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Laurence T. Maloney; Measuring and modeling visual appearance of surfaces. Journal of Vision 2009;9(8):33. doi: 10.1167/9.8.33.
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© ARVO (1962-2015); The Authors (2016-present)
Researchers studying visual perception have developed numerous experimental methods for probing the perceptual system. The range of techniques available to study performance near visual threshold is impressive and rapidly growing and we have a good understanding of what physical differences in visual stimuli are perceptually discriminable. A key remaining challenge for visual science is to develop models and psychophysical methods that allow us to evaluate how the visual system estimates visual appearance. Using traditional methods, for example, it is easy to determine how large a change in the parameters describing a surface is needed to produce a visually discriminable surface. It is less obvious how to evaluate the contributions of these same parameters to perception of visual qualities such as color, gloss or roughness. In this presentation, I'll describe methods for modeling judgments of visual appearance that go beyond simple rating methods and describe how to model them and evaluate the resulting models experimentally. I'll describe three applications. The first concerns how illumination and surface albedo contribute to the rated dissimilarity of illuminated surfaces in three-dimensional scenes. The second concerns modeling of super-threshold differences in image quality using difference scaling, and the third concerns application of additive conjoint measurement to evaluating how observers perceive gloss and meso-scale surface texture (‘bumpiness‘) when both are varied.
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