Visual Perception in Realistic Image Synthesis

Abstract

Realism is often a primary goal in computer graphics imagery, and we strive to create images that are perceptually indistinguishable from an actual scene. Rendering systems can now closely approximate the physical distribution of light in an environment. However, physical accuracy does not guarantee that the displayed images will have authentic visual appearance. In recent years the emphasis in realistic image synthesis has begun to shift from the simulation of light in an environment to images that look as real as the physical environment they portray. In other words the computer image should be not only physically correct but also perceptually equivalent to the scene it represents. This implies aspects of the Human Visual System (HVS) must be considered if realism is required. Visual perception is employed in many different guises in graphics to achieve authenticity. Certain aspects of the visual system must be considered to identify the perceptual effects that a realistic rendering system must achieve in order to reproduce effectively a similar visual response to a real scene. This paper outlines the manner in which knowledge about visual perception is increasingly appearing in state-of-the-art realistic image synthesis. After a brief overview of the HVS, this paper is organized into four sections, each exploring the use of perception in realistic image synthesis, each with slightly different emphasis and application. First, Tone Mapping Operators, which attempt to map the vast range of computed radiance values to the limited range of display values, are discussed. Then perception based image quality metrics, which aim to compare images on a perceptual rather than physical basis, are presented. These metrics can be used to evaluate, validate and compare imagery. Thirdly, perception driven rendering algorithms are described. These algorithms focus on embedding models of the HVS directly into global illumination computations in order to improve their efficiency. Finally, techniques for comparing computer graphics imagery against the real world scenes they represent are discussed.