formula. To compensate for disagreement of the CIELAB color difference (ΔE^*_ab) with the human observer, the CIEDE2000 formula was developed. However, since this formula has no uniform color space (UCS), DIN99 UCS may be an alternative UCS at present. The purpose of this study was to determine the correlation between the CIELAB UCS and DIN99 UCS using dental resin composites. Changes and correlations in color coordinates (CIE L*,a*, and b* versus L₉₉, a₉₉, and b₉₉ from DIN99) and color differences (ΔE^*_ab and ΔE₉₉) of dental resin composites after polymerization and thermocycling were determined. After transformation into DIN99 formula, the a value (red–green parameter) shifted to higher values, and the span of distribution was maintained after transformation. However, the span of distribution of b values (yellow–blue parameter) was reduced. Although color differences with the two formulas were correlated after polymerization and thermocycling (r = 0.77 and 0.68, respectively), the color coordinates and color differences with DIN99 were significantly different from those with CIELAB. New UCS (DIN99) was different from the present CIELAB UCS with respect to color coordinates (a and b) and color difference. Adaptation of a more observer‐response relevant uniform color space should be considered after visual confirmation with dental esthetic materials. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 168–173, 2006     

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