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Dual Path DNN Based Heterogenous Reference Image Quality Assessment via Decoupling the Quality Difference and Content Difference

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13458))

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Abstract

Blind image quality assessment (BIQA) is a fundamental yet challenging task in the field of low-level computer vision. The difficulty is particularly due to the limited information, for which the corresponding reference for comparison is typically absent. In order to improve the accuracy and generalization ability of BIQA metrics, our work proposes a dual-path deep neural network (DNN) based heterogenous reference BIQA framework in which an arbitrarily selected pristine image is employed to provide important prior quality information for the IQA framework. The proposed IQA metric is still ‘blind’ since the corresponding reference image is unseen, but our metric could obtain more prior quality information than previous work with the help of heterogenous reference. Experimental results indicate that our proposed BIQA framework is as competitive as state-of-the-art BIQA models.

Supported by The Public Welfare Technology Application Research Project of Zhengjiang Province, China (LGF21F010001).

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Author information

Authors and Affiliations

  1. Key Lab of Film and TV Media Technology of Zhejiang Province, Communication University of Zhejiang, Hangzhou, 310042, China

    Xiaoyu Ma & Dingguo Yu

  2. Institute of Intelligent Media, Communication University of Zhejiang, Hangzhou, 310042, China

    Yaqi Wang, Suiyu Zhang & Dingguo Yu

Authors
  1. Xiaoyu Ma
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  2. Yaqi Wang
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  3. Suiyu Zhang
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  4. Dingguo Yu
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Corresponding author

Correspondence to Dingguo Yu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Ma, X., Wang, Y., Zhang, S., Yu, D. (2022). Dual Path DNN Based Heterogenous Reference Image Quality Assessment via Decoupling the Quality Difference and Content Difference. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_20

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  • DOI: https://doi.org/10.1007/978-3-031-13841-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13840-9

  • Online ISBN: 978-3-031-13841-6

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