A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
21
43 commits
updated Oct 13, 2026
Image Quality Assessment (IQA) is a critical component of image-related technologies and plays a pivotal role in the advancement of image processing and computer vision. In recent years, the proliferation of novel training frameworks and machine learning models has given rise to a multitude of IQA methodologies. This survey conducts a comprehensive review of nearly 200 IQA-related publications, synthesizing key developments in the field and systematically categorizing existing approaches based on their underlying models, training frameworks, publication timelines, application scenarios, and academic impact. This structured analysis aims to facilitate a swift introduction for newcomers while providing seasoned researchers with a clearer perspective on the current state of the field. Moreover, we offer a critical evaluation of the advantages and limitations of various IQA methods and present our perspectives on future research directions. To complement the survey, this repository compiles both the IQA techniques discussed in the paper and other approaches that could not be included due to space constraints, thereby serving as a valuable resource to support further advancements in IQA research.
图像质量评估（IQA）在图像相关的技术中起着非常重要的作用，对于图像处理和计算机视觉领域的技术发展有着深远的影响。近年来，随着新型训练框架和机器学习模型的出现，许多IQA方法涌现出来。本综述通过调研接近二百篇IQA相关论文，总结了IQA发展中值得关注的工作，并按照不同方法所用的模型、训练框架、发表时间、使用场景和影响力进行了整理，以方便初学者快速入门、资深研究者更好地了解领域发展现状。随后，我们对诸多IQA方法的优缺点进行了分析，对IQA方法的未来发展提出了自己的见解。本repository旨在列出论文中提到的、以及受篇幅限制未提到的经典IQA方法，配合论文阅读，以促进IQA技术的发展。
🚀English Paper link: A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
🚀Chinese Paper link: 在计算机视觉领域中，我们应该如何评估图像质量（万字长文）？
🚀The simplified chinese version: 什么样的图像才是好的图像？近200篇文献总结图像质量检测的最新进展与挑战
📗 General Scene Methods
📗 Specific Scene Methods
Citation counts below were queried from OpenAlex on October 13, 2026. Paper identities were matched automatically by normalized exact title and publication year; these matches have not been individually verified. Counts link to their OpenAlex records. ** ** marks unresolved matches, not zero. Rows without a specific paper retain blank cells. OpenAlex counts use a different database coverage and version grouping from other services; differences from the original numbers must not be interpreted as citation growth. The original February 1, 2025 citation snapshot is preserved for historical reference.
If you find this survey useful, please cite our paper:
@misc{ma2025surveyimagequalityassessment,
title={A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook},
author={Chengqian Ma and Zhengyi Shi and Zhiqiang Lu and Shenghao Xie and Fei Chao and Yao Sui},
year={2025},
eprint={2502.08540},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.08540},
}
If you have any questions or suggestions, please contact us via:
A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
21
43 commits
updated Oct 13, 2026
Image Quality Assessment (IQA) is a critical component of image-related technologies and plays a pivotal role in the advancement of image processing and computer vision. In recent years, the proliferation of novel training frameworks and machine learning models has given rise to a multitude of IQA methodologies. This survey conducts a comprehensive review of nearly 200 IQA-related publications, synthesizing key developments in the field and systematically categorizing existing approaches based on their underlying models, training frameworks, publication timelines, application scenarios, and academic impact. This structured analysis aims to facilitate a swift introduction for newcomers while providing seasoned researchers with a clearer perspective on the current state of the field. Moreover, we offer a critical evaluation of the advantages and limitations of various IQA methods and present our perspectives on future research directions. To complement the survey, this repository compiles both the IQA techniques discussed in the paper and other approaches that could not be included due to space constraints, thereby serving as a valuable resource to support further advancements in IQA research.
图像质量评估（IQA）在图像相关的技术中起着非常重要的作用，对于图像处理和计算机视觉领域的技术发展有着深远的影响。近年来，随着新型训练框架和机器学习模型的出现，许多IQA方法涌现出来。本综述通过调研接近二百篇IQA相关论文，总结了IQA发展中值得关注的工作，并按照不同方法所用的模型、训练框架、发表时间、使用场景和影响力进行了整理，以方便初学者快速入门、资深研究者更好地了解领域发展现状。随后，我们对诸多IQA方法的优缺点进行了分析，对IQA方法的未来发展提出了自己的见解。本repository旨在列出论文中提到的、以及受篇幅限制未提到的经典IQA方法，配合论文阅读，以促进IQA技术的发展。
🚀English Paper link: A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
🚀Chinese Paper link: 在计算机视觉领域中，我们应该如何评估图像质量（万字长文）？
🚀The simplified chinese version: 什么样的图像才是好的图像？近200篇文献总结图像质量检测的最新进展与挑战
📗 General Scene Methods
📗 Specific Scene Methods
Citation counts below were queried from OpenAlex on October 13, 2026. Paper identities were matched automatically by normalized exact title and publication year; these matches have not been individually verified. Counts link to their OpenAlex records. ** ** marks unresolved matches, not zero. Rows without a specific paper retain blank cells. OpenAlex counts use a different database coverage and version grouping from other services; differences from the original numbers must not be interpreted as citation growth. The original February 1, 2025 citation snapshot is preserved for historical reference.
If you find this survey useful, please cite our paper:
@misc{ma2025surveyimagequalityassessment,
title={A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook},
author={Chengqian Ma and Zhengyi Shi and Zhiqiang Lu and Shenghao Xie and Fei Chao and Yao Sui},
year={2025},
eprint={2502.08540},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.08540},
}
If you have any questions or suggestions, please contact us via: