Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.08540 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913688366088192 |
|---|---|
| author | Ma, Chengqian Shi, Zhengyi Lu, Zhiqiang Xie, Shenghao Chao, Fei Sui, Yao |
| author_facet | Ma, Chengqian Shi, Zhengyi Lu, Zhiqiang Xie, Shenghao Chao, Fei Sui, Yao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook Ma, Chengqian Shi, Zhengyi Lu, Zhiqiang Xie, Shenghao Chao, Fei Sui, Yao Computer Vision and Pattern Recognition 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. |
| title | A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.08540 |