Measuring and Predicting Visual Fidelity
Benjamin Watson, Alinda Friedman, Aaron McGaffey
Proceedings of SIGGRAPH 2001, August 2001, pp. 213--220.
Abstract: This paper is a study of techniques for measuring and predicting visual fidelity. As visual stimuli we use polygonal models, and vary their fidelity with two different model simplification algorithms. We also group the stimuli into two object types: animals and man made artifacts. We examine three different experimental techniques for measuring these fidelity changes: naming times, ratings, and preferences. All the measures were sensitive to the type of simplification and level of simplification. However, the measures differed from one another in their response to object type. We also examine several automatic techniques for predicting these experimental measures, including techniques based on images and on the models themselves. Automatic measures of fidelity were successful at predicting experimental ratings, less successful at predicting preferences, and largely failures at predicting naming times. We conclude with suggestions for use and improvement of the experimental and automatic measures of visual fidelity.
Keyword(s): visual fidelity, model simplification, image quality, naming time, human vision, perception
BibTeX format:
@inproceedings{Watson:2001:MAP,
  author = {Benjamin Watson and Alinda Friedman and Aaron McGaffey},
  title = {Measuring and Predicting Visual Fidelity},
  booktitle = {Proceedings of SIGGRAPH 2001},
  pages = {213--220},
  month = aug,
  year = {2001},
}
Search for more articles by Benjamin Watson.
Search for more articles by Alinda Friedman.
Search for more articles by Aaron McGaffey.

Return to the search page.


graphbib: Powered by "bibsql" and "SQLite3."