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Proceedings Paper

Dimensionality of visual complexity in computer graphics scenes
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Paper Abstract

How do human observers perceive visual complexity in images? This problem is especially relevant for computer graphics, where a better understanding of visual complexity can aid in the development of more advanced rendering algorithms. In this paper, we describe a study of the dimensionality of visual complexity in computer graphics scenes. We conducted an experiment where subjects judged the relative complexity of 21 high-resolution scenes, rendered with photorealistic methods. Scenes were gathered from web archives and varied in theme, number and layout of objects, material properties, and lighting. We analyzed the subject responses using multidimensional scaling of pooled subject responses. This analysis embedded the stimulus images in a two-dimensional space, with axes that roughly corresponded to "numerosity" and "material / lighting complexity". In a follow-up analysis, we derived a one-dimensional complexity ordering of the stimulus images. We compared this ordering with several computable complexity metrics, such as scene polygon count and JPEG compression size, and did not find them to be very correlated. Understanding the differences between these measures can lead to the design of more efficient rendering algorithms in computer graphics.

Paper Details

Date Published: 13 February 2008
PDF: 10 pages
Proc. SPIE 6806, Human Vision and Electronic Imaging XIII, 68060E (13 February 2008); doi: 10.1117/12.767029
Show Author Affiliations
Ganesh Ramanarayanan, Cornell Univ. (United States)
Kavita Bala, Cornell Univ. (United States)
James A. Ferwerda, Rochester Institute of Technology (United States)
Bruce Walter, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 6806:
Human Vision and Electronic Imaging XIII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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