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In this paper we introduce a new light reflection model for image synthesis based on experimental studies of surface gloss perception. To develop the model, we've conducted two experiments that explore the relationships between the physical parameters used to describe the reflectance properties of glossy surfaces and the perceptual dimensions of glossy appearance. In the first experiment we use multidimensional scaling techniques to reveal the dimensionality of gloss perception for simulated painted surfaces. In the second experiment we use magnitude estimation methods to place metrics on these dimensions that relate changes in apparent gloss to variations in surface reflectance properties. We use the results of these experiments to rewrite the parameters of a physically-based light reflection model in perceptual terms. The result is a new psychophysically-based light reflection model where the dimensions of the model are perceptually meaningful, and variations along the dimensions are perceptually uniform. We demonstrate that the model can facilitate describing surface gloss in graphics rendering applications. This work represents a new methodology for developing light reflection models for image synthesis.

In this paper we develop a computational model of visual adaptation for realistic image synthesis based on psychophysical experiments. The model captures the changes in threshold visibility, color appearance, visual acuity, and sensitivity over time that are caused by the visual system's adaptation mechanisms. We use the model to display the results of global illumination simulations illuminated at intensities ranging from daylight down to starlight. The resulting images better capture the visual characteristics of scenes viewed over a wide range of illumination levels. Because the model is based on psychophysical data it can be used to predict the visibility and appearance of scene features. This allows the model to be used as the basis of perceptually-based error metrics for limiting the precision of global illumination computations.

We introduce a new class of primitive functions with non-linear parameters for representing light reflectance functions. The functions are reciprocal, energy-conserving and expressive. They can capture important phenomena such as off-specular reflection, increasing reflectance and retro-reflection. We demonstrate this by fitting sums of primitive functions to a physically-based model and to actual measurements. The resulting representation is simple, compact and uniform. It can be applied efficiently in analytical and Monte Carlo computations.

In this paper we present a multiscale color appearance model which simulates luminance, pattern and color processing of the human visual system to accurately predict the color appearance attributes of spectral stimuli in complex surroundings under a wide range of illumination and viewing conditions.