Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Models

• URL: http://arxiv.org/abs/2508.19850v1
• Date: Wed, 27 Aug 2025 13:07:24 GMT
• Title: Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Models
• Authors: Xiaoqi Wang, Yun Zhang, Weisi Lin,
• Abstract summary: Machine vision systems (MVS) are intrinsically vulnerable to performance degradation under adverse visual conditions.<n>We propose a machine-centric image quality assessment (MIQA) framework that quantifies the impact of image degradations on MVS performance.
• Score: 60.356842878501254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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