Measuring and predicting visual fidelity

TL;DR

This study evaluates various methods for measuring and predicting visual fidelity in polygonal models, analyzing human responses and automatic techniques across different object types and simplification levels.

Contribution

It introduces a comprehensive comparison of experimental and automatic measures for visual fidelity, highlighting their strengths and limitations across different object types.

Findings

Measures are sensitive to simplification level and type.

Automatic measures predict ratings well but not naming times.

Different measures respond differently to object types.

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.