No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding

Yong CHEN; Kaixin ZHU; Hao FANG; Huanlin LIU

doi:10.11999/JEIT190721

Volume 42 Issue 10

Oct. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2533-2540

Yong CHEN, Kaixin ZHU, Hao FANG, Huanlin LIU. No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2533-2540. doi: 10.11999/JEIT190721

Citation:

Yong CHEN, Kaixin ZHU, Hao FANG, Huanlin LIU. No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2533-2540. doi: 10.11999/JEIT190721

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No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding

doi: 10.11999/JEIT190721

1.
Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (51977021)

Received Date: 2019-09-17
Rev Recd Date: 2020-02-16

Available Online: 2020-03-09

Publish Date: 2020-10-13

Abstract

Abstract

Considering the problem that it is difficult to accurately and effectively extract the quality features of mixed distortion image, an image quality assessment method based on spatial distribution analysis is proposed. Firstly, the brightness coefficients of the image are normalized, and the image is divided into blocks. While the Convolutional Neural Network (CNN) is used for end-to-end depth learning, the multi-level stacking of convolution cores is applied to acquire image quality perception features. The feature is mapped to the mass fraction of the image block through the full connection layer, then the quality pool is obtained by aggregating the quality of the block. Through the analysis of the spatial distribution of local quality in the quality pool, the features that can represent its spatial distribution are extracted, and then the mapping model from local quality to overall quality is established by the neural network to aggregate the local quality of the image. Finally, the effectiveness of the algorithm is verified by the performance tests in MLIVE, MDID2013 and MDID2016 mixed distortion image databases.
- Image quality assessment,
- No-reference,
- Convolutional Neural Network (CNN)

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References(20)

References

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