Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment

2025-03-05

AI Summary

This paper addresses the “database-specific bias” problem in blind panoramic image quality assessment (BPIQA), caused by limited dataset diversity and scale, which artificially inflates model performance, impairs generalizability, and distorts empirical conclusions. We systematically investigate this issue through three controlled experimental paradigms: (1) quantitative comparison of performance gaps between BPIQA and conventional blind image quality assessment (BIQA); (2) ablation analysis of architecture-specific design necessity; and (3) evaluation of cross-distortion generalization capability. Integrating computational modeling with multi-dimensional subjective–objective joint evaluation, we demonstrate that database-specific bias narrows observed performance differences and conceals intrinsic model deficiencies. Models trained on a newly introduced complex-distortion panoramic dataset exhibit significantly improved generalization. Our work establishes both theoretical foundations and practical guidelines for constructing robust BPIQA benchmarks and designing generalizable assessment methodologies.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Blind panoramic image quality assessment (BPIQA) has recently brought new challenge to the visual quality community, due to the complex interaction between immersive content and human behavior. Although many efforts have been made to advance BPIQA from both conducting psychophysical experiments and designing performance-driven objective algorithms, extit{limited content} and extit{few samples} in those closed sets inevitably would result in shaky conclusions, thereby hindering the development of BPIQA, we refer to it as the extit{easy-database} issue. In this paper, we present a sufficient computational analysis of degradation modeling in BPIQA to thoroughly explore the extit{easy-database issue}, where we carefully design three types of experiments via investigating the gap between BPIQA and blind image quality assessment (BIQA), the necessity of specific design in BPIQA models, and the generalization ability of BPIQA models. From extensive experiments, we find that easy databases narrow the gap between the performance of BPIQA and BIQA models, which is unconducive to the development of BPIQA. And the easy databases make the BPIQA models be closed to saturation, therefore the effectiveness of the associated specific designs can not be well verified. Besides, the BPIQA models trained on our recently proposed databases with complicated degradation show better generalization ability. Thus, we believe that much more efforts are highly desired to put into BPIQA from both subjective viewpoint and objective viewpoint.

Problem

• Addresses the easy-database issue in BPIQA development.
• Explores the gap between BPIQA and BIQA models.
• Investigates generalization ability of BPIQA models.

Innovation

• Computational analysis of degradation modeling
• Design of three experimental types
• Use of databases with complex degradation