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

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.

References

[1]
Miao Yang, Jintong Hu, Chongyi Li, Gustavo Rohde, Yixiang Du, and Ke Hu. 2019. An in-depth survey of underwater image enhancement and restoration. IEEE Access 7 (Aug. 2019), 123638–123657.
[2]
Min Han, Zhiyu Lyu, Tie Qiu, and Meiling Xu. 2020. A review on intelligence dehazing and color restoration for underwater images. IEEE Trans. Syst. Man Cybern. Syst. 50, 5 (May 2020), 1820–1832.
[3]
Saeed Anwar and Chongyi Li. 2020. Diving deeper into underwater image enhancement: A survey. Signal Process. Image Commun. 89 (Nov. 2020), 115978.
[4]
Miao Yang and Arcot Sowmya. 2015. An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24, 12 (Dec. 2015), 6062–6071.
[5]
Karen Panetta, Chen Gao, and Sos Again. 2016. Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41, 3 (July 2016), 541–551.
[6]
Yan Wang, Na Li, Zongying Li, Zhaorui Gu, Haiyong Zheng, Bing Zheng, and Mengnan Sun. 2017. An imaging-inspired no-reference underwater color image quality assessment metric. Comput. Electron. Eng. 70 (Dec. 2017), 904–913.
[7]
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. Signal Process. Image Commun. 94 (March 2021), 116218.
[8]
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 (Nov. 2019), 4376–4389.
[9]
Risheng Liu, Xin Fan, Ming Zhu, Minjun Hou, and Zhongxuan Luo. 2020. Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light. IEEE Trans. Circuits Syst. Video Technol. 30, 12 (Jan. 2020), 4861–4875.
[10]
Dana Berman, Deborah Levy, Shai Avidan, and Tali Treibitz. 2021. Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans. Pattern Anal. Mach. Intell. 43, 8 (Aug. 2021), 2822–2837.
[11]
Amanda Duarte, Felipe Codevilla, Joel De O. Gaya, and Silvia S. C. Botelho. 2016. A dataset to evaluate underwater image restoration methods. In Proceedings of the 2016 MTS/IEEE OCEANS Conference (OCEANS’16). 1–6.
[12]
C. Sánchez-Ferreira, L. S. Coelho, H. V. H. Ayala, M. C. Q. Farias, and C. H. Llanos. 2019. Bio-inspired optimization algorithms for real underwater image restoration. Signal Process. Image Commun. 77 (Sept. 2019), 49–65.
[13]
Chongyi Li, Saeed Anwar, and Fatih Porikli. 2020. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recognit. 98 (Feb. 2020), 107038.
[14]
Guojia Hou, Xin Zhao, Zhenkuan Pan, Huan Yang, Lu Tan, and Jingming Li. 2020. Benchmarking underwater image enhancement and restoration, and beyond. IEEE Access 8 (July 2020), 122078–122091.
[15]
Di Wu, Fei Yuan, and En Cheng. 2020. Underwater no-reference image quality assessment for display module of ROV. Sci. Program. 2 (Aug. 2020), 1–15.
[16]
Pengfei Guo, Lang He, Shuangyin Liu, Delu Zeng, and Hantao Liu. 2021. Underwater image quality assessment: Subjective and objective methods. IEEE Trans. Multimedia 24 (April 2021), 1980–1989.
[17]
Alan Conrad Bovik, Muhammad Farooq Sabir, and Hamid R. Sheikh. 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15, 11 (Nov. 2006), 3440–3451.
[18]
Y. Horita, K. Shibata, and Y. Kawayoke. 2023. MICT Image Quality Evaluation Database. Retrieved January 10, 2023 from https://computervisiononline.com/dataset/1105138668.
[19]
Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Karen Egiazarian, Jaakko Astola, Marco Carli, and Federica Battisti. 2009. TID2008: A database for evaluation of full-reference visual quality assessment metrics. Adv. Modern Radioelectron. 10, 4 (2009), 30–45.
[20]
Toni Virtanen, Mikko Nuutinen, Mikko Vaahteranoksa, Pirkko Oittinen, and Jukka Häkkinen. 2015. CID2013: A database for evaluating no-reference image quality assessment algorithms. IEEE Trans. Image Process. 24, 1 (Jan. 2015), 390–402.
[21]
Wen Sun, Fei Zhou, and Qingmin Liao. 2017. MDID: A multiply distorted image database for image quality assessment. Pattern Recognit. 61 (Jan. 2017), 153–168.
[22]
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. 2017. Waterloo exploration database: New challenges for image quality assessment models. IEEE Trans. Image Process. 26, 2 (Feb. 2017), 1004–1016.
[23]
Dinesh Jayaraman, Anish Mittal, Anush K. Moorthy, and Alan C. Bovik. 2012. Objective quality assessment of multiply distorted images. In Proceedings of the 2012 Conference Record of the 46th Asilomar Conference on Signals, Systems, and Computers (ASILOMAR’12). 1693–1697.
[24]
Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, et al. 2015. Image database TID2013: Peculiarities, results and perspectives. Signal Process. Image Commun. 30 (Jan. 2015), 57–77.
[25]
Silvia Corchs and Francesca Gasparini. 2017. A multidistortion database for image quality. In Proceedings of the International Workshop on Computational Color Imaging (CCIW’17). 95–104. http://www.mmsp.unimib.it/image-quality/.
[26]
Alexandre Ciancio, André Luiz N. Targino da Costa, Eduardo A. B. da Silva, Amir Said, Ramin Samadani, and Pere Obrador. 2011. No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Trans. Image Process. 20, 1 (Jan. 2011), 64–75.
[27]
Deepti Ghadiyaram and Alan C. Bovik. 2016. Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process. 25, 1 (Jan. 2016), 372–387.
[28]
Hanhe Lin, Vlad Hosu, and Dietmar Saupe. 2018. KonIQ-10k: Towards an ecologically valid and large-scale IQA database. arXiv:1803.08489.
[29]
Alexandre Ninassi, Patrick Le Callet, and Florent Autrusseau. 2006. Pseudo no reference image quality metric using perceptual data hiding. In Proceedings of SPIE Human Vision and Electronic Imaging.
[30]
Eric Cooper Larson and Damon Michael Chandler. 2010. Most apparent distortion: Full-reference image quality assessment and the role of strategy. J. Electron. Imag. 19, 1 (Jan. 2010), 011006.
[31]
Felipe Codevilla, Joel De O. Gaya, Nelson Duarte Filho, and Silvia S. C. Costa Botelho. 2015. Achieving turbidity robustness on underwater images local feature detection. Int. J. Comput. Vis. 60, 2 (Sept. 2015), 91–110.
[32]
Yupeng Ma, Xiaoyi Feng, Lujing Chao, Dong Huang, Zhaoqiang Xia, and Xiaoyue Jiang. 2018. A new database for evaluating underwater image processing methods. In Proceedings of the 2018 8th International Conference on Image Processing Theory, Tools, and Applications (IPTA’18). 1–6.
[33]
Anush Krishna Moorthy and Alan Conrad Bovik. 2010. A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17, 5 (May 2010), 513–516.
[34]
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 (Dec. 2012), 4695–4708.
[35]
Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. 2013. Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20, 3 (March 2013), 209–212.
[36]
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 (Aug. 2012), 3339-3352.
[37]
Kede Ma, Wentao Liu, Kai Zhang, Zhengfang Duanmu, Zhou Wang, and Wangmeng Zuo. 2018. End-to-end blind image quality assessment using deep neural networks. IEEE Trans. Image Process. 27, 3 (March 2018), 1202–1213.
[38]
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi. 2020. MetaIQA: Deep meta-learning for no-reference image quality assessment. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20). 14131–14140.
[39]
Jinjian Wu, Jupo Ma, Fuhu Liang, Weisheng Dong, Guangming Shi, and Weisi Lin. 2020. End-to-end blind image quality prediction with cascaded deep neural network. IEEE Trans. Image Process. 29 (June 2020), 7414–7426.
[40]
Le Kang, Peng Ye, Yi Li, and David Doermann. 2014. Convolutional neural networks for no-reference image quality assessment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 1733–1740.
[41]
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. 2020. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20). 3664–3673.
[42]
Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang. 2020. Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Trans. Circuits Syst. Video Technol. 30, 1 (Jan. 2020), 36–47.
[43]
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, and Thomas Wiegand. 2018. Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27, 1 (Jan. 2018), 206–219.
[44]
Weixia Zhang, Kede Ma, Guangtao Zhai, and Xiaokang Yang. 2021. Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Trans. Image Process. 30 (2021), 3474–3486.
[45]
Weixia Zhang, Dingquan Li, Chao Ma, Guangtao Zhai, Xiaokang Yang, and Kede Ma. 2022. Continual learning for blind image quality assessment. IEEE Trans. Pattern Anal. Mach. Intell. Early access, 2022.
[46]
Jing Wang, Haotian Fan, Xiaoxia Hou, Yitian Xu, Tao Li, Xuechao Lu, and Lean Fu. 2022. MSTRIQ: No reference image quality assessment based on swin transformer with multi-stage fusion. In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’22). 1268–1277.
[47]
Rony Ferzli and Lina J. Karam. 2019. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18, 4 (April 2009), 717–728.
[48]
Niranjan D. Narvekar and Lina J. Karam. 2011. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Process. 20, 9 (Sept. 2011), 2678–2683.
[49]
Leida Li, Weisi Lin, Xuesong Wang, Gaobo Yang, Khosro Bahrami, and Alex C. Kot. 2016. No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Image Cybern. 46, 1 (Jan. 2016), 39–50.
[50]
Lark Kwon Choi, Jaehee You, and Alan Conrad Bovik. 2015. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24, 11 (Nov. 2015), 3888–3901.
[51]
Lixiong Liu, Tianshu Wang, and Hua Huang. 2019. Pre-attention and spatial dependency driven no-reference image quality assessment. IEEE Trans. Multimedia 21, 9 (Sept. 2019), 2305–2318.
[52]
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. Multimed. Comput. Commun. Appl. 17, 110 (Oct. 2021), 1–21.
[53]
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.
[54]
Qiuping Jiang, Yuese Gu, Chongyi Li, Runmin Cong, and Feng Shao. 2022. Underwater image enhancement quality evaluation: Benchmark dataset and objective metric. IEEE Trans. Circuits Syst. Video Technol. 32, 9 (Sept. 2022), 5959–5974.
[55]
Zhenqi Fu, Xueyang Fu, Yue Huang, and Xinghao Ding. 2022. Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement. Signal Process. Image Commun. 102 (March 2022), 116622.
[56]
Chunle Guo, Ruiqi Wu, Xin Jin, Linghao Han, Zhi Chai, Weidong Zhang, and Chongyi Li. 2022. Underwater Ranker: Learn which is better and how to be better. arXiv:2208.06857.
[57]
Adrian Galdran, David Pardo, Artzai Picón, and Aitor Alvarez-Gila. 2015. Automatic red channel underwater image restoration. J. Vis. Commun. Image Represent. 26 (Jan. 2015), 132–145.
[58]
Guojia Hou, Zhenkuan Pan, Guodong Wang, Huan Yang, and Jinming Duan. 2019. An efficient nonlocal variational method with application to underwater image restoration. Neurocomputing 369 (Dec. 2019), 106–121.
[59]
Chongyi Li, Jichang Guo, Runmin Cong, Yanwei Pang, and Bo Wang. 2016. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans. Image Process. 25, 12 (Dec. 2016), 5664–5677.
[60]
Xinjie Li, Guojia Hou, Lu Tan, and Wanquan Liu. 2020. A hybrid framework for underwater image enhancement. IEEE Access 8 (Oct. 2020), 2169–3536.
[61]
Weidong Zhang, Peixian Zhuang, Haihan Sun, Guohou Li, Sam Kwong, and Chongyi Li. 2022. Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Trans. Image Process. 31 (June 2022), 3997–4010.
[62]
Codruta O. Ancuti, Cosmin Ancuti, Christophe De Vleeschouwer, and Philippe Bekaert. 2018. Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27, 1 (Jan. 2018), 379–393.
[63]
Nicholas Hope. n.d. Bubble Vision Underwater Imaging. Retrieved January 10, 2023 from https://bubblevision.com.
[64]
Peixian Zhuang, Chongyi Li, and Jiamin Wu. 2021. Bayesian retinex underwater image enhancement. Eng. Appl. Artif. Intell. 101 (May 2021), 104171.
[65]
Kashif Iqbal, Rosalina Abdul Salam, Azam Osman, and Abdullah Zawawi Talib. 2007. Underwater image enhancement using an integrated colour model. IAENG Int. J. Comput. Sci. 34, 2 (March 2007), 239–244.
[66]
Xueyang Fu and Xiangyong Cao. 2020. Underwater image enhancement with global–local networks and compressed-histogram equalization. Signal Process. Image Commun. 86 (Aug. 2020), 115892.
[67]
Guojia Hou, Zhenkuan Pan, Baoxiang Huang, Guodong Wang, and Xin Luan. 2018. Hue preserving-based approach for underwater colour image enhancement. IET Image Process. 12, 2 (Feb. 2018), 292–298.
[68]
Yan-Tsung Peng and Pamela C. Cosman. 2017. Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26, 4 (April 2017), 1579–1594.
[69]
Tunai Porto Marques and Alexandra Branzan Albu. 2020. L2UWE: A framework for the efficient enhancement of low-light underwater images using local contrast and multi-scale fusion. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’20). 538–539.
[70]
Xueyang Fu, Zhiwen Fan, Mei Ling, Yue Huang, and Xinghao Ding. 2017. Two-step approach for single underwater image enhancement. In Proceedings of the 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS’17). 789–794.
[71]
Chongyi Li, Saeed Anwar, Junhui Hou, Runmin Cong, Chunle Guo, and Wenqi Ren. 2021. Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE Trans. Image Process. 30 (May 2021), 4985–5000.
[72]
Jun Xie, Guojia Hou, Guodong Wang, and Zhenkuan Pan. 2022. A variational framework for underwater image dehazing and deblurring. IEEE Trans. Circuits Syst. Video Technol. 32, 6 (June 2022), 3514–3526.
[73]
Guojia Hou, Jingming Li, Guodong Wang, Huan Yang, Baoxiang Huang, and Zhenkuan Pan. 2020. A novel dark channel prior guided variational framework for underwater image restoration. J. Vis. Commun. Image Represent. 66 (Jan. 2020), 102732.
[74]
Xueyang Fu, Peixian Zhuang, Yue Huang, Yinghao Liao, Xiao-Ping Zhang, and Xinghao Ding. 2014. A retinex-based enhancing approach for single underwater image. In Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP’14). 4572–4576.
[75]
Stefan Winkler. 2012. Analysis of public image and video databases for quality assessment. IEEE J. Sel. Top. Signal. Process. 6, 6 (Aug. 2012), 616–625.
[76]
Yang Wang, Yang Cao, Jing Zhang, Feng Wu, and Zhengjun Zha. 2021. Leveraging deep statistics for underwater image enhancement. ACM Trans. Multimed. Comput. Commun. Appl. 17, 116 (Oct. 2021), 1–20.
[77]
Xinjie Li, Guojia Hou, Kunqian Li, and Zhenkuan Pan. 2022. Enhancing underwater image via adaptive color and contrast enhancement, and denoising. Eng. Appl. Artif. Intell. 111 (May 2022), 104759.
[78]
Qi Qi, Kunqian Li, Haiyong Zheng, Xiang Gao, Guojia Hou, and Kun Sun. 2022. SGUIE-Net: Semantic attention guided underwater image enhancement with multi-scale perception. IEEE Trans. Image Process. 31 (Oct. 2022), 6816–6830.
[79]
Peixian Zhuang, Jiamin Wu, Fatih Porikli, and Chongyi Li. 2022. Underwater image enhancement with hyper-Laplacian reflectance priors. IEEE Trans. Image Process. 31 (Aug. 2022), 5442–5455.
[80]
Jingchun Zhou, Tongyu Yang, Weishen Chu, and Weishi Zhang. 2022. Underwater image restoration via backscatter pixel prior and color compensation. Eng. Appl. Artif. Intell. 111 (May 2022), 104785.
[81]
Jieyu Yuan, Zhanchuan Cai, and Wei Cao. 2022. TEBCF: Real-world underwater image texture enhancement model based on blurriness and color fusion. IEEE Trans. Geosci. Remote Sens. 60 (Oct. 2021), 4204315.
[82]
Kuanqin Li, Li Wu, Qi Qi, Wenjie Liu, Xiang Gao, Liqin Zhou, and Dalei Song. 2022. Beyond single reference for training: Underwater image enhancement via comparative learning. IEEE Trans. Circuits Syst. Video Technol. Early access, November 28, 2022.
[83]
Nan Li, Guojia Hou, Yuhai Liu, Zhenkuan Pan, and Lu Tan. 2022. Single underwater image enhancement using integrated variational model, Digit. Signal Process. 129 (2022), 103660.
[84]
S. Alireza Golestaneh, Saba Dadsetan, and Kris M. Kitani. 2022. No-reference image quality assessment via transformers, relative ranking, and self-consistency. In Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV’22). 3989–3999.

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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
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    Association for Computing Machinery

    New York, NY, United States

    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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    Author Tags

    1. Underwater image
    2. image quality assessment
    3. benchmark dataset
    4. image enhancement and restoration
    5. mean opinion score

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