# A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook

arXiv: https://arxiv.org/abs/2502.08540 · HTML: https://mlsanity.com/paper/2502.08540/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality](https://mlsanity.com/paper/2505.19696/) (2025)
- [Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings](https://mlsanity.com/paper/2507.22802/) (2025)
- [RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360degImage Quality Assessment](https://mlsanity.com/paper/2603.14297/) (2026)
- [A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation](https://mlsanity.com/paper/2605.15906/) (2026)
- [Architecture-Agnostic Feature Synergy for Universal Defense Against Heterogeneous Generative Threats](https://mlsanity.com/paper/2603.14860/) (2026)
- [Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling](https://mlsanity.com/paper/2604.01898/) (2026)
- [Assessing Quality of Experience in Natural Language Generation of German Text](https://mlsanity.com/paper/2608.18888/) (2026)

## Abstract (arXiv)

Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of image processing and computer vision. Recently, IQA has witnessed a notable surge in innovative research efforts, driven by the emergence of novel architectural paradigms and sophisticated computational techniques. This survey delivers an extensive analysis of contemporary IQA methodologies, organized according to their application scenarios, serving as a beneficial reference for both beginners and experienced researchers. We analyze the advantages and limitations of current approaches and suggest potential future research pathways. The survey encompasses both general and specific IQA methodologies, including conventional statistical measures, machine learning techniques, and cutting-edge deep learning models such as convolutional neural networks (CNNs) and Transformer models. The analysis within this survey highlights the necessity for distortion-specific IQA methods tailored to various application scenarios, emphasizing the significance of practicality, interpretability, and ease of implementation in future developments.