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Blindly Evaluate the Quality of Underwater Images via Multi-perceptual Properties

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Digital Multimedia Communications (IFTC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1766))

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Abstract

The quality of underwater images can vary greatly due to the complexity of the underwater environment as well as the limitations of imaging devices. This can have an effect on the practical applications that are used in fields such as scientific research, the modern military, and other fields. As a result, attaining subjective quality assessment to differentiate distinct qualities of underwater photos plays a significant role in guiding subsequent tasks. In this study, an effective reference-free underwater image quality assessment metric is proposed by combining the colorfulness, contrast, sharpness, and high-level semantics cues while taking into account the underwater image degradation effect and human visual perception scheme. Specifically, we employ the low-level perceptual property-based method to characterize the image’s visual quality, and we use deep neural networks to extract the image’s semantic content. SVR is then used to create the quality prediction model by analyzing the relationship between the extracted features and the picture quality. Experiments done on the UWIQA database demonstrate the superiority of the proposed method.

Y. Du and X. Xiao—Contribute equally to this work.

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Acknowledgments

This work is supported by the National Key R &D Program of China (No. 2021YFF0900503), the National Natural Science Foundation of China (No. 62102059 & 62201538), the Fundamental Research Funds for the Central Universities (No. 3132022225), and the Natural Science Foundation of Shandong Province under grant ZR2022QF006.

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Authors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Yan Du, Xianjing Xiao, Runze Hu & Xiu Li

  2. Changchun Institute of Optics, Fine Mechanic and Physics, Chinese Academy of Sciences, Changchun, China

    Jiasong Wang

  3. School of Computer Science and Technology, Ocean University of China, Qingdao, China

    Yutao Liu

  4. College of Artificial Intelligence, Dalian Maritime University, Dalian, China

    Zhaolin Wan

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  1. Yan Du
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Corresponding author

Correspondence to Runze Hu .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Jia Wang

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Du, Y. et al. (2023). Blindly Evaluate the Quality of Underwater Images via Multi-perceptual Properties. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_21

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  • DOI: https://doi.org/10.1007/978-981-99-0856-1_21

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