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