# Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment

arXiv: https://arxiv.org/abs/2503.03255 · HTML: https://mlsanity.com/paper/2503.03255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 77 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020)
- [Benchmarking the Robustness of Semantic Segmentation Models](https://mlsanity.com/paper/1908.05005/) (2019)
- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://mlsanity.com/paper/2008.03889/) (2020)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [How Far are We from Solving Pedestrian Detection?](https://mlsanity.com/paper/1602.01237/) (2016)
- [Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment](https://mlsanity.com/paper/2003.03849/) (2020)
- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [Troubleshooting Blind Image Quality Models in the Wild](https://mlsanity.com/paper/2105.06747/) (2021)
- [I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively](https://mlsanity.com/paper/2002.10648/) (2020)
- [Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images](https://mlsanity.com/paper/2103.06116/) (2021)
- [Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition](https://mlsanity.com/paper/2103.00259/) (2021)
- [Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping](https://mlsanity.com/paper/2109.00180/) (2021)
- [ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers](https://mlsanity.com/paper/2303.06907/) (2023)
- [UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics](https://mlsanity.com/paper/2204.08813/) (2022)
- [Perceptual Quality Assessment of 360^circ Images Based on Generative Scanpath Representation](https://mlsanity.com/paper/2309.03472/) (2023)
- [Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11511/) (2025)
- [Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model](https://mlsanity.com/paper/2502.15271/) (2025)

## Built on by (most cited first, 1 of 1)

- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach](https://mlsanity.com/paper/2604.23953/) (2026)

## Abstract (arXiv)

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, textitlimited content and textitfew samples in those closed sets inevitably would result in shaky conclusions, thereby hindering the development of BPIQA, we refer to it as the textiteasy-database issue. In this paper, we present a sufficient computational analysis of degradation modeling in BPIQA to thoroughly explore the textiteasy-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.