Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers to assign an absolute psychophysical scale to the quality of underwater images. In this paper, we propose a pairwise subjective comparison procedure for underwater images quality ranking inspired by the intuitive suppression and competence mechanisms in visual perception. In the proposed method, we construct a preselection based initial image quality dataset by full pairwise comparison, which also enables online adaptive new image updating. The proposed method is not constrained by the lack of reference images, and is reliable and sensitive to images with discriminable distortion level and various image contents. The proposed pairwise comparison further allows an uncertain choice, which does not require a reinforce human opinion. To the best of our knowledge, this is the first implementation for underwater image subjective quality ranking, and a new approach to the image quality ranking for different image contents with unknown distortion level. We demonstrate that the obtained subjective image ranking correlates well with the human perception of quality difference among the underwater images than that of the single stimuli image quality assessment with finite labor burden. Moreover, our proposed method accurately characterize the gradual degradation in the underwater image sequence taken in controlled conditions. The proposed progressive learning ranking is also an alternative way to realize adaptive extension of the existing image quality databases.

由于水的物理化学特性、悬浮的有机物质和水中的漂浮颗粒，水下图像包含扭曲的相互作用混合物。与传统自然图像质量数据库中的图像不同，水下图像通常难以通过参考图像和具有梯度失真的图像集获取。因此，观看者更难以为水下图像的质量分配绝对的心理物理尺度。在本文中，我们受到视觉感知中的直观抑制和能力机制的启发，提出了一种用于水下图像质量排名的成对主观比较程序。在所提出的方法中，我们通过完全成对比较构建了一个基于预选的初始图像质量数据集，这也使得在线自适应新图像更新成为可能。所提出的方法不受缺乏参考图像的限制，并且对具有可辨别失真水平和各种图像内容的图像可靠且敏感。提议的成对比较进一步允许不确定的选择，这不需要强化人类意见。据我们所知，这是水下图像主观质量排名的第一个实现，也是一种新的图像质量排名方法，用于对失真水平未知的不同图像内容进行图像质量排名。我们证明，与具有有限劳动负担的单一刺激图像质量评估相比，获得的主观图像排名与人类对水下图像质量差异的感知相关性很好。而且，我们提出的方法准确地表征了在受控条件下拍摄的水下图像序列的逐渐退化。提出的渐进学习排序也是实现现有图像质量数据库自适应扩展的另一种方式。