A simple principled approach for modeling and understanding uniform color metrics

Colour vision is aligned with natural scene statistics at 4 months of age

Visual perception in adult humans is thought to be tuned to represent the statistical regularities of natural scenes. For example, in adults, visual sensitivity to different hues shows an asymmetry which coincides with the statistical regularities of colour in the natural world. Infants are sensitive to statistical regularities in social and linguistic stimuli, but whether or not infants' visual systems are tuned to natural scene statistics is currently unclear. We measured colour discrimination in infants to investigate whether or not the visual system can represent chromatic scene statistics in very early life. Our results reveal the earliest association between vision and natural scene statistics that has yet been found: even as young as 4 months of age, colour vision is aligned with the distributions of colours in natural scenes. RESEARCH HIGHLIGHTS: We find infants' colour sensitivity is aligned with the distribution of colours in the natural world, as it is in adults. At just 4 months, infants' visual systems are tailored to extract and represent the statistical regularities of the natural world. This points to a drive for the human brain to represent statistical regularities even at a young age.

Gaining the system: limits to compensating color deficiencies through post-receptoral gain changes

Color percepts of anomalous trichromats are often more similar to normal trichromats than predicted from their receptor spectral sensitivities, suggesting that post-receptoral mechanisms can compensate for chromatic losses. The basis for these adjustments and the extent to which they could discount the deficiency are poorly understood. We modeled the patterns of compensation that might result from increasing the gains in post-receptoral neurons to offset their weakened inputs. Individual neurons and the population responses jointly encode luminance and chromatic signals. As a result, they cannot independently adjust for a change in the chromatic inputs, predicting only partial recovery of the chromatic responses and increased responses to achromatic contrast. These analyses constrain the potential sites and mechanisms of compensation for a color loss and characterize the utility and limits of neural gain changes for calibrating color vision.

Using Smooth Metamers to Estimate Color Appearance Metrics for Diverse Color-Normal Observers

Color-normal subjects sometimes disagree about metameric matches involving highly structured SPDs, because their cone fundamentals differ slightly, but non-negligibly. This has significant implications for the design of light sources and displays, so it should be estimated. We propose a broadly applicable estimation method based on a simple adaptive "front-end" interface that can be used with any selected standard color appearance model. The interface accepts, as input, any set of color matching functions for the individual subject (for example, these could be that person's cone response functions) and also the associated tristimulus values for the test stimulus and also for the reference stimulus (i.e. reference white). The interface converts this data into tristimulus values of the form used by the selected color appearance model (which could, for example, be X, Y, Z), while also carrying out the needed transform, which is based on an estimate of the subject's likely previous long-term adaptations to their unique cone fundamentals. The selected standard color appearance model then provides color appearance data that is an estimate of the color appearance of the test stimulus, for that individual subject. This information has the advantage of being interpretable within that model's well-known color space. The adaptive front end is based on the fact that, for any selected input SPD and the subject's unique color matching functions, there can be many different SPDs that are metameric for that individual. Since observer-to-observer color perception differences are minimized for spectrally smooth SPDs, smooth metamers predict color appearances reasonably accurately.

Temporal dynamics of the neural representation of hue and luminance polarity

Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

Metamer mismatching underlies color difference sensitivity

Color difference sensitivity as represented by the size of discrimination ellipsoids is known to depend on where the colors reside within color space. In the past, various color spaces and color difference formulas have been developed as parametric fits to the experimental data with the goal of establishing a color coordinate system in which equally discriminable colors are equal distances apart. These empirical models, however, provide no explanation as to why color discrimination varies in the way it does. This article considers the hypothesis that the variation in color discrimination tolerances reflects the uncertainty created by the degree of metamer mismatching for a given color. Specifically, the greater the degree of metamer mismatching for a color, the wider the range of spectral reflectances that could have led to it and, hence, the more finely a color needs to be discriminated in order to reliably identify materials and objects. To test this hypothesis, the available color discrimination data sets for surface colors are gathered and analyzed. A strong correlation between color discrimination and the degree of metamer mismatching is found. This correlation provides evidence that metamer mismatching provides an explanation as to why color discrimination varies throughout color space as it does.

Color appearance model incorporating contrast adaptation - implications for individual differences in color vision

Color appearance models use standard color matching functions to derive colorimetric information from spectral radiometric measurements of a visual environment, and they process that information to predict color perceptual attributes such as hue, chroma and lightness. That processing is usually done by equations with fixed numerical coefficients that were predetermined to yield optimal agreement for a given standard observer. Here we address the well-known fact that, among color-normal observers, there are significant differences of color matching functions. These cause disagreements between individuals as to whether certain colors match, an important effect that is often called observer metamerism. Yet how these individual sensitivity differences translate into differences in perceptual metrics is not fully addressed by many appearance models. It might seem that appearance could be predicted by substituting an individual's color matching functions into an otherwise-unchanged color appearance model, but this is problematic because the model's coefficients were not optimized for the new observer. Here we explore a solution guided by the idea that processes of adaptation in the visual system tend to compensate color perception for differences in cone responses and consequent color matching functions. For this purpose, we developed a simple color appearance model that uses only a few numerical coefficients, yet accurately predicts the perceptual attributes of Munsell samples under a selected standard lighting condition. We then added a feedback loop to automatically adjust the model coefficients, in response to switching between cone fundamentals simulating different observers and color matching functions. This adjustment is intended to model long term contrast adaptation in the vision system by maintaining average overall color contrast levels. Incorporating this adaptation principle into color appearance models could allow better assessments of displays and illumination systems, to help improve color appearances for most observers.

Color Vision: Decoding Color Space

A new study has used magnetoencephalography to track cortical responses to color as they emerge in time. Similarities and differences within these neural responses parallel characteristics of the perceptual experience of color.

Color Space Geometry Uncovered with Magnetoencephalography

The geometry that describes the relationship among colors, and the neural mechanisms that support color vision, are unsettled. Here, we use multivariate analyses of measurements of brain activity obtained with magnetoencephalography to reverse-engineer a geometry of the neural representation of color space. The analyses depend upon determining similarity relationships among the spatial patterns of neural responses to different colors and assessing how these relationships change in time. We evaluate the approach by relating the results to universal patterns in color naming. Two prominent patterns of color naming could be accounted for by the decoding results: the greater precision in naming warm colors compared to cool colors evident by an interaction of hue and lightness, and the preeminence among colors of reddish hues. Additional experiments showed that classifiers trained on responses to color words could decode color from data obtained using colored stimuli, but only at relatively long delays after stimulus onset. These results provide evidence that perceptual representations can give rise to semantic representations, but not the reverse. Taken together, the results uncover a dynamic geometry that provides neural correlates for color appearance and generates new hypotheses about the structure of color space.

The Verriest Lecture: Adventures in blue and yellow

Conventional models of color vision assume that blue and yellow (along with red and green) are the fundamental building blocks of color appearance, yet how these hues are represented in the brain and whether and why they might be special are questions that remain shrouded in mystery. Many studies have explored the visual encoding of color categories, from the statistics of the environment to neural processing to perceptual experience. Blue and yellow are tied to salient features of the natural color world, and these features have likely shaped several important aspects of color vision. However, it remains less certain that these dimensions are encoded as primary or "unique" in the visual representation of color. There are also striking differences between blue and yellow percepts that may reflect high-level inferences about the world, specifically about the colors of light and surfaces. Moreover, while the stimuli labeled as blue or yellow or other basic categories show a remarkable degree of constancy within the observer, they all vary independently of one another across observers. This pattern of variation again suggests that blue and yellow and red and green are not a primary or unitary dimension of color appearance, and instead suggests a representation in which different hues reflect qualitatively different categories rather than quantitative differences within an underlying low-dimensional "color space."

Methods for Assessing Quantity and Quality of Illumination

Human vision provides useful information about the shape and color of the objects around us. It works well in many, but not all, lighting conditions. Since the advent of human-made light sources, it has been important to understand how illumination affects vision quality, but this has been surprisingly difficult. The widespread introduction of solid-state light emitters has increased the urgency of this problem. Experts still debate how lighting can best enable high-quality vision-a key issue since about one-fifth of global electrical power production is used to make light. Photometry, the measurement of the visual quantity of light, is well established, yet significant uncertainties remain. Colorimetry, the measurement of color, has achieved good reproducibility, but researchers still struggle to understand how illumination can best enable high-quality color vision. Fortunately, in recent years, considerable progress has been made. Here, we summarize the current understanding and discuss key areas for future study.

Hyperspectral imaging in color vision research: tutorial

This tutorial offers an introduction to terrestrial and close-range hyperspectral imaging and some of its uses in human color vision research. The main types of hyperspectral cameras are described together with procedures for image acquisition, postprocessing, and calibration for either radiance or reflectance data. Image transformations are defined for colorimetric representations, color rendering, and cone receptor and postreceptor coding. Several example applications are also presented. These include calculating the color properties of scenes, such as gamut volume and metamerism, and analyzing the utility of color in observer tasks, such as identifying surfaces under illuminant changes. The effects of noise and uncertainty are considered in both image acquisition and color vision applications.