The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA)
methods typically use feature extraction in upstream backbone networks, which
assumes that all extracted features are relevant. However, we argue 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. To address
this issue, we propose a Quality-Aware Feature Matching IQA metric(QFM-IQM)
that employs contrastive learning to remove harmful features from the upstream
task. Specifically, our approach enhances the semantic noise distinguish
capabilities of neural networks by comparing image pairs with similar semantic
features but varying quality scores and adaptively adjusting the upstream
task's features by introducing disturbance. 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 8 standard NR-IQA datasets, achieving PLCC
values of 0.932 (vs. 0.908 in TID2013) and 0.913 (vs. 0.894 in LIVEC).

使用对比学习方法的质量感知特征匹配图像质量评估度量 (QFM-IQM)，通过对具有相似语义特征但质量得分不同的图像对进行比较，以自适应地调整上游任务的特征，移除有害特征并通过蒸馏方法提高模型的泛化能力，在 8 个标准 NR-IQA 数据集上实现了优越的性能。