系统梳理图像质量评估方法，为研究者提供实用参考。
A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
- 按应用场景分类分析传统与深度学习评估方法
- 指出当前方法在实用性与可解释性上的不足
- 适合图像处理与计算机视觉方向的研究者参考
图像质量评估（IQA）是图像技术中的关键挑战，深刻影响图像处理与计算机视觉的发展。近年来，随着新型架构和先进计算技术的出现，IQA领域研究热度显著上升。本综述对当前主流IQA方法进行系统分析，按应用情境组织，为初学者与资深研究者提供有益参考。我们评估了现有方法的优缺点，并提出未来研究方向。内容涵盖通用与特定场景的IQA方法，包括传统统计指标、机器学习技术以及前沿的卷积神经网络（CNN）和Transformer模型。分析强调需发展针对不同失真类型的专用评估方法，未来研究应重视实用性、可解释性与实现便捷性。
原文摘要 · Abstract (English)
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.
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