🤖 AI Summary
Existing no-reference image quality assessment (NR-IQA) methods rely on semantic backbone networks for feature extraction; however, their outputs often contain semantically irrelevant—or even detrimental—noise, leading to substantial quality score discrepancies between image pairs with small feature distances. To address this, we propose Quality-aware Feature Matching IQM (QFM-IQM), the first NR-IQA framework to introduce adversarial semantic noise matching: it identifies such noise by constructing image pairs that are quality-similar but semantically dissimilar. QFM-IQM explicitly models feature sensitivity to semantic noise and adaptively suppresses redundant or harmful channels via channel-wise gating. Additionally, knowledge distillation is integrated to enhance generalization across diverse distortion types. Evaluated on eight standard IQA benchmarks, QFM-IQM consistently outperforms state-of-the-art methods, achieving significant improvements in prediction accuracy, cross-dataset generalization, and robustness to semantic confounders.
📝 Abstract
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain a significant amount of quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
