Due to the attenuation and scattering of light by water, there are many quality defects in raw underwater images such as color casts, decreased visibility, reduced contrast, et al.. Many different underwater image enhancement (UIE) algorithms have been proposed to enhance underwater image quality. However, how to fairly compare the performance among UIE algorithms remains a challenging problem. So far, the lack of comprehensive human subjective user study with large-scale benchmark dataset and reliable objective image quality assessment (IQA) metric makes it difficult to fully understand the true performance of UIE algorithms. We in this paper make efforts in both subjective and objective aspects to fill these gaps. Firstly, we construct a new Subjectively-Annotated UIE benchmark Dataset (SAUD) which simultaneously provides real-world raw underwater images, readily available enhanced results by representative UIE algorithms, and subjective ranking scores of each enhanced result. Secondly, we propose an effective No-reference (NR) Underwater Image Quality metric (NUIQ) to automatically evaluate the visual quality of enhanced underwater images. Experiments on the constructed SAUD dataset demonstrate the superiority of our proposed NUIQ metric, achieving higher consistency with subjective rankings than 22 mainstream NR-IQA metrics. The dataset and source code will be made available at https://github.com/yia-yuese/SAUD-Dataset.

中文翻译：

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水下图像增强质量评估：基准数据集和目标指标


由于水对光的衰减和散射，原始水下图像存在许多质量缺陷，例如色偏、可见度下降、对比度降低等。人们提出了许多不同的水下图像增强（UIE）算法来增强水下图像图像质量。然而，如何公平地比较UIE算法的性能仍然是一个具有挑战性的问题。到目前为止，缺乏具有大规模基准数据集和可靠的客观图像质量评估（IQA）指标的全面人类主观用户研究，使得很难完全理解UIE算法的真实性能。我们在本文中从主观和客观两个方面做出努力来填补这些空白。首先，我们构建了一个新的主观注释 UIE 基准数据集（SAUD），它同时提供真实世界的原始水下图像、代表性 UIE 算法的现成增强结果以及每个增强结果的主观排名分数。其次，我们提出了一种有效的无参考（NR）水下图像质量度量（NUIQ）来自动评估增强的水下图像的视觉质量。在构建的 SAUD 数据集上进行的实验证明了我们提出的 NUIQ 指标的优越性，与 22 个主流 NR-IQA 指标相比，与主观排名具有更高的一致性。数据集和源代码将在 https://github.com/yia-yuese/SAUD-Dataset 上提供。