The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.

Natural surfaces, such as those of food and drink, have translucent properties. Translucent materials involve complex optics, such as sub-surface scattering and refraction, but humans can easily distinguish them from opaque materials. Here, we investigated image features that are diagnostic of the perceived translucency and transparency, focusing on the fact that variations in the opacity of a surface affect largely the non-specular component (shading pattern) of an image and little the specular component (highlights). In a simple rating experiment with computer-generated objects, we show that the non-specular image component tends to be blurred, faint, and even partially contrast-reversed for objects that appear more translucent or transparent. A subsequent experiment further demonstrated that manipulation of the contrast and blur of the non-specular image component dramatically alters the apparent translucency of an opaque object. The results support the notion that the spatial and contrast relationship between specular highlights and non-specular shading patterns is a robust cue for the perceived translucency and transparency of three-dimensional objects.

Human observers can distinguish the albedo of real-world surfaces even when the surfaces are viewed in isolation, contrary to the Gelb effect. We sought to measure this ability and to understand the cues that might underlie it. We took photographs of complex surfaces such as stucco and asked observers to judge their diffuse reflectance by comparing them to a physical Munsell scale. Their judgments, while imperfect, were highly correlated with the true reflectance. The judgments were also highly correlated with certain image statistics, such as moment and percentile statistics of the luminance and subband histograms. When we digitally manipulated these statistics in an image, human judgments were correspondingly altered. Moreover, linear combinations of such statistics allow a machine vision system (operating within the constrained world of single surfaces) to estimate albedo with an accuracy similar to that of human observers. Taken together, these results indicate that some simple image statistics have a strong influence on the judgment of surface reflectance.

In contrast to the classical findings of lightness constancy, recent psychophysical studies show the strong dependency of the perceived reflectance of a surface on the structure of the natural illumination. The present study examined this inconstancy for systematic variations in the light field and an image-based explanation for it. Observers matched the specular and diffuse reflectance of a three-dimensional object in a complex scene under a fixed light field to that in the scene under different light fields with variable mean, contrast, and gamma. For the both specular and diffuse components, the matched reflectance was relatively constant against changes in the mean illuminance but varied extensively with changes in the contrast and gamma of the light field. We found that the matching data were well predicted by the similarity of the subband histograms of the images. The results support the notion that early spatial filtering can provide a unified account of both the constancy in the perceived surface reflectance against mean illuminance and the inconstancy for higher-order illumination statistics.

It is well known that prolonged observation of a dynamic visual pattern raises the contrast threshold for a subsequently presented static pattern. We found that if the post-adaptation test was presented gradually, so that its onset transient was weak, the test pattern was undetectable even at high contrast. Although the smooth-onset patterns were invisible, they caused apparent shifts in the orientation and contrast of neighboring stimuli, indicating the implicit processing of the target features. However, this strong aftereffect was not obtained if the target grating drifted rapidly or was onset abruptly. These results suggest that when human observers become less sensitive to transients in stimuli due to dynamic adaptation, they cannot consciously perceive sluggish stimuli containing weak transients. This is consistent with the notion that the visual system cannot prompt a conscious awareness of a single stimulus unless triggered by enough transient or temporally salient signals.