The luminance variations in images arise from complex interactions between surface optics, the illumination field, and intervening media. One of the fundamental goals of vision science is to explain how the visual system uses images to infer surface shape, albedo, and gloss. Recently, there has been a growing body of research that has tried to identify how the luminance variations in images determine the degree to which a surface appears glossy. Studies have approached this issue in three different ways: manipulating the surface and its illumination field, manipulating simple image statistics, or manipulating the locations of specular highlights.
One approach has evaluated how perceived gloss varies with parameters of the surface and the illumination field that can be simulated using ray tracing models. Using this approach, it has been shown that the degree to which a surface appears glossy depends on the observers' viewing direction (Obein, Knoblauch, & Vienot,
2004), the shape of the surface (Ho, Landy, & Maloney,
2008; Nishida & Shinya,
1998; Wijntjes & Pont,
2010), and the illumination field (Doerschner, Boyaci, & Maloney,
2010; Fleming, Dror, & Adelson,
2003; Olkkonen & Brainard,
2010). It has also been found that perceived gloss increases if the surface occupies different positions in the image over time (Sakano & Ando,
2010; Wendt, Faul, Ekroll, & Mausfeld,
2010) or in the two eyes' views (Wendt, Faul, & Mausfeld,
2008). However, it is not clear from these studies what information the visual system uses to infer surface gloss because each manipulation changed the image in a variety of complex ways.
A second approach has been to search for simple image statistics that are correlated with surface gloss (Motoyoshi, Nishida, Sharan, & Adelson,
2007; Nishida & Shinya,
1998). Motoyoshi et al. (
2007) argued that specular reflections typically generate positively skewed luminance histograms. They proposed that the visual system computes subband skew (or something “similar”) to infer surface gloss. Consistent with this proposal, they modulated perceived gloss by manipulating the histogram skew of images of glossy surfaces. However, it has been argued that the correlation between gloss and skew is limited to a restricted set of surface geometries, surface reflectance properties, and illumination fields (Anderson & Kim,
2009; Kim & Anderson,
2010). Consequently, histogram or subband skew cannot distinguish gloss from other possible sources of skew such as pigmentation, the illumination direction, and the surface geometry (see also Wijntjes & Pont,
2010).
A third approach has been to search for image cues that the visual system could use to distinguish specular highlights from other causes of luminance maxima (such as pigmentation or variations in surface geometry or illumination). The 3D shape of glossy surfaces strongly influences where specular highlights occur; therefore, specular highlights might be identified on the basis that they are “congruent” with shape information (Anderson & Kim,
2009; Beck & Prazdny,
1981; Blake & Bülthoff,
1990; Todd, Norman, & Mingolla,
2004). Several studies have manipulated the congruence of luminance maxima with shape information by repositioning specular highlights away from their rendered locations. Specular highlights were either rotated (Anderson & Kim,
2009; Beck & Prazdny,
1981), translated (Anderson & Kim,
2009), or their 3D depth was manipulated using binocular disparity (Blake & Bülthoff,
1990). Each of these manipulations diminished perceived gloss, which was attributed to an incompatibility of the relocated highlights with shape information.
Although the idea of using information about surface shape to identify specular highlights seems fruitful, more work is required to determine the image cues that the visual system uses to infer surface gloss. Below, we describe and test two image cues that the visual system might use to assess whether luminance maxima are congruent with diffuse shading gradients: orientation congruence and brightness congruence.