Surface color and color constancy

Color constancy is often treated as the tendency of surfaces to stay the same perceived color under changing illumination or context (removing/adding/replacing surrounding objects). But these types of color constancies are not basic ones and there is another kind of color constancy that is fundamental for the explanation of all color constancy phenomena. We experience it when looking at a curved uniformly colored surface or when changing the shape of the surface. A new concept of surface color is developed and the variety of all perceived colors is suggested to be described as a nine-dimensional set of 3 X 3 matrices corresponding to different surface colors. Examples of color matrices calculated for some colored surfaces being viewed by the standard viewer are presented and arguments supporting the concept are discussed. It is shown that the set of color matrices represents all perceived colors quite adequately.     

Complementary colors theory of color vision: Physiology,color mixture,color constancy and color perception

I describe complementary colors' physiology and functional roles in color vision, in a three‐stage theory (receptor, opponent color, and complementary color stages). 40 specific roles include the complementary structuring of: S and L cones, opponent single cells, cardinal directions, hue cycle structure, hue constancy, trichromatic color mixture, additive/subtractive primaries, two unique hues, color mixture space, uniform hue difference, lightness‐, saturation‐, and wavelength/hue‐discrimination, spectral sensitivity, chromatic adaptation, metamerism, chromatic induction, Helson‐Judd effect, colored shadows, color rendering, warm‐cool colors, brilliance, color harmony, Aristotle's flight of colors, white‐black responsivity, Helmholtz‐Kohlrausch effect, rainbows/halos/glories, dichromatism, spectral‐sharpening, and trimodality of functions (RGB peaks, CMY troughs whose complementarism adapts functions to illuminant). The 40 specific roles fall into 3 general roles: color mixture, color constancy, and color perception. Complementarism evidently structures much of the visual process. Its physiology is evident in complementarism of cones, and opponent single cells in retina, LGN, and cortex. Genetics show our first cones were S and L, which are complementary in daylight D65, giving a standard white to aid chromatic adaptation. M cone later split from L to oppose the nonspectral (red and purple) hues mixed from S+L. Response curves and wavelength peaks of cones L, S, and (S+L), M, closely resemble, and lead to, those of opponent‐color chromatic responses y, b, and r, g, a bimodal system whose summation gives spectral‐sharpened trimodal complementarism (RGB peaks, CMY troughs). Spectral sharpening demands a post‐receptoral, post‐opponent‐colors location, hence a third stage. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

Precise color capture using custom color targets

We have developed a procedure to achieve a high accuracy of color capture for digital still cameras for cultural heritage digitization. The procedure uses a standard color target and additional spectral measurements directly on the artwork. A set of complementary colors is determined thanks to an initial analysis of the digital image and a comparison with the standard color target. The developed procedure results in a digital copy that better represents the colors of the original than using only standard color targets.     

Product color emotional design considering color layout

Emotional experience and demand for product colors are important factors in users' decisions to buy and use a product. Therefore, accurately characterizing users' emotional responses of the product's color has become a significant consideration for product color design. However, a product color design problem exists in which it is challenging to accurately and efficiently position users' color image space because consumers have completely different image perceptions when encountering a large number of color schemes generated by different color spatial distributions. For this reason, this article proposes a product color emotional design method that considers the color layout. A table is built of an elemental composition for product color design, which contains color layout forms. This article also establishes a mapping model based on the semantic difference and back propagation neural network between the users' color image perceptions and the elements of the product's color design. The system recommends a color layout form that matches the users' emotional image goal through the k‐nearest neighbor algorithm, and then the form is initialized using a genetic algorithm. The system can realize the solution to the optimal product color scheme by optimizing and evaluating the population. Designers can make intuitive choices and decisions through the product color recommendation system. Through an example of color design for industrial vacuum cleaners, this article shows that the method has satisfactory feasibility and applicability for solving the problem of the optimization of product color design with color layout forms.     

The munsell color system in fundamental color space

The Munsell color system is investigated as a structure in fundamental color space. The entire collection of samples from the 1929 Munsell Book of Color is mapped into fundamental color space and surfaces of constant hue, value, and chroma are identified. An algorithm, based on these surfaces, is presented for estimating the Munsell specification of an arbitrary reflectance curve.     

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     

Vectorial color

A set of orthonormal color matching functions is developed, in which the first is an all‐positive achromatic function, the second is red–green, and the third can be loosely described as blue–yellow. The achromatic function, proportional to the familiar $ \bar y $ , is a sum of red and green cones. The red–green function uses the same cone sensitivities, but subtracted, with coefficients so that it is orthogonal to the achromatic one. The third function involves all three cones, but is primarily a blue sensitivity. Using this basis to compute the tristimulus vectors of narrow‐band lights at unit power gives Jozef Cohen's locus of unit monochromats, (LUM) an invariant shape now graphed in a space where the axes have intuitive meaning. The extreme points of the LUM reveal the wavelengths that act most strongly in mixtures, a close approximation to William Thornton's Prime Colors. In effect, decades of research converge in three functions and a vectorial schema, demystifying such issues as color rendering and the selection of additive primaries. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011.     

Cognitive color

This report surveys cognitive aspects of color in terms of behavioral, neuropsychological, and neurophysiological data. Color is usually defined as a color stimulus or as perceived color. In this article, a definition of the concept of cognitive color is formulated. To elucidate this concept, those visual tasks are described where it is relevant: in color categorization, color coding, color naming, the Stroop effect, spatial organization of colored visual objects, visual search, and color memory. © 2003 Wiley Periodicals, Inc. Col Res Appl, 29, 7–19, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10209     

Influence of a holistic color interval on color harmony

This study investigates how a holistic color interval, i.e., the nondirectional color difference between a pair of colors in a CIELAB uniform color space, influences perceived color harmony. A set of 1035 test color pairs displayed on a CRT was evaluated for the degree of harmony. These test color pairs consist of pairs combined from among the selected 46 test colors evenly distributed in color space. The subjects were asked to select their three preferred colors from these 46 test colors and then to evaluate the degree of harmony of the test color combinations. The color intervals (ΔE) of each test color combination were calculated and treated as values of an independent variable. In addition, the evaluated degrees of color harmony were considered as values of a dependent variable, in which statistical analysis confirmed the relationship: the degree of harmony is a cubic function of the color interval. Moreover, the plot of this relationship allowed us to identify four color intervals: roughly corresponding to the regions of first ambiguity, similarity, second ambiguity, and contrast in Moon and Spencer's model. However, our results indicated that Moon and Spencer's principles for classifying harmonious/disharmonious regions in terms of the color interval for three color attributes—lightness, chroma and hue—may be inappropriate in predicting perceived color harmony. As for the color intervals between a pair of colors considered as a function of the three attributes, the interval for lightness may have a predominant effect on color harmony, expressed in terms of a cubic relationship. Results of the study further demonstrated that the subject's choice of colors significantly influences perceived color harmony. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 29–39, 2001     

Dependence between apparent color and extractable color in paprika

The color properties of 96 paprika samples were evaluated by tristimulus reflectance measurements. The extractable color (ASTA units) of all these samples was also determined. The linear correlation between individual CIELAB parameters and extractable color was very poor. Several color indices used with other foods were shown to be of insufficient accuracy for predicting the extractable color in paprika. A new color index for paprika (PACI) is proposed based on the CIELAB coordinates L* (lightness), a* (red‐blue), and h (hue angle), and it is calculated as “1000a*/(L*+h)”. This new index showed a high correlation with the logarithm of extractable color (r = 0.9662) and was able to distinguish between sample groups of different ASTA units. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 93–97, 1999     

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.     

Image color adjustment for harmony with a target color

Although a number of methods have been developed for image adjustment in various applications, very little work has been done in the context of visual design. In this regard, this article introduces a novel and practical context of image color adjustment and develops a method to adjust an image for harmony with a target color. The experiment with designers revealed that designers made significant changes in hue dimension, and preferred to promote color similarity between the image and the target color. Based on insights from designers, we proposed a method to achieve a harmonious combination of an image and a color element by increasing the hue similarity between them. The result of a user test revealed that our method is particularly useful for images with nonliving objects but less effective for images involving human skin, foods, and so on. It is expected that the practical context investigated in this study can promote a variety of related studies that satisfy the tangible needs of industries and academia.     

Spectral color reproduction minimizing spectral and perceptual color differences

In this article, we are combining minimization criteria in the colorant separation process for spectral color reproduction. The colorant separation is performed by inverting a spectral printer model: the spectral Yule‐Nielsen modified Neugebauer model. The inversion of the spectral printer model is an optimization operation in which a criterion is minimized at each iteration. The approach we proposed minimizes a criterion defined by the weighted sum of a spectral difference and a perceptual color difference. The weights can be tuned with a parameter α ∞ [0, 1]. Our goal is to decrease the spectral difference between the original data and its reproduction and also to consider perceptual color difference under different illuminant conditions. In order to find the best α value, we initially compare a pure colorimetric criterion and a pure spectral criterion for the reproduction, then we combine them. We perform four colorant separations: the first separation will minimize the 1976 CIELAB color difference where four illuminants are tested, the second separation will minimize an equally weighted summation of 1976 CIELAB color difference with the four illuminants tested independently, the third colorant separation will minimize a spectral difference, and the fourth colorant separation will combine a weighted sum of a spectral difference and one of the two first colorimetric differences previously introduced. This last colorant separation can be tuned with a parameter in order to emphasize on spectral or colorimetric difference. We use a six colorants printer with artificial inks for our experiments. The prints are simulated by the spectral Yule‐Nielsen modified Neugebauer model. Two groups of data are used for our experiments. The first group describes the data printed by our printing system, which is represented by a regular grid in colorant space of the printer and the second group describes the data which is not originally produced by our printing system but mapped to the spectral printer gamut. The Esser test chart and the Macbeth Color Checker test chart have been selected for the second group. Spectral gamut mapping of this data is carried out before performing colorant separation. Our results show improvement for the colorant separations combining a sum of 1976 CIELAB color difference for a set of illuminants and for the colorant separation combining a sum of 1976 CIELAB color difference and spectral difference, especially in the case of spectral data originally produced by the printer. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 494–504, 2008     

Transferring the color imagery from an image: A color network model for assisting color combination

In order to transfer the color imagery from an image to another object to generate color schemes, a color network model is proposed. It consists of two subnets: source net for describing the color information of the reference image; target net for demonstrating the target object to be colored. Thus, the problem of reusing features of color patterns is translated into the mapping process from the source net to target net. Four indicators are designed to measure the conformity between the two nets: color sequence, adjacency, concentration, and the subspace size. In the meantime, a comprehensive aesthetic evaluation given by the designer is introduced to search the optimum combination of the four indicators to help generate imagery matching schemes. A prototype system is developed based on CorelDraw as a design tool. The feasibility of the color network model is then verified through a color combination of graphic design task.     

Judd's contributions to color metrics and evaluation of color differences

The contributions of Dr. Deane Brewster Judd to colorimetry extended over five decades. Among the most crucial contributions to the development of modern colorimetry were his studies of colorimetric purity, relations among color-mixture and luminosity data, visual sensibility to color differences, uniform chromaticity diagrams, Munsell notation and renotation, color names, color blindness and anomalies, color-vision theory, and uniform color scales.     

Derivation of a color space for image color difference measurement

In digital image reproduction, it is often desirable to compute image difference of reproductions and the original images. The traditional CIE color difference formula, designed for simple color patches in controlled viewing conditions, is not adequate for computing image difference for spatially complex image stimuli. Zhang and Wandell [Proceedings of the SID Symposium, 1996; p 731–734] introduced the S‐CIELAB model to account for complex color stimuli using spatial filtering as a preprocessing stage. Building on S‐CIELAB, iCAM was designed to serve as both a color appearance model and also an image difference metric for complex color stimuli [IS&T/SID 10th Color Imaging Conference, 2002; p 33–38]. These image difference models follow a similar image processing path to approximate the behavior of human observers. Generally, image pairs are first converted into device‐independent coordinates such as CIE XYZ tristimulus values or approximate human cone responses (LMS), and then further transformed into opponent‐color channels approximating white‐black, red‐green, and yellow‐blue color perceptions. Once in the opponent space, the images are filtered with approximations of human contrast sensitivity functions (CSFs) to remove information that is invisible to the human visual system. The images are then transformed back to a color difference space such as CIELAB, and pixel‐by‐pixel color differences are calculated. The shape and effectiveness of the CSF spatial filters used in this type of modeling is highly dependent on the choice of opponent color space. For image difference calculations, the ideal opponent color space would be both linear and orthogonal such that the linear filtering is correct and any spatial processing on one channel does not affect the others. This article presents a review of historical opponent color spaces and an experimental derivation of a new color space and corresponding spatial filters specifically designed for image color difference calculations. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Optimization of color reproduction on CRT‐color monitors

In the present experimental study, we quantify the influence of the brightness and contrast levels of a CRT‐color monitor in the color reproduction of 60 Munsell chips distributed throughout the chromatic diagram. The images were captured by two CCD cameras, and the color differences were evaluated after reproducing the chips on a color monitor (the experiment was performed with 3 different monitors) for 9 combinations of brightness‐contrast levels. We evaluated the color differences with 3 different formulas: CIELAB, CIELUV, and CIE94. The results indicate that the optimal settings of a monitor, to minimize the color differences, is a medium or minimum brightness level in combination with a maximum contrast level. This combination ensures a more faithful color reproduction with respect to the original image. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 207–213, 1999     

Comparison of color vision models based on spectral color representation

Recent break‐throughs in retinal imaging have raised new questions for color vision research, and the existing color vision models should be re‐evaluated. Many color vision models are based on an assumption that there are no differences in the detection phase, neither in the spatial configuration nor in the spectral sensitivities of cells. In this article, we have run experiments with four different color vision models. This evaluation gives us more knowledge about the essential properties of the models. We show how the tested color vision models are able to replicate the behaviour of human color vision by evaluating their performance in Farnsworth‐Munsell 100‐Hue color vision test. Also, the wavelength discrimination power of each model is presented and the properties of color spaces spanned by models are examined using samples from Munsell Book of Color. Our experiments show that there are large differences in the properties of different models. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 341–350, 2009     

Observer variability in metameric color matches using color reproduction media

Standard color-matching functions are designed to represent the mean color-matching response of the population of human observers with normal color vision. When using these functions, two questions arise. Are they an accurate representation of the population? And what is the uncertainty in color-match predictions? To address these questions in the dual context of human visual performance and cross-media reproduction, a color-matching experiment was undertaken in which twenty observers made matches between seven different colors presented in reflective and transmissive color reproduction media and a CRT display viewed through an optical apparatus that produced a simple split-field stimulus. In addition, a single observer repeated the experiment 20 times to estimate intra-observer variability. The results were used to evaluate the accuracy of three sets of color-matching functions, to quantify the magnitude of observer variability, and to compare intra- and inter-observer variability in color-matching. These results are compared with various techniques designed to predict the range of color mismatches. The magnitude of observer variability in this experiment also provides a quantitative estimate of the limit of cross-media color reproduction accuracy that need not be exceeded. On average, the differences between matches made by two different observers was approximately 2.5 CIELAB units. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 174–188, 1997