Neural mechanisms of chromatic and achromatic vision

Building upon electrophysiological recordings from the lateral geniculate nucleus (LGN) of the macaque monkey, we describe a model for neural processing of color and brightness/lightness information that starts in the cone receptors and continues in the opponent cells of the retina, LGN, and visual cortex. The excitation of the three cone types to direct stimulation by light is modified in accordance with a hyperbolic response function before providing inputs to retinal ganglion cells. Using weighted differences of such cone outputs, we simulate the responses of common types of opponent ganglion and geniculate cells to light modulation along the chromatic and luminance dimensions. Extrapolating the results of the simulation, we suggest a way in which the brain might combine inputs from the geniculate to obtain correlates of chromatic and achromatic color vision and of brightness/lightness perception. In particular, we demonstrate for the first time how combinations of “L–M” and “M–L” parvocellular ON‐ and OFF‐opponent‐cells may lead to a quantitative account of brightness and blackness scaling. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 433–443, 2008     

The dependence of color constancy and brightness constancy on saturation

Color constancy and brightness constancy are not independent, as often assumed, since increasing sample saturation decreases the demand on color constancy mechanisms and increases the demand on brightness constancy mechanisms made by changes in illuminant color temperature. Re‐analyzed published data illustrates these tendencies for low saturations, but comprehensive measurements will be needed to pin down the postulated relationships over the full range.     

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     

On attributes of achromatic and chromatic object‐color perceptions

Adapting luminance dependencies of various color attributes of object colors (lightness, brightness, whiteness‐blackness, whiteness‐blackness strength, chroma, and colorfulness) were clarified under white illumination with various adapting illuminances. The correlation between the perceptions of lightness and brightness and those of whiteness‐blackness and whiteness‐blackness strength is also clarified for achromatic object colors. The difference between the increase of brightness and that of whiteness‐blackness contrast (the effect studied by Stevens and Jameson—Hurvich) by raising their adapting illuminance is resolved without any contradiction. It is also shown that the nonlinear color‐appearance model developed by the author and his colleagues is able to explain the complex characteristics of all the above color attributes of object colors by making minor modifications to it. In addition, two kinds of classifications of various color attributes are given; one is based on the similarity of perception level, and the other on the degree of adapting illuminance dependency. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 318–332, 2000     

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     

No measured effect of a familiar contextual object on color constancy

Some familiar objects have a typical color, such as the yellow of a banana. The presence of such objects in a scene is a potential cue to the scene illumination, since the light reflected from them should on average be consistent with their typical surface reflectance. Although there are many studies on how the identity of an object affects how its color is perceived, little is known about whether the presence of a familiar object in a scene helps the visual system stabilize the color appearance of other objects with respect to changes in illumination. We used a successive color matching procedure in three experiments designed to address this question. Across the experiments we studied a total of six subjects (two in Experiment 1, three in Experiment 2, and four in Experiment 3) with partial overlap of subjects between experiments. We compared measured color constancy across conditions in which a familiar object cue to the illuminant was available with conditions in which such a cue was not present. Overall, our results do not reveal a reliable improvement in color constancy with the addition of a familiar object to a scene. An analysis of the experimental power of our data suggests that if there is such an effect, it is small: less than approximately a change of 0.09 in a constancy index where an absence of constancy corresponds to an index value of 0 and perfect constancy corresponds to an index value of 1. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 347–359, 2014     

Color constancy,color‐mixing ability,and color inference

In the laboratory, the performance of color constancy has always been unstable. The variables posed by the subjects themselves tend to be neglected. In this study, a simple color‐mixing test was used to sort subjects into three groups. They were also provided with hue and brightness in a fixed color order as guidance during experimental tasks in an attempt to observe any changes in cognitive skills among subjects of varying color‐mixing abilities under changed illuminations. Three hypotheses were proposed: (1) Matching hypothesis: people remember and apply the spatial locations of color surrounds to the target color to achieve color constancy; (2) Comparing hypothesis: people identify the relative difference between color surrounds to achieve color constancy; and (3) Reasoning hypothesis: people compare the color surrounds and refer to color knowledge accumulated in the past to achieve color constancy through reasoning. The experimental results were as follows if the Comparing hypothesis is taken into account, (a) when hue guidance is hidden the subjects' identification rate (IR) performance supported the Reasoning hypothesis; (b) when hue guidance was shown, the IR performance of subjects in all three groups supported the Matching hypothesis. Based on these results, this study offers two recommendations: (a) color surrounds with a fixed relative location should be avoided because spatial location memory leading to people using matching skills and (b) subjects should be screened based on their color‐mixing ability because significant differences in color constancy performance exist between people with varying levels of color‐mixing ability. © 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     

Recognized visual space of illumination: A new account of center‐surround simultaneous color contrast

Appearances of an object color in a space are determined by a cortical representation of illuminant for a space or the recognized visual space of illumination (RVSI). The simultaneous color contrast phenomenon on a simple center‐surround configuration can be explained by RVSI. It is hypothesized that our visual system constructs an RVSI on the surround and then that RVSI determines color appearance of the center test. If this is correct, the color contrast can be quite strong when the surround is enlarged to be an enclosed space. To support the hypothesis, color appearance of a physical gray test was measured in a green surround of various sizes. Observers were asked to do elementary color naming in the first experiment. The results showed same tendency for all observers: once the surround was extended to walls, a ceiling, and a floor of a box, perceived chromaticness abruptly increased. In other words, three‐dimensional surround evoked strong simultaneous color contrast. In the second experiment the matching method was employed with the green and other three surround colors: red, blue, and yellow. The results were consistent with the first experiment. The well‐known color contrast is thought to be a weak version of this color change. It suggested that RVSI plays an important role in the well‐known color contrast demonstration on two‐dimensional planes. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 255–260, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20019     

Chroma,chromatic luminance,and luminous reflectance. Part II: Related models of chroma,colorfulness, and brightness

Part I of this article found, inter alia, that chroma resembles log inverted luminance. This article develops three math models of Munsell chroma and associated colorfulness from (1) inverted luminous reflectance Y, (2) inverted chromatic luminance, and (3) inverted chromatic luminance combined (over the mid‐spectrum 480–580 nm) with the unimodal curve for spectral absorptance of M cones. The first two models are simple but of limited accuracy and demonstrate that inverted luminance (of any form) cannot fully account for varying relative chroma around the hue cycle, particularly the minor minimum and maximum about 490 and 520 nm (which also feature in B:L functions). The third model is rather complex but very accurate, apparently the only accurate model of Munsell chroma or other experimentally based scales of relative chromaticness in the literature. It adjusts to any level of luminance or purity, as demonstrated for several levels. Three models of brightness (B:L ratio) for 2⁰ field aperture colors are given, based on either Munsell chroma or log inverted chromatic luminance. The former provides two accurate and simple models of the CIE B:L function: (1) log chroma = B:L ratio ±0.1, and (2) (chroma/k)^x = B:L ratio ±0.1. The latter also predicts B:L for nonspectral colors and those of lower purities, e.g., object colors. The results finally solve the relationship between brightness and chroma and demonstrate that B:L ratio (a contrast in constant luminance) arises directly from chroma (also a form of contrast in constant luminance), or the reverse. © 2008 Wiley Periodicals, Inc. Col Res Appl, 34, 55–67, 2009.     

Strong effect of the simultaneous color contrast in an afterimage

The color appearance of the afterimage of the simultaneous color contrast pattern was investigated by the elementary color naming method. The color appearances of the surrounding, an afterimage of the surrounding, and the test patch were measured, and the results were shown on the polar diagram of the opponent colors theory. The colors of both the surrounding and the afterimage of the test patch were the same. The relationship between the afterimage color of the test patch and the afterimage of the surrounding was found to be the same as the relationship between the illumination color and the test patch color in the two‐rooms technique, implying that the same visual mechanism works for both situations, that is, eyes chromatically adapt to the afterimage color of the surroundings, and the afterimage color of the test patch is determined with the eyes so adapted.     

Effects of hue,saturation, and brightness on color preference in social networks: Gender‐based color preference on the social networking site Twitter

Account information for over 1 million Twitter users was downloaded and analyzed to determine color preference. Blues were found to be the most preferred color, whereas greens were least preferred. Distinct gender‐specific differences were found. Males preferred blues to a greater extent than females, whereas females preferred magentas to a much greater extent than males. Males preferred darker colors to a greater extent than women. Density plots within hue, saturation, and brightness space summarize the distribution of color choices and illustrate color preferences for both males and females. © 2011 Wiley Periodicals, Inc. Col Res Appl, 38, 196–202, 2013.     

The relationship between visual acuity and color contrast in the OSA uniform color space

OSA uniform color space was used to study the relationship between visual acuity and OSA color contrast. Visual acuity is characterized by 50% minimal separable visual angle using Landolt-C. The OSA color contrast is characterized by the distance between colors in OSA color space. Twenty subjects with normal color vision were tested on 342 test sheets printed with colored Landolt-Cs and background. These results demonstrated that MSVA is approximately inverse log-linearly related to OSA color contrast (R² = 80.4%). Although luminance contrast (R² = 54.2%) is more salient than chromatic contrast (R² = 16.4%), both contrasts can induce very high visual acuity provided that they are sufficiently high. There is also evidence of an additive interaction between chromatic contrast and luminance contrast. Based on these findings, the OSA uniform color space and its color difference formula can be used as a scale for quantifying color contrast to accurately predict the size of colored text or symbols. © 1996 John Wiley & Sons, Inc.     

Sensor‐response‐ratio constancy under changes in natural and artificial illuminants

We have analyzed the constancy of the response ratio for cones, second‐stage mechanisms, and CCD sensors when daylight or an artificial illuminant (A, F2, F7, and F11) is changed to an equal‐energy illuminant (E) in scenes containing natural and artificial objects. The response ratios were always found to be roughly constant for all the sensors. For daylight, we have deduced mathematical expressions, which relate the values of these ratios with the correlated color temperature (CCT) and applied these expressions to the synthesis of images of a scene viewed under different daylights corresponding to a variety of CCTs. The results were highly satisfactory in a rural scene for any CCT. In the scene with artificial objects, the results were also good for nonextreme CCTs. We also included in our study artificial illuminants, with which we achieved very good image syntheses for illuminants A and F7. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 284–292, 2007     

Evaluation of color matching performances for SPEM and other models

We performed subjective experiments to evaluate color matching performance of the Spectral Properties Estimation Model (SPEM) and six other models (von Kries, CIELAB, LLAB, RLAB, Nayatani, and CIECAM97s) between two CRT monitors whose whites were quite different. Moreover, we evaluated color matching of these models between a CRT monitor and a printed image set in a dark room. The SPEM we developed is a new chromatic adaptation model based on hypothetical spectral properties estimation. This article describes the subjective experiments and the results obtained. The SPEM produced good color matching performance in the experiments. The detailed algorithm of the SPEM is given in the Appendix. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 445–453, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10197     

An algorithm for the selection of high‐contrast color sets

Nominal color coding is the aesthetic and functional use of color to convey qualitative information in graphical environments. The specification of high‐contrast color sets is a fundamental step in this process. We formulate the color‐coding problem here as a combinatorial optimization problem on graphs and present an algorithm that performs well and does not require that the function used to code the similarity between colors be a distance function. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 132–138, 1999