No-reference image quality assessment by using convolutional neural networks via object detection

Jingchao CAO, Wenhui WU, Ran WANG, Sam KWONG

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

2 Citations (Scopus)


Convolutional neural networks (CNNs) have been widely applied in the image quality assessment (IQA) field, but the size of the IQA databases severely limits the performance of the CNN-based IQA models. The most popular method to extend the size of database in previous works is to resize the images into patches. However, human visual system (HVS) can only perceive the qualities of objects in an image rather than the qualities of patches in it. Motivated by this fact, we propose a CNN-based algorithm for no-reference image quality assessment (NR-IQA) based on object detection. The network has three parts: an object detector, an image quality prediction network, and a self-correction measurement (SCM) network. First, we detect objects from input image by the object detector. Second, a ResNet-18 network is applied to extract features of the input image and a fully connected (FC) layer is followed to estimate image quality. Third, another ResNet-18 network is used to extract features of both the images and its detected objects, where the features of the objects are concatenated to the features of the image. Then, another FC layer is followed to compute the correction value of each object. Finally, the predicted image quality is amended by the SCM values. Experimental results demonstrate that the proposed NR-IQA model has state-of-the-art performance. In addition, cross-database evaluation indicates the great generalization ability of the proposed model.
Original languageEnglish
Pages (from-to)3543–3554
Number of pages12
JournalInternational Journal of Machine Learning and Cybernetics
Issue number11
Early online date23 Jul 2022
Publication statusPublished - Nov 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), in part by the General Research Fund-Research Grants Council (GRF-RGC) under Grant 9042816 (CityU 11209819) and Grant 9042958 (CityU 11203820), in part by the National Natural Science Foundation of China under Grants 62176160, 62006158 and 61732011, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515010791, in part by the Natural Science Foundation of Shenzhen (University Stability Support Program) under Grants 20200804193857002 and 20200810150732001, and in part by the Interdisciplinary Innovation Team of Shenzhen University.


  • Correction value
  • Deep learning
  • Feature extraction
  • No reference image quality assessment
  • Object detection


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