Averaging colors of multicolor mosaics

Perceiving the representative surface color of real-world materials

Natural surfaces such as soil, grass, and skin usually involve far more complex and heterogenous structures than the perfectly uniform surfaces assumed in studies on color and material perception. Despite this, we can easily perceive the representative color of these surfaces. Here, we investigated the visual mechanisms underlying the perception of representative surface color using 120 natural images of diverse materials and their statistically synthesized images. Our matching experiments indicated that the perceived representative color revealed was not significantly different from the Portilla-Simoncelli-synthesized images or phase-randomized images except for one sample, even though the perceived shape and material properties were greatly impaired in the synthetic stimuli. The results also showed that the matched representative colors were predictable from the saturation-enhanced color of the brightest point in the image, excluding the high-intensity outliers. The results support the notion that humans judge the representative color and lightness of real-world surfaces depending on simple image measurements.

Perception of saturation in natural objects

The distribution of colors across a surface depends on the interaction between its surface properties, its shape, and the lighting environment. Shading, chroma, and lightness are positively correlated: points on the object that have high luminance also have high chroma. Saturation, typically defined as the ratio of chroma to lightness, is therefore relatively constant across an object. Here we explored to what extent this relationship affects perceived saturation of an object. Using images of hyperspectral fruit and rendered matte objects, we manipulated the lightness-chroma correlation (positive or negative) and asked observers which of two objects appeared more saturated. Despite the negative-correlation stimulus having greater mean and maximum chroma, lightness, and saturation than the positive, observers overwhelmingly chose the positive as more saturated. This suggests that simple colorimetric statistics do not accurately represent perceived saturation of objects-observers likely base their judgments on interpretations about the cause of the color distribution.

Effect of nanostructural irregularities on structural color in the tail feathers of the Oriental magpie Pica serica

The tail feathers of magpies are iridescent, with hues ranging from navy to violet and green. It has been previously shown that the hexagonal arrangement of melanosomes in the distal barbules is responsible for these colors, but previous simulation models have relied on average values for the parameters associated with this arrangement (e.g., periodicity), and it remains to be studied whether the actual (rather than averaged) structural arrangement and its inherent irregularities reliably predict structural color. Previous studies using unmodified images for the analysis have not focused on the effect of such irregularities on the color production. In this study, we conducted finite-difference time-domain (FDTD) simulations using actual transmission electron microscopy (TEM) images obtained from the distal barbules of a magpie tail feather, compared the reflectance spectra predicted using the FDTD simulation with those measured with a spectrometer, and found a substantial discrepancy between the two. Fourier analysis suggests that the non-uniform arrangement of the melanosomes within the barbule is responsible for this discrepancy by creating variation in the periodicity. Our results suggest that a simple model in which the parameters for internal structures are averaged cannot fully explain the variation in the structural colors observed in biological samples such as the feathers of birds.

Task-driven and flexible mean judgment for heterogeneous luminance ensembles

Spatial averaging of luminances over a variegated region has been assumed in visual processes such as light adaptation, texture segmentation, and lightness scaling. Despite the importance of these processes, how mean brightness can be computed remains largely unknown. We investigated how accurately and precisely mean brightness can be compared for two briefly presented heterogeneous luminance arrays composed of different numbers of disks. The results demonstrated that mean brightness judgments can be made in a task-dependent and flexible fashion. Mean brightness judgments measured via the point of subjective equality (PSE) exhibited a consistent bias, suggesting that observers relied strongly on a subset of the disks (e.g., the highest- or lowest-luminance disks) in making their judgments. Moreover, the direction of the bias flexibly changed with the task requirements, even when the stimuli were completely the same. When asked to choose the brighter array, observers relied more on the highest-luminance disks. However, when asked to choose the darker array, observers relied more on the lowest-luminance disks. In contrast, when the task was the same, observers' judgments were almost immune to substantial changes in apparent contrast caused by changing the background luminance. Despite the bias in PSE, the mean brightness judgments were precise. The just-noticeable differences measured for multiple disks were similar to or even smaller than those for single disks, which suggested a benefit of averaging. These findings implicated flexible weighted averaging; that is, mean brightness can be judged efficiently by flexibly relying more on a few items that are relevant to the task.

Color ensembles: Sampling and averaging spatial hue distributions

Color serves both to segment a scene into objects and background and to identify objects. Although objects and surfaces usually contain multiple colors, humans can readily extract a representative color description, for instance, that tomatoes are red and bananas yellow. The study of color discrimination and identification has a long history, yet we know little about the formation of summary representations of multicolored stimuli. Here, we characterize the human ability to integrate hue information over space for simple color stimuli varying in the amount of information, stimulus size, and spatial configuration of stimulus elements. We show that humans are efficient at integrating hue information over space beyond what has been shown before for color stimuli. Integration depends only on the amount of information in the display and not on spatial factors such as element size or spatial configuration in the range measured. Finally, we find that observers spontaneously prefer a simple averaging strategy even with skewed color distributions. These results shed light on how human observers form summary representations of color and make a link between the perception of polychromatic surfaces and the broader literature of ensemble perception.

Color variance and achromatic settings

The average color in a scene is a potentially important cue to the illuminant and thus for color constancy, but it remains unknown how well and in what ways observers can estimate the mean chromaticity. We examined this by measuring the variability in "achromatic" settings for stimuli composed of different distributions of colors with varying contrast ranges along the luminance, SvsLM, and LvsM cardinal axes. Observers adjusted the mean chromaticity of the palette to set the average to gray. Variability in the settings increased as chromatic contrast or (to a lesser extent) luminance contrast increased. Signals along the cardinal axes are relatively independent in many detection and discrimination tasks, but showed strong interference in the white estimates. This "cross-masking" and the effects of chromatic variance in general may occur because observers cannot explicitly perceive or represent the mean of a set of qualitatively different hues (e.g., that red and green hues average to gray), and thus may infer the mean only indirectly (e.g., from the relative saturation of different hues).

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."