DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network

Deep neural networks have achieved great performance on blind Image Quality Assessment (IQA), but it is still challenging for using one network to accurately predict the quality of images with different distortions. In this paper, a Distortion-Aware Convolutional Neural Network (DACNN) is proposed f...

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Bibliographic Details
Published inIEEE transactions on circuits and systems for video technology Vol. 32; no. 11; pp. 7518 - 7531
Main Authors Pan, Zhaoqing, Zhang, Hao, Lei, Jianjun, Fang, Yuming, Shao, Xiao, Ling, Nam, Kwong, Sam
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Blind image quality assessment
Distortion
distortion-aware network
Feature extraction
Fuses
fusion network
Image quality
Knowledge engineering
Machine learning
Modules
Neural networks
Quality assessment
Semantics
Siamese network
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Summary:Deep neural networks have achieved great performance on blind Image Quality Assessment (IQA), but it is still challenging for using one network to accurately predict the quality of images with different distortions. In this paper, a Distortion-Aware Convolutional Neural Network (DACNN) is proposed for blind IQA, which works effectively for not only synthetically distorted images but also authentically distorted images. The proposed DACNN consists of a distortion aware module, a distortion fusion module, and a quality prediction module. In the distortion aware module, a Siamese network-based pretraining strategy is proposed to design a synthetic distortion-aware network for full learning the synthetic distortions, and an authentic distortion-aware network is used for extracting the authentic distortions. To efficiently fuse the learned distortion features, and make the network pay more attention to the essential features, a weight-adaptive fusion network is proposed to adaptively adjust the weight of each distortion. Finally, the quality prediction module is adopted to map the fused features to a quality score. Extensive experiments on four authentic IQA databases and four synthetic IQA databases have proved the effectiveness of the proposed DACNN.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2022.3188991