Abstract
The apparent glossiness of a surface depends not only on the intrinsic reflectance properties of the surface itself, but also on various extrinsic scene factors that drastically alter the pattern of reflected light. For example, a perfectly visible reflectance difference in ideal viewing conditions can become invisible in degenerate viewing conditions. This makes it challenging to define thresholds for discriminating differences in specular reflectance characteristics (‘gloss sensitivity’). Here we tested how well image-computable visual discrimination models predict human gloss discrimination performance across diverse viewing conditions, as a key step towards establishing a principled definition of gloss sensitivity. While previous studies of gloss perception have typically varied surface reflectance along with one other extrinsic variable of interest, here we focus exclusively on the effect of surface shape, illumination and viewpoint on the perception of a single, fixed difference in microscopic surface roughness. Smooth surfaces yield sharp reflections while rougher surfaces produce blurrier reflections, and a less glossy appearance. We rendered pairs of images depicting an object with a fixed specular reflectance and high or low surface roughness in a variety of shape, illumination and viewpoint combinations, and collected pair comparisons from human observers (N=100) in an online experiment. These judgments were used to rank each scene according to how often each image pair appeared to depict a larger difference in gloss. We find that these judgments are highly consistent across observers, and that the ranking of scenes is well predicted by the High Dynamic Range Visible Difference Predictor, a widely used model of image quality and perceived image differences. This result suggests that such a metric could be used to estimate upper and lower bounds of gloss sensitivity across viewing conditions, which is an important step towards establishing a standard measurement framework for gloss.