Color ensembles: Sampling and averaging spatial hue distributions

A Bayesian observer model reveals a prior for natural daylights in hue perception

Incorporating statistical characteristics of stimuli in perceptual processing can be highly beneficial for reliable estimation from noisy sensory measurements but may generate perceptual bias. According to Bayesian inference, perceptual biases arise from the integration of internal priors with noisy sensory inputs. In this study, we used a Bayesian observer model to derive biases and priors in hue perception based on discrimination data for hue ensembles with varying levels of chromatic noise. Our results showed that discrimination thresholds for isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space exhibited a bimodal pattern, with lowest thresholds near a non-cardinal blue-yellow axis that aligns closely with the variation of natural daylights. Perceptual biases showed zero crossings around this axis, indicating repulsion away from yellow and attraction towards blue. These biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our findings suggest that visual processing takes advantage of knowledge of the distribution of colors in natural environments for hue perception.

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.

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

Ensemble coding of color and luminance contrast

Ensemble coding has been demonstrated for many attributes including color, but the metrics on which this coding is based remain uncertain. We examined ensemble percepts for stimulus sets that varied in chromatic contrast between complementary hues, or that varied in luminance contrast between increments and decrements, in both cases focusing on the ensemble percepts for the neutral gray stimulus defining the category boundary. Each ensemble was composed of 16 circles with four contrast levels. Observers saw the display for 0.5 s and then judged whether a target contrast was a member of the set. False alarms were high for intermediate contrasts (within the range of the ensemble) and fell for higher or lower values. However, for ensembles with complementary hues, gray was less likely to be reported as a member, even when it represented the mean chromaticity of the set. When the settings were repeated for luminance contrast, false alarms for gray were higher and fell off more gradually for out-of-range contrasts. This difference implies that opposite luminance polarities represent a more continuous perceptual dimension than opponent-color variations, and that "gray" is a stronger category boundary for chromatic than luminance contrasts. For color, our results suggest that ensemble percepts reflect pooling within rather than between large hue differences, perhaps because the visual system represents hue differences more like qualitatively different categories than like quantitative differences within an underlying color "space." The differences for luminance and color suggest more generally that ensemble coding for different visual attributes might depend on different processes that in turn depend on the format of the visual representation.