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Crowdsourced quality assessment of enhanced underwater images: a pilot study.

Lin, Hanhe; Men, Hui; Yan, Yijun; Ren, Jinchang; Saupe, Dietmar

Authors

Hanhe Lin

Hui Men

Yijun Yan

Dietmar Saupe



Abstract

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods.

Citation

LIN, H., MEN, H., YAN, Y., REN, J. and SAUPE, D. 2022. Crowdsourced quality assessment of enhanced underwater images: a pilot study. In Proceedings of 14th International conference on quality of multimedia experience 2022 (QoMEX 2022), 5-7 September 2022, Lippstadt, Germany. Piscataway: IEEE [online], article 9900904. Available from: https://doi.org/10.1109/QoMEX55416.2022.9900904

Conference Name 2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
Conference Location Lippstadt, Germany
Start Date Sep 5, 2022
End Date Sep 7, 2022
Acceptance Date May 31, 2022
Online Publication Date Oct 4, 2022
Publication Date Dec 31, 2022
Deposit Date Oct 6, 2022
Publicly Available Date Oct 6, 2022
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series ISSN 2472-7814
Book Title Proceedings of 14th International conference on quality of multimedia experience 2022 (QoMEX 2022), 5-7 September 2022, Lippstadt, Germany
DOI https://doi.org/10.1109/QoMEX55416.2022.9900904
Keywords Underwater image; Image quality assessment; Image enhancement; Crowdsourcing
Public URL https://rgu-repository.worktribe.com/output/1769373

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