Measuring and predicting visual fidelity

2025-07-15

AI Summary

Accurately measuring visual fidelity and predicting human perception remains challenging, particularly in distinguishing perceptual differences across object categories and simplification types. Method: We propose a systematic evaluation framework integrating polygon mesh simplification (using two distinct algorithms) with psychophysical experiments—namely naming time, subjective rating, and pairwise preference judgments—to rigorously compare perceptual responses to animals versus man-made objects under controlled simplification levels. Contribution/Results: This work introduces the first three-factor controlled analysis jointly varying simplification type, degree, and object category. We find naming time and preference tasks exhibit higher sensitivity to fidelity degradation than subjective ratings; conversely, state-of-the-art image- and mesh-based automatic metrics reliably predict only subjective ratings, failing to model higher-order perceptual decisions. These findings expose fundamental cognitive limitations of prevailing fidelity metrics and provide empirical grounding and a methodological paradigm for developing human-centered graphics fidelity assessment frameworks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Low Level & Physics-based Vision
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Measuring visual fidelity of polygonal models
• Comparing experimental techniques for fidelity assessment
• Evaluating automatic prediction of visual fidelity measures

Innovation

• Uses polygonal models with simplification algorithms
• Measures fidelity via naming, ratings, preferences
• Predicts fidelity with image and model techniques