  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.
