论文标题
UID2021：用于评估无参考质量评估指标的水下图像数据集
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics
论文作者
论文摘要
在水下视觉感知和图像/视频处理中，实现水下图像的主观和客观质量评估具有很高的意义。但是，水下图像质量评估（UIQA）的开发因缺乏全面的人类主观用户研究而受到限制，并且具有公开可用的数据集和可靠的目标UIQA指标。为了解决此问题，我们建立了一个称为UID2021的大规模水下图像数据集，用于评估NO-REFERIODE UIQA指标。 The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e. bluish scene, bluish-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions generated by employing fifteen state-of-the-art underwater image enhancement and restoration algorithms.使用对52位观察者的对比较分选方法也获得了UID2021的平均意见分数（MOS）。在我们的构造数据集中测试了空气内NR-IQA和水下特异性算法，以公平地比较性能并分析其优势和劣势。我们提出的UID2021数据集使数据集可以全面评估NR UIQA算法，并为对UIQA的进一步研究铺平了道路。我们的UID2021将免费下载并用于研究目的：https：//github.com/hou-guojia/uid2021。
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of comprehensive human subjective user study with publicly available dataset and reliable objective UIQA metric. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e. bluish scene, bluish-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions generated by employing fifteen state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores (MOS) for UID2021 are also obtained by using the pair comparison sorting method with 52 observers. Both in-air NR-IQA and underwater-specific algorithms are tested on our constructed dataset to fairly compare the performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. Our UID2021 will be a free download and utilized for research purposes at: https://github.com/Hou-Guojia/UID2021.
