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Blind Image Quality Assessment Method Based on DeepSA-Net

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

Blind image quality assessment refers to the accurate prediction of the visual quality of any input image without a reference image. With the rapid growth of the number of images and increasing requirements for image quality, how to assess image quality has become an urgent problem. Complex images are difficult to consider professionally from a single perspective. A blind image quality assessment algorithm based on a deep semantic adaptation network (DeepSA-Net) is proposed. Based on the end-to-end deep learning model, the semantic pre-trained models and multi-resolution adaptive module are added. The adaptive factor \(\alpha \) is proposed to better capture global and local quality information and fuse multi-resolution features to improve the convergence ability and speed of the network. Finally, the quality assessment results of images are obtained by regression. The experiment used the Spearman correlation coefficient and Pearson correlation coefficient as assessment indicators. The results showed that DeepSA-Net outperformed most current methods in real distortion scene databases and had excellent assessment ability in synthetic distortion databases. In addition, ablation study and different distortion studies were designed to fully validate the effectiveness and feasibility of the algorithm.

Supported by organization China Mobile (Suzhou) Software Technology Company Limited.

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Authors and Affiliations

  1. China Mobile (Suzhou) Software Technology Company Limited, Suzhou, 215163, China

    Haobing Tian, Jingyi Li, Qi Yan, Yang Zhong, Lang Zhang & Pengju Jiao

Authors
  1. Haobing Tian
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  2. Jingyi Li
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  3. Qi Yan
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  4. Yang Zhong
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  5. Lang Zhang
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  6. Pengju Jiao
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Corresponding author

Correspondence to Haobing Tian .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Tian, H., Li, J., Yan, Q., Zhong, Y., Zhang, L., Jiao, P. (2024). Blind Image Quality Assessment Method Based on DeepSA-Net. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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