Perceptual no-reference image quality assessment with meta-learning by graph representation learning and multi-scale feature fusion

No-reference image quality assessment (NR-IQA) aims to predict perceptual quality in alignment with the human visual system (HVS), yet existing methods face challenges in capturing long-range dependencies across distortion types and levels while preserving content fidelity during preprocessing. This paper presents a perceptually-driven NR-IQA framework that integrates meta-learning, graph representation learning, and multi-scale feature fusion to address these limitations. First, a meta-learning paradigm is employed to pre-train a self-calibrated convolutional backbone, which adaptively models spatial and channel-wise dependencies across scales, thereby enhancing the extraction of distortion-aware features while mitigating information loss caused by fixed-input preprocessing. Second, a graph representation learning module is introduced to explicitly encode the hierarchical relationships among distortion types, distortion levels, and image content. Nodes in the graph correspond to distorted images, while edges capture inter-sample similarities; these are jointly optimized via a graph convolutional network under dual supervision from a triplet-based distortion-type discriminator and a probabilistic distortion-level regressor that accounts for content-induced uncertainty. Extensive experiments on four benchmark datasets demonstrate that our method achieves better performance, with average SROCC and PLCC improvements of 3.6–36.6% over hand-crafted feature-based methods and consistent gains over deep learning-based approaches. Ablation studies and visualizations confirm that the proposed components collectively yield a more discriminative and generalizable distortion representation, closely mirroring human perceptual judgments.