research-article

UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics

Authors:

Zhenkuan PanAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 4

Article No.: 151, Pages 1 - 24

https://doi.org/10.1145/3578584

Published: 27 February 2023 Publication History

Abstract

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 publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their 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. The dataset is available at https://github.com/Hou-Guojia/UID2021.

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Zhang YChandler DLeszczuk M(2024)Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformerOptics Express10.1364/OE.52395132:14(24018)Online publication date: 17-Jun-2024
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Gadre AMachester ZKumar S(2024)Adapting LoRa Ground Stations for Low-latency Imaging and Inference from LoRa-enabled CubeSatsACM Transactions on Sensor Networks10.1145/3675170Online publication date: 27-Jun-2024
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Index Terms

UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 4

July 2023

263 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3582888

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 27 February 2023

Online AM: 06 January 2023

Accepted: 23 December 2022

Revised: 19 December 2022

Received: 19 April 2022

Published in TOMM Volume 19, Issue 4

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https://doi.org/10.1016/j.optlastec.2024.111464
Zhang YChandler DLeszczuk M(2024)Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformerOptics Express10.1364/OE.52395132:14(24018)Online publication date: 17-Jun-2024
https://doi.org/10.1364/OE.523951
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https://dl.acm.org/doi/10.1145/3675170
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