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

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

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022
• 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)
• Peer Reviewed: Peer Reviewed
• 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