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A Survey of Non-reference Image Quality Assessment Based on CNN

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DOI: 10.23977/icasit.2019.034


Yao Ma, Fuming Sun, Shijie Hao

Corresponding Author

Yao Ma


Due to the serious shortage of training data, CNN's research on non-reference image quality assessment (NR-IQA) is very constrained. In this paper, the existing neural network research on NR-IQA is summarized, and the methods to solve the problem are divided into three types: image segmentation, pre-training migration and unsupervised sequence learning. Then from these three aspects, a more representative algorithm is selected to test and compare performance on different datasets, and analyze the advantages and problems of the three. In the end, the direction of NR-IQA development is discussed, which provides a comprehensive reference for researchers in this field.


CNN, image segmentation, pre-training migration, unsupervised sequence learning, NR-IQA

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