Full-Reference Image Quality Assessment via Low-Level and High-Level Feature Fusion

Chao WU, Xiaofeng LIAO*, Hong YUE, Xueyong XU, Xuekai WEI, Dingcheng WU, Mingliang ZHOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


We propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image gradient to replace the low-level features in the first two stages of the deep network, but also combine them with the middle-stage features of the deep network to construct the new low-level features. The deep features of shallow layers contain some unwanted noise and further result in a decline in IQA performance. Second, we combine the global features extracted by the self-attention-based model with the semantic features extracted by the convolutional neural network to form the high-level features. Instead of directly using the self-attention-based model trained on the classification task, we first train a no-reference (NR) IQA regression model on a larger dataset and then use the global features of this NR-IQA model. The self-attention-based model can capture the internal connections of the image and is more effective in extracting global information due to its larger perceptual field. In the final pooling stage, we combine the average pooling and the standard deviation pooling to obtain the dispersion and concentration of the similarity maps for a more comprehensive description of quality. Experiments show that our FR-IQA method is able to obtain competitive results on three standard IQA datasets.

Original languageEnglish
Article number2354016
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number11
Publication statusPublished - 15 Sept 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 World Scientific Publishing Company.


  • Full-reference (FR)
  • high-level feature
  • image quality assessment (IQA)
  • low-level feature


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