research-article

Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective Method

Authors:

Hongwei YingAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 3

Article No.: 71, Pages 1 - 23

https://doi.org/10.1145/3624983

Published: 23 October 2023 Publication History

Abstract

The complicated underwater environment and lighting conditions lead to severe influence on the quality of underwater imaging, which tends to impair underwater exploration and research. To effectively evaluate the quality of underwater images, an underwater image quality assessment dataset is constructed from synthetic to real-world, and then a new objective underwater image assessment method based on the characteristics of the underwater imaging is proposed (UICQA). Specifically, to address the lack of a publicly available datasets and more accurately quantify the quality of underwater images, a subjective underwater image quality assessment dataset from synthetic to real-world underwater images, named USRD, is constructed. Considering that the transmission map can effectively reflect the characteristics of the underwater imaging, statistical features are effectively extracted from the transmission map for distinguishing underwater images of different quality. Further, considering that the transmission map negatively correlates with scene depth, a local-to-global transmission map weighted contrast feature is constructed. Additionally, the color features of human perception and texture features based on fractal dimensions are proposed. Finally, the experimental results show that the proposed UICQA method exhibits the highest correlation with ground truth scores compared to state-of-the-art UIQA methods.

References

[1]

Yang Wang, Yang Cao, Jing Zhang, Feng Wu, and Zheng-Jun Zha. 2021. Leveraging deep statistics for underwater image enhancement. ACM Trans. Multim. Comput., Commun. Applic. 17, 3s (2021), 20.

[2]

Prasen Sharma, Ira Bisht, and Arijit Sur. 2023. Wavelength-based attributed deep neural network for underwater image restoration. ACM Trans. Multimedia Comput. Commun. Appl. 19, 1, Article 2 (January 2023), 23 pages.

Digital Library

[3]

Chenggang Yan, Tong Teng, Yutao Liu, Yongbing Zhang, Haoqian Wang, and Xiangyang Ji. 2021. Precise no-reference image quality evaluation based on distortion identification. ACM Trans. Multim. Comput., Commun. Applic. 17, 3s (2021), 21.

[4]

Yipeng Liu, Qi Yang, Yiling Xu, and Le Yang. 2023. Point cloud quality assessment: Dataset construction and learning-based No-reference metric. ACM Trans. Multimedia Comput. Commun. Appl. 19, 2s, Article 80 (April 2023), 26 pages.

Digital Library

[5]

Wenxia Li, Chi Lin, and Ting Luo. 2022. Subjective and objective quality evaluation for underwater image enhancement and restoration. Symmetry 14, 3 (2022), 558.

[6]

Qiuping Jiang, Yuese Gu, Chongyi Li, Runmin Cong, and Feng Shao. 2022. Underwater image enhancement quality evaluation: Benchmark dataset and objective metric. IEEE Trans. Circ. Syst. Vid. Technol. 32, 9 (2022), 5959–5974.

[7]

Guojia Hou, Yuxuan Li, Huan Yang, Kunqian Li, and Zhenkuan Pan. 2023. UID2021: An underwater image dataset for evaluation of no-reference quality assessment metrics. ACM Trans. Multimedia Comput. Commun. Appl. 19, 4, Article 151 (July 2023), 24 pages.

Digital Library

[8]

Pengfei Guo, Lang He, and Shuangyin Liu. 2021. Underwater image quality assessment: Subjective and objective methods. IEEE Trans. Multim. 24 (2021), 1980–1989.

[9]

Ning Yang, Qihang Zhong, Kun Li, Runmin Cong, Yao Zhao, and Sam Kwong. 2021. A reference-free underwater image quality assessment metric in frequency domain. Sig. Process.: Image Commun. 94 (2021), 116218.

[10]

Guangtao Zhai and Xiongkuo Min. 2020. Perceptual image quality assessment: A survey. Sci. China Inf. Sci. 63, 11 (2020), 1–52.

[11]

Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 12 (2012), 4695–4708.

Digital Library

[12]

Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. 2012. Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20, 3 (2012), 209–212.

[13]

Lin Zhang, Lei Zhang, and Alan C. Bovik. 2015. A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24, 8 (2015), 2579–2591.

Digital Library

[14]

Shirui Huang, Keyan Wang, Huan Liu, Jun Chen, and Yunsong Li. 2023. Contrastive semi-supervised learning for underwater image restoration via reliable bank. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’23). 18145–18155.

[15]

Yang Wang, Jing Zhang, Yang Cao, and Zengfu Wang. 2017. A deep CNN method for underwater image enhancement. In IEEE International Conference on Image Processing (ICIP’17). B1382–1386. DOI:109/ICIP.2017.8296508

[16]

Miao Yang and Arcot Sowmya. 2015. An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24, 12 (2015), 6062–6071.

Digital Library

[17]

Karen Panetta, Chen Gao, and Sos Agaian. 2015. Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41, 3 (2015), 541–551.

[18]

Yan Wang, Na Li, and Zongying Li. 2018. An imaging-inspired no-reference underwater color image quality assessment metric. Comput. Electric. Eng. 70 (2018), 904–913.

[19]

Zhihang Luo, Zhijie Tang, Lizhou Jiang, and Chi Wang. 2022. An underwater-imaging-model-inspired no-reference quality metric for images in multi-colored environments. Expert Syst. Applic. 191 (2022), 116361.

Digital Library

[20]

Guojia Hou, Ting Lu, Yuxuan Li, Zhenkuan Pan, and Baoxiang Huang. 2022. No-reference quality assessment for underwater images. Retrieved from: SSRN 4089412 (2022).

[21]

Pengfei Guo, Hantao Liu, Delu Zeng, Tao Xiang, Leida Li, and Ke Gu. 2022. An underwater image quality assessment metric. In IEEE Transactions on Multimedia.DOI:

[22]

Yannan Zheng, Weiling Chen, Rongfu Lin, Tiesong Zhao, and Patrick Le Callet. 2022. UIF: An objective quality assessment for underwater image enhancement. IEEE Trans. Image Process. 31 (2022), 5456–5468.

Digital Library

[23]

Yuxuan Li, Guojia Hou, Wanquan Liu, and Kunqian Li. 2023. No-reference underwater image quality assessment based on quality-aware features. Retrieved from: SSRN 4154043 (2023).

[24]

Paul Drews, Erickson Nascimento, and Filipe Moraes. 2013. Transmission estimation in underwater single images. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 825–830.

Digital Library

[25]

Chongyi Li, Saeed Anwar, and Fatih Porikli. 2020. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recog. 98 (2020), 107038.

Digital Library

[26]

Pritish M. Uplavikar, Zhenyu Wu, and Zhangyang Wang. 2019. All-in-one underwater image enhancement using domain-adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1–8.

[27]

Kaiming He, Jian Sun, and Xiaoou Tang. 2010. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 12 (2010), 2341–2353.

Digital Library

[28]

Haoran Xu, Jianming Guo, and Qing Liu. 2012. Fast image dehazing using improved dark channel prior. In Proceedings of the IEEE International Conference on Information Science and Technology. IEEE, 663–667.

[29]

Kristofor B. Gibson, Dung T. Vo, and Truong Q. Nguyen. 2011. An investigation of dehazing effects on image and video coding. IEEE Trans. Image Process. 21, 2 (2011), 662–673.

Digital Library

[30]

Shih-Chia Huang, Bo-Hao Chen, and Wei-Jheng Wang. 2014. Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Trans. Circ. Syst. Vid. Technol. 24, 10 (2014), 1814–1824.

[31]

Shih-Chia Huang, Jian-Hui Ye, and Bo-Hao Chen. 2014. An advanced single-image visibility restoration algorithm for real-world hazy scenes. IEEE Trans. Industr. Electron. 62, 5 (2014), 2962–2972.

[32]

John Y. Chiang and Ying-Ching Chen. 2011. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21, 4 (2011), 1756–1769.

Digital Library

[33]

Paul Drews, Erickson Nascimento, and Filipe Moraes. 2013. Transmission estimation in underwater single images. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 825–830.

Digital Library

[34]

Adrian Galdran, David Pardo, and Artzai Picón. 2015. Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 26 (2015), 132–145.

Digital Library

[35]

Xinwei Zhao, Tao Jin, and Song Qu. 2015. Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng. 94 (2015), 163–172.

[36]

Xuesong Jiang, Hongxun Yao, and Shengping Zhang. 2013. Night video enhancement using improved dark channel prior. In Proceedings of the IEEE International Conference on Image Processing. IEEE, 553–557.

[37]

Dana Berman, Deborah Levy, and Shai Avidan. 2020. Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans. Pattern Anal. Mach. Intell. 43, 8 (2020), 2822–2837.

[38]

Yan-Tsung Peng, Keming Cao, and Pamela C. Cosman. 2018. Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27, 6 (2018), 2856–2868.

[39]

Dana Berman, Tali Treibitz, and Shai Avidan. 2017. Diving into haze-lines: Color restoration of underwater images. In Proceedings of the British Machine Vision Conference, Vol. 1.

[40]

Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, and Dacheng Tao. 2019. An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29 (2019), 4376–4389.

[41]

Md Jahidul Islam, Peigen Luo, and Junaed Sattar. 2020. Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception. arXiv preprint arXiv:2002.01155 (2020).

[42]

ITU-R. 2012. Methodology for the subjective assessment of the quality of television pictures. ITU-R Recommendation BT.500-13. https://www.itu.int/rec/R-REC-BT.500-14-201910-I

[43]

Kalpana Seshadrinathan, Rajiv Soundararajan, Alan Conrad Bovik, and Lawrence K. Cormack. 2010. Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19, 6 (2010), 1427–1441.

Digital Library

[44]

Lin Ma, Weisi Lin, and Chenwei Deng. 2012. Image retargeting quality assessment: A study of subjective scores and objective metrics. IEEE J. Select. Topics Sig. Process. 6, 6 (2012), 626–639.

[45]

Daniel L. Ruderman. 1994. The statistics of natural images. Netw.: Comput. Neural Syst. 5, 4 (1994), 517.

[46]

Deepti Ghadiyaram and Alan C. Bovik. 2017. Perceptual quality prediction on authentically distorted images using a bag of features approach. J. Vis. 17, 1 (2017), 32–32.

[47]

Michele A. Saad, Alan C. Bovik, and Christophe Charrier. 2012. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21, 8 (2012), 3339–3352.

Digital Library

[48]

Zhou Wang, Eero P. Simoncelli, and Alan C. Bovik. 2003. Multiscale structural similarity for image quality assessment. In Proceedings of the 37th Asilomar Conference on Signals, Systems & Computers, Vol. 2. IEEE, 1398–1402.

[49]

Nour-Eddine Lasmar, Youssef Stitou, and Yannick Berthoumieu. 2009. Multiscale skewed heavy tailed model for texture analysis. In Proceedings of the IEEE International Conference on Image Processing. IEEE, 2281–2284.

[50]

Karen Panetta, Sos Agaian, Yicong Zhou, and Eric J. Wharton. 2010. Parameterized logarithmic framework for image enhancement. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 41, 2 (2010), 460–473.

Digital Library

[51]

Domonkos Varga. 2020. No-reference image quality assessment based on the fusion of statistical and perceptual features. J. Imaging 6, 8 (2020), 75.

[52]

O. S. Al-Kadi\(^\ast\) and D. Watson. 2008. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. In IEEE Transactions on Biomedical Engineering, 55, 7 (2008), 1822–1830. DOI:

[53]

Rong-En Fan, Kai-Wei Chang, and Cho-Jui Hsieh. 2008. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9 (2008), 1871–1874.

Digital Library

[54]

Xiongkuo Min, Kede Ma, and Ke Gu. 2017. Unified blind quality assessment of compressed natural, graphic, and screen content images. IEEE Trans. Image Process. 26, 11 (2017), 5462–5474.

Digital Library

[55]

Xiongkuo Min, Ke Gu, and Guangtao Zhai. 2017. Blind quality assessment based on pseudo-reference image. IEEE Trans. Multim. 20, 8 (2017), 2049–2062.

Cited By

Duminil AIeng SGruyer D(2024)Assessing fidelity in synthetic datasets: A multi-criteria combination methodology2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706451(1-8)Online publication date: 8-Jul-2024
https://doi.org/10.23919/FUSION59988.2024.10706451
Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Li XXu HJiang GYu MChen YLuo TYing H(2024)Underwater Image Quality Assessment Based on Multiscale and Antagonistic EnergyIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.333865773(1-14)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3338657

Index Terms

Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective Method
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 3

March 2024

665 pages

EISSN:1551-6865

DOI:10.1145/3613614

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

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Publication History

Published: 23 October 2023

Online AM: 20 September 2023

Accepted: 12 September 2023

Revised: 10 August 2023

Received: 15 September 2022

Published in TOMM Volume 20, Issue 3

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Natural Science Foundation of China
Zhejiang Natural Science Foundation of China
Scientific Research Fund of Zhejiang Provincial Education Department
Natural Science Foundation of Ningbo, China

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Cited By

Duminil AIeng SGruyer D(2024)Assessing fidelity in synthetic datasets: A multi-criteria combination methodology2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706451(1-8)Online publication date: 8-Jul-2024
https://doi.org/10.23919/FUSION59988.2024.10706451
Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Li XXu HJiang GYu MChen YLuo TYing H(2024)Underwater Image Quality Assessment Based on Multiscale and Antagonistic EnergyIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.333865773(1-14)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3338657

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