Adaptive Feature Selection for No-Reference Image Quality Assessment by
  Mitigating Semantic Noise Sensitivity
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.
Xudong Li Timin Gao Runze Hu Yan Zhang Shengchuan Zhang Xiawu Zheng Jingyuan Zheng Yunhang Shen Ke Li Yutao Liu Pingyang Dai Rongrong Ji