%0 Conference Paper %1 lin2022crowdsourced %A Lin, Hanhe %A Men, Hui %A Yan, Yijun %A Ren, Jinchang %A Saupe, Dietmar %B Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX) %D 2022 %I IEEE %K 2022 a05 from:christinawarren sfbtrr161 %P 1--4 %R 10.1109/QoMEX55416.2022.9900904 %T Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study %U https://ieeexplore.ieee.org/document/9900904 %X 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.

@inproceedings{lin2022crowdsourced, 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.}, added-at = {2022-11-25T11:59:01.000+0100}, author = {Lin, Hanhe and Men, Hui and Yan, Yijun and Ren, Jinchang and Saupe, Dietmar}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20b31b5a1ab2082557255acda558da191/sfbtrr161}, booktitle = {Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX)}, doi = {10.1109/QoMEX55416.2022.9900904}, interhash = {804cc34cafb9e69fbc18a2205e85dcc0}, intrahash = {0b31b5a1ab2082557255acda558da191}, keywords = {2022 a05 from:christinawarren sfbtrr161}, month = {9}, pages = {1--4}, publisher = {IEEE}, timestamp = {2023-07-21T10:45:57.000+0200}, title = {Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study}, url = {https://ieeexplore.ieee.org/document/9900904}, year = 2022 }