为机器视觉系统构建评估图像质量的新框架，精准衡量退化对模型性能的影响。
Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Models
- 提出端到端的机器图像质量评估范式，聚焦模型表现而非人眼感知。
- 构建包含250万样本的MIQD-2.5M数据库，覆盖75个模型与250种退化类型。
- 新模型RA-MIQA可精确定位空间退化区域，尤其擅长捕捉细微失真和背景干扰。
机器视觉系统在恶劣视觉条件下易性能下降。为此，我们提出一种以机器为中心的图像质量评估（MIQA）框架，量化图像退化对机器视觉系统性能的影响。建立了涵盖端到端评估流程的MIQA范式，并构建了包含250万样本的机器中心图像质量数据库（MIQD-2.5M），该数据集覆盖75个视觉模型、250种退化类型及三个代表性任务，同时在一致性与准确性指标上捕捉不同退化的响应特征。进一步提出一种区域感知的MIQA（RA-MIQA）模型，通过细粒度的空间退化分析评估机器视觉质量。大量实验表明，相比七种基于人类视觉系统（HVS）的IQA指标及五种重训练的经典骨干网络，RA-MIQA在多个维度表现更优，如图像分类任务中一致性与准确性的斯皮尔曼等级相关系数（SRCC）分别提升13.56%和13.37%。研究还揭示，基于人类视觉的指标无法有效预测机器性能，而现有专用MIQA模型在背景退化、精度导向估计和微小失真方面仍存在局限。本工作有助于提升机器视觉系统的可靠性，并为机器中心的图像处理与优化奠定基础。模型与代码已开源：https://github.com/XiaoqiWang/MIQA。
原文摘要 · Abstract (English)
Machine vision systems (MVS) are intrinsically vulnerable to performance degradation under adverse visual conditions. To address this, we propose a machine-centric image quality assessment (MIQA) framework that quantifies the impact of image degradations on MVS performance. We establish an MIQA paradigm encompassing the end-to-end assessment workflow. To support this, we construct a machine-centric image quality database (MIQD-2.5M), comprising 2.5 million samples that capture distinctive degradation responses in both consistency and accuracy metrics, spanning 75 vision models, 250 degradation types, and three representative vision tasks. We further propose a region-aware MIQA (RA-MIQA) model to evaluate MVS visual quality through fine-grained spatial degradation analysis. Extensive experiments benchmark the proposed RA-MIQA against seven human visual system (HVS)-based IQA metrics and five retrained classical backbones. Results demonstrate RA-MIQA's superior performance in multiple dimensions, e.g., achieving SRCC gains of 13.56% on consistency and 13.37% on accuracy for image classification, while also revealing task-specific degradation sensitivities. Critically, HVS-based metrics prove inadequate for MVS quality prediction, while even specialized MIQA models struggle with background degradations, accuracy-oriented estimation, and subtle distortions. This study can advance MVS reliability and establish foundations for machine-centric image processing and optimization. The model and code are available at: https://github.com/XiaoqiWang/MIQA.
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