A model of visual adaptation for realistic image synthesis

TLDR

The authors develop a computational model of visual adaptation for realistic image synthesis grounded in psychophysical experiments. The model simulates changes in threshold visibility, color appearance, visual acuity, and sensitivity over time, and applies these dynamics to render global illumination simulations across daylight to starlight intensities. The resulting images more accurately reflect human perception across a wide range of illumination levels, enabling prediction of visibility and appearance and providing perceptually based error metrics to limit global illumination precision. © CR.

Abstract

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. CR