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PhD Thesis Perceptually-motivated Non-Photorealistic Graphics

Author(s): Holger Winnemöller.
PhD Thesis: Northwestern University, Evanston, Illinois, U.S.A., 2006.
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At a high level, computer graphics deals with conveying information to an observer by visual means. Generating realistic images for this task requires considerable time and computing resources. Human vision faces the opposite challenge: to distill knowledge of the world from a massive influx of visual information. It is reasonable to assume that synthetic images based on human perception and tailored for a given task can (1) decrease image synthesis costs by obviating a physically realistic lighting simulation, and (2) increase human task performance by omitting superfluous detail and enhancing visually important features. This dissertation argues that the connection between non-realistic depiction and human perception is a valuable tool to improve the effectiveness of computer-generated images to support visual communication tasks, and conversely, to learn more about human perception of such images. Artists have capitalized on non-realistic imagery to great effect, and have become masters of conveying complex and even abstract messages by visual means. The relatively new field of non-photorealistic computer graphics attempts to harness artists’ implicit expertise by imitating their visual styles, media, and tools, but only few works move beyond such simulations to verify the effectiveness of generated images with perceptual studies, or to investigate which stylistic elements are effective for a given visual communication task. This dissertation demonstrates the mutual beneficence of non-realistic computer graphics and perception with two rendering frameworks and accompanying psychophysical studies: (1) Inspired by low-level human perception, a novel image-based abstraction framework simplifies and enhances images to make them easier to understand and remember. (2) A non-realistic rendering framework generates isolated visual shape cues to study human perception of fast-moving objects. The first framework leverages perception to increase effectiveness of (non-realistic) images for visually-driven tasks, while the second framework uses non-realistic images to learn about task-specific perception, thus closing the loop. As instances of the bi-directional connections between perception and non-realistic imagery, the frameworks illustrate numerous benefits including effectiveness (e.g. better recognition of abstractions versus photographs), high performance (e.g. real-time image abstraction), and relevance (e.g. shape perception in non-impoverished conditions).

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