The review of distortion-related image quality assessment

Jiebin Yan; Yuming Fang; Xuelin Liu

doi:10.11834/jig.210790

Review | Views : 0 下载量: 4112 CSCD: 0

PDF
Export
Share
Collection
Album

The review of distortion-related image quality assessment
Vol. 27, Issue 5, Pages: 1430-1466(2022)
Received：13 September 2021，

Revised：2022-1-10，

Accepted：17 January 2022，

Published：16 May 2022
DOI： 10.11834/jig.210790
稿件说明：

移动端阅览

DOI：

Jiebin Yan, Yuming Fang, Xuelin Liu. The review of distortion-related image quality assessment[J]. Journal of Image and Graphics, 2022, 27(5): 1430-1466. DOI： 10.11834/jig.210790.

摘要

随着多媒体技术的快速发展及广泛应用，图像质量评价因其在多媒体处理中的重要作用得到越来越多的关注，其作用包括图像数据筛选、算法参数选择与优化等。根据图像质量评价应用时是否需要参考信息，它可分为全参考图像质量评价、半参考图像质量评价和无参考图像质量评价，前两类分别需要全部参考信息和部分参考信息，而第3类不需要参考信息。无论是全参考、半参考还是无参考图像质量评价，图像失真对图像质量评价的影响均较大，主要体现在图像质量评价数据库构建和图像质量评价模型设计两方面。本文从图像失真的角度，主要概述2011—2021年国内外公开发表的图像质量评价模型，涵盖全参考、半参考和无参考模型。根据图像的失真类型，将图像质量评价模型分为针对合成失真的图像质量评价模型、针对真实失真的图像质量评价模型和针对算法相关失真的图像质量评价模型。其中，合成失真是指人工添加噪声，如高斯噪声和模糊失真，通常呈现均匀分布；真实失真是指在图像的获取中，由于环境、拍摄设备或拍摄操作不当等因素所引入的失真类型。相对合成失真，真实失真更为复杂，可能包括一种或多种失真，数据收集难度更大；算法相关失真是指图像处理算法或计算机视觉算法在处理图像时，由于算法本身的缺陷或性能不足等原因而出现在结果图像中的降质，相对合成失真和真实失真，算法相关失真的显著特点是该类型失真呈现非均匀分布。本文介绍现有的图像质量评价数据库，包括图像数据来源和数据库构建细节等；然后重点介绍图像质量评价模型的设计思想。最后总结了介绍的图像质量评价模型，并指出未来可能的发展方向。

Abstract

Image quality assessment (IQA) has been widely applied to multimedia processing

such as low-quality image screening

parameter tuning

and algorithm optimization. IQA models can be classified into three categories like full-reference (FR)

reduced-reference (RR) and no-reference/blind (NR/B) models in terms of the accessibility to reference information. The FR and RR IQA models are based on full and partial reference information each while NR/B models can assess image visual quality without reference information. The early IQA models mainly relies on feature engineering

and their performance is limited by the representation ability of hand-crafted features. Benefiting from the excellent representation ability of deep neural networks

the deep learning based IQA models have shown superior performance and surpassed the feature engineering based IQA models

especially in representing large number of images. Due to the strong representation ability

the deep learning based IQA models have attracted most of our attention in the past decade. Additionally

IQA models have been introduced to image restoration

super-resolution and other tasks to improve algorithm performance and facilitate better benchmarks. For instance

some differentiable FR-IQA models like structural similarity(SSIM) and multi-scale SSIM(MSSIM) can be used straightforward via optimization

facilitating the relevant models training. In the development of IQA research

image distortion plays an important role. Both image quality database construction and IQA models are developed by considering distortion. Previous IQA studies are constrained of dozens of pristine images

and then generate their distorted images via disrupting the pristine images artificially. Benefiting from the uniformity of this type of images

the global quality score of each image in the early image quality databases can be regarded as the quality label of the image patches (e.g.

32×32 pixels) extracted from the corresponding image. Consequently

huge amount of data like local patches and the associated global quality scores can be carried out to learn the mapping function from local patch to visual quality. However

the patch-based strategy may be problematic

since such a small patch is unable to capture image context information. Besides

this data augmentation operation could not be simply adapted to quality assessment of authentic distorted images and other types of images such as 3D synthesized images

since authentic distortions are much more complex than simulated distortions and the distortions in 3D synthesized images exhibit non-uniform distribution. Customized solutions regarding to image distortion are required to be implemented in IQA models

e.g.

we can use local variation measurement and global change modeling to capture the distortion in 3D synthesized images. Our research mainly introduces the distortion-related IQA models from 2011 to 2021

aiming to facilitate a review on the evolution of image quality databases and IQA models both. In the light of image distortion categories

IQA models are classified into three categories: the models for simulated distortions

the models for authentic distortions

and the models for the distortions related to algorithms. Specifically

simulated distortions are also called artificial distortions

which show uniform distribution

including Gaussian noise and motion blur

etc. Authentic distortions are mainly introduced in the context of image photography scenario

shooting equipment

inadequate operation in shooting process. In comparison of simulated distortions

authentic distortions are more complicated

leading to the challenge of data collection. Authentic distortions can be also classified into several major types

like out-of-focus

blur and over-exposure. A large number of authentical distorted images collection need to be followed some distortion rules

and it is a labor-intensive task. The distortions related to algorithms refer to the image degradations occurred in the resultant images due to the intrinsic defects or the limited performance of image processing or computer vision algorithms

and a distinctive characteristic of this type of distortions is that their distributions are inhomogeneous

which differs from other two types of distortions. Overall

current public image quality databases are initial to be promoted

including the details of image data sources and database construction. The designation of IQA models is then involved in. Finally

we summarize the IQA models introduced in this paper

and point out the potential development directions in the future.

关键词

Keywords

references

Adhikarla V K, Vinkler M, Sumin D, Mantiuk R K, Myszkowski K, Seidel H P and Didyk P. 2017. Towards a quality metric for dense light fields//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 3720-3729 [ DOI: 10.1109/CVPR.2017.396 http://dx.doi.org/10.1109/CVPR.2017.396 ]

Ancuti C O, Ancuti C, Timofte R and De Vleeschouwer C. 2018b. I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images[EB/OL]. [2020-04-13] . https://arxiv.org/pdf/1804.05091.pdf https://arxiv.org/pdf/1804.05091.pdf

Ancuti C O, Ancuti C, Timofte R and Vleeschouwer C D. 2018a. O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE: 867-8678 [ DOI: 10.1109/CVPRW.2018.00119 http://dx.doi.org/10.1109/CVPRW.2018.00119 ]

Bare B, Li K, Yan B, Feng B L and Yao C F. 2018a. A deep learning based no-reference image quality assessment model for single-image super-resolution//Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. Calgary, Canada: IEEE: 1223-1227 [ DOI: 10.1109/ICASSP.2018.8461931 http://dx.doi.org/10.1109/ICASSP.2018.8461931 ]

Battisti F, Bosc E, Carli M, Le Callet P and Perugia S. 2015. Objective image quality assessment of 3D synthesized views. Signal Processing: Image Communication, 30: 78-88 [DOI: 10.1016/j.image.2014.10.005]

Benoit A, Le Callet P, Campisi P and Cousseau R. 2008. Quality assessment of stereoscopic images. EURASIP Journal of Image and Video Processing, 2008: #659024 [DOI: 10.1155/2008/659024]

Beron J, Benitez-Restrepo H D and Bovik A C. 2020. Blind image quality assessment for super resolution via optimal feature selection. IEEE Access, 8: 143201-143218 [DOI: 10.1109/ACCESS.2020.3014497]

Bevilacqua M, Roumy A, Guillemot C and Morel M L A. 2012. Low-complexity single-image super-resolution based on nonnegative neighbor embedding//Proceedings of British Machine Vision Conference. Surrey, UK: BMVA Press: 135 [ DOI: 10.5244/C.26.135 http://dx.doi.org/10.5244/C.26.135 ]

Bosc E, Pepion R, Le Callet P, Koppel M, Ndjiki-Nya P, Pressigout M and Morin L. 2011. Towards a new quality metric for 3-D synthesized view assessment. IEEE Journal of Selected Topics in Signal Processing, 5(7): 1332-1343 [DOI: 10.1109/JSTSP.2011.2166245]

Bosse S, Maniry D, Müller K R, Wiegand T and Samek W. 2018b. Deep neural networks for no-reference and full-reference image quality assessment. IEEE Transactions on Image Processing, 27(1): 206-219 [DOI: 10.1109/TIP.2017.2760518]

Chen B L, Li H L, Fan H F and Wang S Q. 2021a. No-reference screen content image quality assessment with unsupervised domain adaptation. IEEE Transactions on Image Processing, 30: 5463-5476 [DOI: 10.1109/TIP.2021.3084750]

Chen J L, Xu Y W, Ma K D, Huang H W and Zhao T S. 2018a. A hybrid quality metric for non-integer image interpolation//Proceedings of 2018 Tenth International Conference on Quality of Multimedia Experience. Cagliari, Italy: IEEE: 1-3 [ DOI: 10.1109/QoMEX.2018.8463405 http://dx.doi.org/10.1109/QoMEX.2018.8463405 ]

Chen J N, Shen L Q, Zheng L R and Jiang X H. 2018b. Naturalization module in neural networks for screen content image quality assessment. IEEE Signal Processing Letters, 25(11): 1685-1689 [DOI: 10.1109/LSP.2018.2871250]

Chen M J, Cormack L K and Bovik A C. 2013a. No-reference quality assessment of natural stereopairs. IEEE Transactions on Image Processing, 22(9): 3379-3391 [DOI: 10.1109/TIP.2013.2267393]

Chen M J, Su C C, Kwon D K, Cormack L K and Bovik A C. 2013b. Full-reference quality assessment of stereopairs accounting for rivalry. Signal Processing: Image Communication, 28(9): 1143-1155 [DOI: 10.1016/j.image.2013.05.006]

Chen QF, Xu J and Koltun V. 2017. Fast image processing with fully-convolutional networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2516-2525 [ DOI: 10.1109/ICCV.2017.273 http://dx.doi.org/10.1109/ICCV.2017.273 ]

Chen Z B, Xu J H, Lin C Y and Zhou W. 2020. Stereoscopic omnidirectional image quality assessment based on predictive coding theory. IEEE Journal of Selected Topics in Signal Processing, 14(1): 103-117 [DOI: 10.1109/JSTSP.2020.2968182]

Chen Z Y, Jiang T T and Tian Y H. 2014. Quality assessment for comparing image enhancement algorithms//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 3003-3010 [ DOI: 10.1109/CVPR.2014.384 http://dx.doi.org/10.1109/CVPR.2014.384 ]

Choi L K, You J and Bovik A C. 2015. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Transactions on Image Processing, 24(11): 3888-3901 [DOI: 10.1109/TIP.2015.2456502]

Chu Y, Luo G X and Chen F. 2021. A real haze video database for haze level evaluation//Proceedings of the 13th International Conference on Quality of Multimedia Experience. Montreal, Canada: IEEE: 69-72 [ DOI: 10.1109/QoMEX51781.2021.9465461 http://dx.doi.org/10.1109/QoMEX51781.2021.9465461 ]

Ciancio A, Da Costa A L N T T, Da Silva E A B, Said A, Samadani R and Obrador P. 2011. No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Transactions on Image Processing, 20(1): 64-75 [DOI: 10.1109/TIP.2010.2053549]

Conze P H, Robert P and Morin L. 2012. Objective view synthesis quality assessment//Proceedings of SPIE 8288, Stereoscopic Displays and Applications XXIII. Burlingame, USA: SPIE: 82881M [ DOI: 10.1117/12.908762 http://dx.doi.org/10.1117/12.908762 ]

Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S and Schiele B. 2016. The Cityscapes dataset for semantic urban scene understanding//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 3213-3223 [ DOI: 10.1109/CVPR.2016.350 http://dx.doi.org/10.1109/CVPR.2016.350 ]

Dendi S V R, Dev C, Kothari N and Channappayya S S. 2019. Generating image distortion maps using convolutional autoencoders with application to no reference image quality assessment. IEEE Signal Processing Letters, 26(1): 89-93 [DOI: 10.1109/LSP.2018.2879518]

Ding K Y, Ma K D, Wang S Q and Simoncelli E P. 2020. Image quality assessment: unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5): 2567-2581 [DOI: 10.1109/TPAMI.2020.3045810]

Ding K Y, Ma K D, Wang S Q and Simoncelli E P. 2021. Comparison of full-reference image quality models for optimization of image processing systems. International Journal of Computer Vision, 129(4): 1258-1281 [DOI: 10.1007/s11263-020-01419-7]

Dong X, Zhu Y, Li W X, Xie L X, Wong A and Yuille A. 2015. Fidelity-naturalness evaluation of single image super resolution [EB/OL]. [2020-11-21] . https://arxiv.org/pdf/1511.06834.pdf https://arxiv.org/pdf/1511.06834.pdf

Duan H Y, Zhai G T, Min X K, Zhu Y C, Fang Y and Yang X K. 2018. Perceptual quality assessment of omnidirectional images//Proceedings of 2018 IEEE International Symposium on Circuits and Systems. Florence, Italy: IEEE: 1-5 [ DOI: 10.1109/ISCAS.2018.8351786 http://dx.doi.org/10.1109/ISCAS.2018.8351786 ]

Fang Y M, Du R G, Zuo Y F, Wen W Y and Li L D. 2020a. Perceptual quality assessment for screen content images by spatial continuity. IEEE Transactions on Circuits and Systems for Video Technology, 30(11): 4050-4063 [DOI: 10.1109/TCSVT.2019.2951747]

Fang Y M, Fang Z J, Yuan F N, Yang Y, Yang S Y and Xiong N N. 2017a. Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11): 2956-2966 [DOI: 10.1109/TSMC.2016.2557225]

Fang Y M, Liu J Y, Zhang Y B, Lin W S and Guo Z M. 2019a. Reduced-reference quality assessment of image super-resolution by energy change and texture variation. Journal of Visual Communication and Image Representation, 60: 140-148 [DOI: 10.1016/j.jvcir.2018.12.035]

Fang Y M, Ma K D, Wang Z, Lin W S, Fang Z J and Zhai G T. 2015. No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Processing Letters, 22(7): 838-842 [DOI: 10.1109/LSP.2014.2372333]

Fang Y M, Sui X J, Yan J B, Liu X L and Huang L P. 2021. Progress in no-reference image quality assessment. Journal of Image and Graphics, 26(2): 265-286

方玉明, 眭相杰, 鄢杰斌, 刘学林, 黄丽萍. 2021. 无参考图像质量评价研究进展. 中国图象图形学报, 26(2): 265-286 [DOI: 10.11834/jig.200274]

Fang Y M, Wei K K, Hou J H, Wen W Y and Imamoglu N. 2018a. Light filed image quality assessment by local and global features of epipolar plane image//Proceedings of the 4th IEEE International Conference on Multimedia Big Data. Xi′an, China: IEEE: 1-6 [ DOI: 10.1109/BigMM.2018.8499086 http://dx.doi.org/10.1109/BigMM.2018.8499086 ]

Fang Y M, Yan J B, Du R G, Zuo Y F, Wen W Y, Zeng Y and Li L D. 2021a. Blind quality assessment for tone-mapped images by analysis of gradient and chromatic statistics. IEEE Transactions on Multimedia, 23: 955-966 [DOI: 10.1109/TMM.2020.2991528]

Fang Y M, Yan J B, Li L D, Wu J J and Lin W S. 2018b. No reference quality assessment for screen content images with both local and global feature representation. IEEE Transactions on Image Processing, 27(4): 1600-1610 [DOI: 10.1109/TIP.2017.2781307]

Fang Y M, Yan J B, Liu X L and Wang J H. 2019b. Stereoscopic image quality assessment by deep convolutional neural network. Journal of Visual Communication and Image Representation, 58: 400-406 [DOI: 10.1016/j.jvcir.2018.12.006]

Fang Y M, Yan J B, Liu J Y, Wang S Q, Li Q H and Guo Z M. 2017c. Objective quality assessment of screen content images by uncertainty weighting. IEEE Transactions on Image Processing, 26(4): 2016-2027 [DOI: 10.1109/TIP.2017.2669840]

Fang Y M, Yan J B, Wang J H, Liu X L, Zhai G T and Le Callet P. 2019c. Learning a no-reference quality predictor of stereoscopic images by visual binocular properties. IEEE Access, 7: 132649-132661 [DOI: 10.1109/ACCESS.2019.2941112]

Fang Y M, Zeng Y, Jiang W H, Zhu H W and Yan J B. 2021b. Superpixel-based quality assessment of multi-exposure image fusion for both static and dynamic scenes. IEEE Transactions on Image Processing, 30: 2526-2537 [DOI: 10.1109/TIP.2021.3053465]

Fang Y M, Zeng K, Wang Z, Lin W S, Fang Z J and Lin C W. 2014. Objective quality assessment for image retargeting based on structural similarity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 4(1): 95-105 [DOI: 10.1109/JETCAS.2014.2298919]

Fang Y M, Zeng Y, Zhu H W and Zhai G T. 2019 d. Image quality assessment of multi-exposure image fusion for both static and dynamic scenes//Proceedings of 2019 IEEE International Conference on Multimedia and Expo.Shanghai, China: IEEE: 442-447 [ DOI: 10.1109/ICME.2019.00083 http://dx.doi.org/10.1109/ICME.2019.00083 ]

Fang Y M, Zhang C, Yang W H, Liu J Y and Guo Z M. 2018c. Blind visual quality assessment for image super-resolution by convolutional neural network. Multimedia Tools and Applications, 77(22): 29829-29846 [DOI: 10.1007/s11042-018-5805-z]

Fang Y M, Zhu H W, Ma K D and Wang Z. 2017b. Perceptual quality assessment of HDR deghosting algorithms//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 3165-3169 [ DOI: 10.1109/ICIP.2017.8296866 http://dx.doi.org/10.1109/ICIP.2017.8296866 ]

Fang Y M, Zhu H W, Ma K D, Wang Z and Li S T. 2020b. Perceptual evaluation for multi-exposure image fusion of dynamic scenes. IEEE Transactions on Image Processing, 29: 1127-1138 [DOI: 10.1109/TIP.2019.2940678]

Fang Y M, Zhu H W, Zeng Y, Ma K D and Wang Z. 2020c. Perceptual quality assessment of smartphone photography//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3674-3683 [ DOI: 10.1109/CVPR42600.2020.00373 http://dx.doi.org/10.1109/CVPR42600.2020.00373 ]

Fu Y, Zeng H Q, Ma L, Ni Z K, Zhu J Q and Ma K K. 2018. Screen content image quality assessment using multi-scale difference of Gaussian. IEEE Transactions on Circuits and Systems for Video Technology, 28(9): 2428-2432 [DOI: 10.1109/TCSVT.2018.2854176]

Gao F and Gao X B. 2014. Active feature learning and its application in blind image quality assessment. Chinese Journal of Computers, 37(10): 2227-2234

高飞, 高新波. 2014. 主动特征学习及其在盲图像质量评价中的应用. 计算机学报, 37(10): 2227-2234 [DOI: 10.3724/SP.J.1016.2014.02227]

Ghadiyaram D and Bovik A C. 2016. Massive online crowdsourced study of subjective and objective picture quality. IEEE Transactions on Image Processing, 25(1): 372-387 [DOI: 10.1109/TIP.2015.2500021]

Ghadiyaram D and Bovik A C. 2017. Perceptual quality prediction on authentically distorted images using a bag of features approach. Journal of Vision, 17(1): 32 [DOI: 10.1167/17.1.32]

Gibson K B and Nguyen T Q. 2013. A no-reference perceptual based contrast enhancement metric for ocean scenes in fog. IEEE Transactions on Image Processing, 22(10): 3982-3993 [DOI: 10.1109/TIP.2013.2265884]

Gu J J, Cai H M, Chen H Y, Ye X X, Jimmy S R and Dong C. 2020a. PIPAL: a large-scale image quality assessment dataset for perceptual image restoration//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK: Springer: 633-651 [ DOI: 10.1007/978-3-030-58621-8 http://dx.doi.org/10.1007/978-3-030-58621-8 ]

Gu K. 2015. Research on Perceptual and Statistical Image Quality Assessment and its Application. Shanghai: Shanghai Jiao Tong University

顾锞. 2015. 基于感知和统计模型的图像质量评价技术及应用研究. 上海: 上海交通大学

Gu K, Qiao J F, Min X K, Yue G H, Lin W S and Thalmann D. 2018a. Evaluating quality of screen content images via structural variation analysis. IEEE Transactions on Visualization and Computer Graphics, 24(10): 2689-2701 [DOI: 10.1109/TVCG.2017.2771284]

Gu K, Tao D C, Qiao J F and Lin W S. 2018b. Learning a no-reference quality assessment model of enhanced images with big data. IEEE Transactions on Neural Networks and Learning Systems, 29(4): 1301-1313 [DOI: 10.1109/TNNLS.2017.2649101]

Gu K, Wang S Q, Yang H, Lin W S, Zhai G T, Yang X K and Zhang W J. 2016c. Saliency-guided quality assessment of screen content images. IEEE Transactions on Multimedia, 18(6): 1098-1110 [DOI: 10.1109/TMM.2016.2547343]

Gu K, Wang S Q, Zhai G T, Ma S W, Yang X K, Lin W S, Zhang W J and Gao W. 2016a. Blind quality assessment of tone-mapped images via analysis of information, naturalness, and structure. IEEE Transactions on Multimedia, 18(3): 432-443 [DOI: 10.1109/TMM.2016.2518868]

Gu K, Zhai G T, Lin W S and Liu M. 2016b. The analysis of image contrast: from quality assessment to automatic enhancement. IEEE Transactions on Cybernetics, 46(1): 284-297 [DOI: 10.1109/TCYB.2015.2401732]

Gu K, Zhai G T, Yang X K and Zhang W J. 2014. Hybrid no-reference quality metric for singly and multiply distorted images. IEEE Transactions on Broadcasting, 60(3): 555-567 [DOI: 10.1109/TBC.2014.2344471]

Gu K, Zhai G T, Yang X K and Zhang W J. 2015. Using free energy principle for blind image quality assessment. IEEE Transactions on Multimedia, 17(1): 50-63 [DOI: 10.1109/TMM.2014.2373812]

Gu K, Zhou J, Qiao J F, Zhai G T, Lin W S and Bovik A C. 2017. No-reference quality assessment of screen content pictures. IEEE Transactions on Image Processing, 26(8): 4005-4018 [DOI: 10.1109/TIP.2017.2711279]

Gu S Y, Bao J M, Chen D and Wen F. 2020b. GIQA: generated image quality assessment//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK: Springer: 369-385 [ DOI: 10.1007/978-3-030-58621-8_22 http://dx.doi.org/10.1007/978-3-030-58621-8_22 ]

Guan J W, Yi S, Zeng X Y, Cham W K and Wang X G. 2017. Visual importance and distortion guided deep image quality assessment framework. IEEE Transactions on Multimedia, 19(11): 2505-2520 [DOI: 10.1109/TMM.2017.2703148]

Hadizadeh H and Bajic I V. 2018. Full-reference objective quality assessment of tone-mapped images. IEEE Transactions on Multimedia, 20(2): 392-404 [DOI: 10.1109/TMM.2017.2740023]

Hassen R, Wang Z and Salama M M A. 2015. Objective quality assessment for multiexposure multifocus image fusion. IEEE Transactions on Image Processing, 24(9): 2712-2724 [DOI: 10.1109/TIP.2015.2428051]

Hautiere N, Tarel J, Aubert D and Dumont E. 2008. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis and Stereology, 27(2): 87-95 [DOI: 10.5566/ias.v27.p87-95]

Hosu V, Lin H H, Sziranyi T and Saupe D. 2020. KonIQ-10k: an ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, 29: 4041-4056 [DOI: 10.1109/TIP.2020.2967829]

Hu B, Li L D, Liu H, Lin W and Q J. 2019. Pairwise-comparison-based rank learning forbenchmarking image restoration algorithms. IEEE Transactions on Multimedia, 21(8): 2042-2056 [DOI: 10.1109/TMM.2019.2894958]

Hu B, Li L D, Qian J S and Fang Y M. 2016. Perceptual evaluation of compressive sensing image recovery//Proceedings of 2016 Eighth International Conference on Quality of Multimedia Experience. Lisbon, Portugal: IEEE: 1-6 [ DOI: 10.1109/QoMEX.2016.7498963 http://dx.doi.org/10.1109/QoMEX.2016.7498963 ]

Hu B, Li L D, Wu J J and Qian J S. 2020. Subjective and objective quality assessment for image restoration: a critical survey. Signal Processing: Image Communication, 85: #115839 [DOI: 10.1016/j.image.2020.115839]

Huang J B, Singh A and Ahuja N. 2015. Single image super-resolution from transformed self-exemplars//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 5197-5206 [ DOI: 10.1109/CVPR.2015.7299156 http://dx.doi.org/10.1109/CVPR.2015.7299156 ]

Huang M K, Shen Q, Ma Z, Bovik A C, Gupta P, Zhou R B and Cao X. 2018. Modeling the perceptual quality of immersive images rendered on head mounted displays: resolution and compression. IEEE Transactions on Image Processing, 27(12): 6039-6050 [DOI: 10.1109/TIP.2018.2865089]

ITU. 2000. Final report from the video quality experts group on the validation of objective models of video quality assessment[EB/OL]. [2020-07-08] . http://videoclarity.com/PDF/COM-80E_final_report.pdf http://videoclarity.com/PDF/COM-80E_final_report.pdf

Jakhetiya V, Gu K, Singhal T, Guntuku S C, Xia Z F and Lin W S. 2019. A highly efficient blind image quality assessment metric of 3-D synthesized images using outlier detection. IEEE Transactions on Industrial Informatics, 15(7): 4120-4128 [DOI: 10.1109/TII.2018.2888861]

Jia H Z, Sun Q S and Wang T H. 2014. Blind image quality assessment based on perceptual features and natural scene statistics. Journal of Image and Graphics, 19(6): 859-867

贾惠珍, 孙权森, 王同罕. 2014. 结合感知特征和自然场景统计的无参考图像质量评价. 中国图象图形学报, 19(6): 859-867 [DOI: 10.11834/jig.20140606]

Jiang H, Jiang G Y, Yu M, Zhang Y, Yang Y, Peng Z J, Chen F and Zhang Q B. 2021. Cubemap-based perception-driven blind quality assessment for 360-degree images. IEEE Transactions on Image Processing, 30: 2364-2377 [DOI: 10.1109/TIP.2021.3052073]

Jiang Q P, Peng Z Y, Yang S and Shao F. 2019. Authentically distorted image quality assessment by learning from empirical score distributions. IEEE Signal Processing Letters, 26(12): 1867-1871 [DOI: 10.1109/LSP.2019.2951533]

Jiang Q P, Shao F and Jiang G Y. 2017. MSFE: blind image quality assessment based on multi-stage feature encoding//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 3160-3164 [ DOI: 10.1109/ICIP.2017.8296865 http://dx.doi.org/10.1109/ICIP.2017.8296865 ]

Kang L, Ye P, Li Y and Doermann D. 2014. Convolutional neural networks for no-reference image quality assessment//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 1733-1740 [ DOI: 10.1109/CVPR.2014.224 http://dx.doi.org/10.1109/CVPR.2014.224 ]

Kang L, Ye P, Li Y and Doermann D. 2015. Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, Canada: IEEE: 2791-2795 [ DOI: 10.1109/ICIP.2015.7351311 http://dx.doi.org/10.1109/ICIP.2015.7351311 ]

Karađuzović-Hadžiabdić K, Telalović J H and Mantiuk R K. 2017. Assessment of multi-exposure HDR image deghosting methods. Computers and Graphics, 63: 1-17 [DOI: 10.1016/j.cag.2017.01.002]

Karras T, Laine S and Aila T. 2019. A style-based generator architecture for generative adversarial networks//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 4396-4405 [ DOI: 10.1109/CVPR.2019.00453 http://dx.doi.org/10.1109/CVPR.2019.00453 ]

Kim H G, Jeong H, Lim H T and Ro Y M. 2019a. Binocular fusion net: deep learning visual comfort assessment for stereoscopic 3D. IEEE Transactions on Circuits and Systems for Video Technology, 29(4): 956-967[DOI: 10.1109/TCSVT.2018.2817250]

Kim H G, Lim H T and Ro Y M. 2020. Deep virtual reality image quality assessment with human perception guider for omnidirectional image. IEEE Transactions on Circuits and Systems for Video Technology, 30(4): 917-928 [DOI: 10.1109/TCSVT.2019.2898732]

Kim J, Nguyen A D and Lee S. 2019b. Deep CNN-based blind image quality predictor. IEEE Transactions on Neural Networks and Learning Systems, 30(1): 11-24 [DOI: 10.1109/TNNLS.2018.2829819]

Kim J, Zeng H, Ghadiyaram D, Lee S, Zhang L and Bovik A C. 2017. Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment. IEEE Signal Processing Magazine, 34(6): 130-141 [DOI: 10.1109/MSP.2017.2736018]

Ko H, Lee D Y, Cho S and Bovik A C. 2020. Quality prediction on deep generative images. IEEE Transactions on Image Processing, 29: 5964-5979 [DOI: 10.1109/TIP.2020.2987180]

Köhler T, Bätz M, Naderi F, Kaup A, Maier A and Riess C. 2020. Toward bridging the simulated-to-real gap: benchmarking super-resolution on real data. IEEE Transactions on Pattern Analysis and machine intelligence, 42(11): 2944-2959 [DOI: 10.1109/TPAMI.2019.2917037]

Kong X, Li K, Yang Q, Liu W and Yang M. 2013. A new image quality metric for image auto-denoising//Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE: 1550-5499 [ DOI: 10.1109/ICCV.2013.359 http://dx.doi.org/10.1109/ICCV.2013.359 ]

Korhonen J, Su Y C and You J Y. 2021. Consumer image quality prediction using recurrent neural networks for spatial pooling [EB/OL]. [2021-01-02] . https://arxiv.org/pdf/2106.00918.pdf https://arxiv.org/pdf/2106.00918.pdf

Krasula L, Fliegel K and Callet P L. 2020. FFTMI: Features fusion for natural tone-mapped images quality evaluation. IEEE Transactions on Multimedia, 22(8): 2038-2047 [DOI: 10.1109/TMM.2019.2952256]

Kundu D and Evans B L. 2016. Visual attention guided quality assessment of tone-mapped images using scene statistics//Proceedings of 2016 IEEE International Conference on Image Processing. Phoenix, USA: IEEE: 2381-8549 [ DOI: 10.1109/ICIP.2016.7532326 http://dx.doi.org/10.1109/ICIP.2016.7532326 ]

Kundu D, Ghadiyaram D, Bovik A C and Evans B L. 2017. Large-scale crowdsourced study for tone-mapped HDR pictures. IEEE Transactions on Image Processing, 26(10): 4725-4740 [DOI: 10.1109/TIP.2017.2713945]

Lai W S, Huang J B, Hu Z, Ahuja N and Yang M H. 2016. A comparative study for single image blind deblurring//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 1701-1709 [ DOI: 10.1109/CVPR.2016.188 http://dx.doi.org/10.1109/CVPR.2016.188 ]

Larson E C and Chandler D M. 2009. Categorical subjective image quality CSIQ database[DB/OL]. [2020-07-08] . http://vision.okstate.edu/csiq/ http://vision.okstate.edu/csiq/

Larson E C and Chandler D M. 2010. Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1): #011006 [DOI: 10.1117/1.3267105]

Le Callet P and Autrusseau F. 2005. Subjective quality assessment IRCCyN/IVC database [DB/OL]. [2020-07-08] . http://www2.irccyn.ec-nantes.fr/ivcdb/ http://www2.irccyn.ec-nantes.fr/ivcdb/

Li A, Li J, Lin Q, Ma C and Yan B. 2020 d. Deep image quality assessment driven single image deblurring//Proceedings of 2020 IEEE International Conference on Multimedia and Expo. London, UK: IEEE: 1945-7871 [ DOI: 10.1109/ICME46284.2020.9102899 http://dx.doi.org/10.1109/ICME46284.2020.9102899 ]

Li B Y, Ren W Q, Fu D P, Tao D C, Feng D, Zeng W J and Wang Z Y. 2019b. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1): 492-505 [DOI: 10.1109/TIP.2018.2867951]

Li D Q, Jiang T T and Jiang M. 2020a. Norm-in-norm loss with faster convergence and better performance for image quality assessment//Proceedings of the 28th ACM International Conference on Multimedia. Seattle, USA: ACM: 789-797 [ DOI: 10.1145/3394171.3413804 http://dx.doi.org/10.1145/3394171.3413804 ]

Li D Q, Jiang T T, Lin W S and Jiang M. 2019a. Which has better visual quality: the clear blue sky or a blurry animal? IEEE Transactions on Multimedia, 21(5): 1221-1234 [ DOI: 10.1109/TMM.2018.2875354 http://dx.doi.org/10.1109/TMM.2018.2875354 ]

Li F, Zhang Y F and Cosman P C. 2021. MMMNet: an end-to-end multi-task deep convolution neural network with multi-scale and multi-hierarchy fusion for blind image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology, 31(12): 4798-4811 [DOI: 10.1109/TCSVT.2021.3055197]

Li J, Zhao Y F, Ye W H, Yu K W and Ge S M. 2020b. Attentive deep stitching and quality assessment for 360° Omnidirectional images. IEEE Journal of Selected Topics in Signal Processing, 14(1): 209-221 [DOI: 10.1109/JSTSP.2019.2953950]

Li L, Yan Y, Lu Z, Wu J, Gu K and Wang S. 2017. No-reference quality assessment of deblurred images based on natural scene statistics. IEEE Access, (5): 2163-2171 [ DOI: 10.1109/ACCESS.2017.2661858 http://dx.doi.org/10.1109/ACCESS.2017.2661858 ]

Li L D, Lin W S, Wang X S, Yang G B, Bahrami K and Kot A C. 2016e. No-reference image blur assessment based on discrete orthogonal moments. IEEE Transactions on Cybernetics, 46(1): 39-50 [DOI: 10.1109/TCYB.2015.2392129]

Li L D, Yan Y, Fang Y M, Wang S Q, Tang L and Qian J S. 2016a. Perceptual quality evaluation for image defocus deblurring. Signal Processing: Image Communication, 48: 81-91 [DOI: 10.1016/j.image.2016.09.005]

Li L D, Zhou Y, Gu K, Yang Y Z and Fang Y M. 2020c. Blind realistic blur assessment based on discrepancy learning. IEEE Transactions on Circuits and Systems for Video Technology, 30(11): 3859-3869 [DOI: 10.1109/TCSVT.2019.2947450]

Li L D, Zhou Y, Lin W S, Wu J J, Zhang X F and Chen B J. 2016b. No-reference quality assessment of deblocked images. Neurocomputing, 177: 572-584 [DOI: 10.1016/j.neucom.2015.11.063]

Li Q H, Lin W S and Fang Y M. 2016c. No-reference quality assessment for multiply-distorted images in gradient domain. IEEE Signal Processing Letters, 23(4): 541-545 [DOI: 10.1109/LSP.2016.2537321]

Li Q H, Lin W S, Xu J T and Fang Y M. 2016d. Blind image quality assessment using statistical structural and luminance features. IEEE Transactions on Multimedia, 18(12): 2457-2469 [DOI: 10.1109/TMM.2016.2601028]

Li S Y, Araujo I B, Ren W Q, Wang Z Y, Tokuda E K, Junior R H, Cesar-Junior R, Zhang J W, Guo X J and Cao X C. 2019c. Single image deraining: a comprehensive benchmark analysis//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 3833-3842 [ DOI: 10.1109/CVPR.2019.00396 http://dx.doi.org/10.1109/CVPR.2019.00396 ]

Liang H and Weller D S. 2016. Comparison-based image quality assessment for selecting image restoration parameters. IEEE Transactions on Image Processing, 25(11): 5118-5130 [DOI: 10.1109/TIP.2016.2601783]

Lin H H, Hosu V and Saupe D. 2019. KADID-10k: a large-scale artificially distorted IQA database//Proceedings of 2019 Eleventh International Conference on Quality of Multimedia Experience. Berlin, Germany: IEEE: 1-3 [ DOI: 10.1109/QoMEX.2019.8743252 http://dx.doi.org/10.1109/QoMEX.2019.8743252 ]

Lin H H, Hosu V and Saupe D. 2020. DeepFL-IQA: weak supervision for deep IQA feature learning [EB/OL]. [2020-01-20] . https://arxiv.org/pdf/2001.08113.pdf https://arxiv.org/pdf/2001.08113.pdf

Lin K Y and Wang G X. 2018. Hallucinated-IQA: no-reference image quality assessment via adversarial learning//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 732-741 [ DOI: 10.1109/CVPR.2018.00083 http://dx.doi.org/10.1109/CVPR.2018.00083 ]

Lin W S and Jay Kuo C C. 2011. Perceptual visual quality metrics: a survey. Journal of Visual Communication and Image Representation, 22(4): 297-312 [DOI: 10.1016/j.jvcir.2011.01.005]

Liu A M, Lin W S and Narwaria M. 2012. Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4): 1500-1512 [DOI: 10.1109/TIP.2011.2175935]

Liu J Y, Xu D J, Yang W H, Fan M H and Huang H F. 2021a. Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision, 129(4): 1153-1184 [DOI: 10.1007/s11263-020-01418-8]

Liu J Z, Zhou W, Xu J H, Li X, An S K and Chen Z B. 2021b. LIQA: lifelong blind image quality assessment [EB/OL]. [2021-04-29] . https://arxiv.org/pdf/2104.14115.pdf https://arxiv.org/pdf/2104.14115.pdf

Liu T J and Liu K H. 2018a. No-reference image quality assessment by wide-perceptual-domain scorer ensemble method. IEEE Transactions on Image Processing, 27(3): 1138-1151 [DOI: 10.1109/TIP.2017.2771422]

Liu W, Zhou F, Lu T, Duan J and Qiu G P. 2021c. Image defogging quality assessment: real-world database and method. IEEE Transactions on Image Processing, 30: 176-190 [DOI: 10.1109/TIP.2020.3033402]

Liu Y M, Wang J, Cho S, Finkelstein A and Rusinkiewicz S. 2013. A no-reference metric for evaluating the quality of motion deblurring. ACM Transactions on Graphics, 32(6): #175 [DOI: 10.1145/2508363.2508391]

Liu Y T, Gu K, Zhang Y B, Li X, Zhai G T, Zhao D B and Gao W. 2020. Unsupervised blind image quality evaluation via statistical measurements of structure, naturalness, and perception. IEEE Transactions on Circuits and Systems for Video Technology, 30(4): 929-943 [DOI: 10.1109/TCSVT.2019.2900472]

Liu Y T, Zhai G T, Gu K, Liu X M, Zhao D B and Gao W. 2018b. Reduced-reference image quality assessment in free-energy principle and sparse representation. IEEE Transactions on Multimedia, 20(2): 379-391 [DOI: 10.1109/TMM.2017.2729020]

LukešT, Fliegel K and Klíma M. 2013. Performance evaluation of image quality metrics with respect to their use for super-resolution enhancement//Proceedings of the 5th International Workshop on Quality of Multimedia Experience. Klagenfurt, Austria: IEEE: 42-43 [ DOI: 10.1109/QoMEX.2013.6603205 http://dx.doi.org/10.1109/QoMEX.2013.6603205 ]

Lv Y Q, Yu M, Jiang G Y, Shao F, Peng Z J and Chen F. 2016. No-reference stereoscopic image quality assessment using binocular self-similarity and deep neural network. Signal Processing: Image Communication, 47: 346-357 [DOI: 10.1016/j.image.2016.07.003]

Ma C, Yang C Y, Yang X K and Yang M H. 2017a. Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding, 158: 1-16 [DOI: 10.1016/j.cviu.2016.12.009]

Ma K D, Duanmu Z F, Wang Z, Wu Q B, Liu W T, Yong H W, Li H L and Zhang L. 2020a. Group maximum differentiation competition: model comparison with few samples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4): 851-864 [DOI: 10.1109/TPAMI.2018.2889948]

Ma K D, Duanmu Z F, Wu Q B, Wang Z, Yong H W, Li H L and Zhang L. 2017b. Waterloo exploration database: new challenges for image quality assessment models. IEEE Transactions on Image Processing, 26(2): 1004-1016 [DOI: 10.1109/TIP.2016.2631888]

Ma K D, Liu W T, Liu T L, Wang Z and Tao D C. 2017c. dipIQ: blind image quality assessment by learning-to-rank discriminable image pairs. IEEE Transactions on Image Processing, 26(8): 3951-3964 [DOI: 10.1109/TIP.2017.2708503]

Ma K D, Duanmu Z F, Yeganeh H and Wang Z. 2018a. Multi-exposure image fusion by optimizing a structural similarity index. IEEE Transactions on Computational Imaging, 4(1): 60-72 [DOI: 10.1109/TIP.2017.2786138]

Ma K D, Duanmu Z F, Zhu H W, Fang Y M and Wang Z. 2020b. Deep guided learning for fast multi-exposure image fusion. IEEE Transactions on Image Processing, 29: 2808-2819 [DOI: 10.1109/TIP.2019.2952716]

Ma K D, Liu W T and Wang Z. 2015a. Perceptual evaluation of single image dehazing algorithms//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, Canada: IEEE: 3600-3604 [ DOI: 10.1109/ICIP.2015.7351475 http://dx.doi.org/10.1109/ICIP.2015.7351475 ]

Ma K D, Liu W T, Zhang K, Duanmu Z F, Wang Z and Zuo W M. 2018b. End-to-end blind image quality assessment using deep neural networks. IEEE Transactions on Image Processing, 27(3): 1202-1213 [DOI: 10.1109/TIP.2017.2774045]

Ma K D, Liu X L, Fang Y M and Simoncelli E P. 2019. Blind image quality assessment by learning from multiple annotators//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 2344-2348 [ DOI: 10.1109/ICIP.2019.8803390 http://dx.doi.org/10.1109/ICIP.2019.8803390 ]

Ma K D, Yeganeh H,Zeng K and Wang Z. 2015b. High dynamic range image compression by optimizing tone mapped image quality index. IEEE Transactions on Image Processing, 24(10): 3086-3097 [DOI: 10.1109/TIP.2015.2436340]

Ma K D, Zeng K and Wang Z. 2015c. Perceptual quality assessment for multi-exposure image fusion. IEEE Transactions on Image Processing, 24(11): 3345-3356 [DOI: 10.1109/TIP.2015.2442920]

Martin D, Fowlkes C, Tal D and Malik J. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics//Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada: IEEE: 416-423 [ DOI: 10.1109/ICCV.2001.937655 http://dx.doi.org/10.1109/ICCV.2001.937655 ]

Martinez J, Pistonesi S, Maciel M C and Flesia A G. 2019. Multi-scale fidelity measure for image fusion quality assessment. Information Fusion, 50: 197-211 [DOI: 10.1016/j.inffus.2019.01.003]

Marziliano P, Dufaux F, Winkler S and Ebrahimi T. 2003. Perceptual blur and ring metrics: application to JPEG2000. Single Processing: Image Communication, 19(2): 163-172 [DOI: 10.1016/j.image.2003.08.003]

Meng C L, An P, Huang X P, Yang C and Liu D Y. 2020. Full reference light field image quality evaluation based on angular-spatial characteristic. IEEE Signal Processing Letters, 27: 525-529 [DOI: 10.1109/LSP.2020.2982060]

Min X K, Zhai G T, Gu K, Yang X K and Guan X P. 2019a. Objective quality evaluation of dehazed images. IEEE Transactions on Intelligent Transportation Systems, 20(8): 2879-2892 [DOI: 10.1109/TITS.2018.2868771]

Min X K, Zhai G T, Gu K, Zhu Y C, Zhou J T, Guo G D, Yang X K, Guan X P and Zhang W J.2019b. Quality evaluation of image dehazing methods using synthetic hazy images. IEEE Transactions on Multimedia, 21(9): 2319-2333 [DOI: 10.1109/TMM.2019.2902097]

Min X K, Zhou J T, Zhai G T, Le Callet P, Yang X K and Guan X P. 2020. A metric for light field reconstruction, compression, and display quality evaluation. IEEE Transactions on Image Processing, 29: 3790-3804 [DOI: 10.1109/TIP.2020.2966081]

Mittal A, Moorthy A K and Bovik A C. 2012. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12): 4695-4708 [DOI: 10.1109/TIP.2012.2214050]

Mittal A, Soundararajan R and Bovik A C. 2013. Making a "completely blind" image quality analyzer. IEEE Signal Processing Letters, 20(3): 209-212 [DOI: 10.1109/LSP.2012.2227726]

Moorthy A K, Su C C, Mittal A and Bovik A C. 2013. Subjective evaluation of stereoscopic image quality. Signal Processing: Image Communication, 28(8): 870-883 [DOI: 10.1016/j.image.2012.08.004]

Nafchi H Z, Shahkolaei A, Moghaddam R F and Cheriet M. 2015. FSITM: a feature similarity index for tone-mapped images. IEEE Signal Processing Letters, 22(8): 1026-1029 [DOI: 10.1109/LSP.2014.2381458]

Nah S, Kim T H and Lee K M. 2017. Deep multi-scale convolutional neural network for dynamic scene deblurring//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 257-265 [ DOI: 10.1109/CVPR.2017.35 http://dx.doi.org/10.1109/CVPR.2017.35 ]

Narvekar N D and Karam L J. 2011. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing. 20(9): 2678-2683 [ DOI: 10.1109/TIP.2011.2131660 http://dx.doi.org/10.1109/TIP.2011.2131660 ]

Nasrinpour H R and Bruce N D B. 2015. Saliency weighted quality assessment of tone-mapped images//Proceedings of 2016 IEEE International Conference on Image Processing. Quebec City, Canada: IEEE: 4799-8339 [ DOI: 10.1109/ICIP.2015.7351748 http://dx.doi.org/10.1109/ICIP.2015.7351748 ]

Ni Z K, Ma L, Zeng H Q, Cai C H and Ma K K. 2016. Gradient direction for screen content image quality assessment. IEEE Signal Processing Letters, 23(10): 1394-1398 [DOI: 10.1109/LSP.2016.2599294]

Ni Z K, Ma L, Zeng H Q, Chen J, Cai C H and Ma K K. 2017. ESIM: edge similarity for screen content image quality assessment. IEEE Transactions on Image Processing, 26(10): 4818-4831 [DOI: 10.1109/TIP.2017.2718185]

Ni Z K, Zeng H Q, Ma L, Hou J H, Chen J and Ma K K. 2018. A Gabor feature-based quality assessment model for the screen content images. IEEE Transactions on Image Processing, 27(9): 4516-4528 [DOI: 10.1109/TIP.2018.2839890]

Oh H, Ahn S, Kim J and Lee S. 2017. Blind deep S3D image quality evaluation via local to global feature aggregation. IEEE Transactions on Image Processing, 26(10): 4923-4936 [DOI: 10.1109/TIP.2017.2725584]

Ou F Z, Wang Y G, Li Jin, Zhu G P and Kwong S. 2019. Controllable list-wise ranking for universal no-reference image quality assessment[EB/OL]. [2020-11-24] . https://arxiv.org/pdf/1911.10566.pdf https://arxiv.org/pdf/1911.10566.pdf

Pan D, Shi P, Hou M, Ying Z F, Fu S Z and Zhang Y. 2018. Blind predicting similar quality map for image quality assessment//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 6373-6382 [ DOI: 10.1109/CVPR.2018.00667 http://dx.doi.org/10.1109/CVPR.2018.00667 ]

Paudyal P, Battisti F, Sjöström M, Olsson R and Carli M. 2017. Towards the perceptual quality evaluation of compressed light field images. IEEE Transactions on Broadcasting, 63(3): 507-522 [DOI: 10.1109/TBC.2017.2704430]

Ponomarenko N, Jin L N, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F and Kuo C C J. 2015. Image database TID2013: peculiarities, results and perspectives. Signal Processing: Image Communication, 30: 57-77 [DOI: 10.1016/j.image.2014.10.009]

Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M and Battisti F. 2009. TID2008——a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 10(4): 30-45

Qureshi M A, Beghdadi A and Deriche M. 2017. Towards the design of a consistent image contrast enhancement evaluation measure. Signal Processing: Image Communication, 58: 212-227 [DOI: 10.1016/j.image.2017.08.004]

Rahman H, Soundararajan R and Babu R V. 2017. Evaluating multiexposure fusion using image information. IEEE Signal Processing Letters, 24(11): 1671-1675 [DOI: 10.1109/LSP.2017.2752233]

Ravuri C S, Sureddi R, Dendi S V R, Raman S and Channappaya S S. 2019. Deep no-reference tone mapped image quality assessment//Proceedings of 2019 Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, USA: IEEE: 7281-4301 [ DOI: 10.1109/IEEECONF44664.2019.9048677 http://dx.doi.org/10.1109/IEEECONF44664.2019.9048677 ]

Rehman A and Wang Z. 2012. Reduced-reference image quality assessment by structural similarity estimation. IEEE Transactions on Image Processing, 21(8): 3378-3389 [DOI: 10.1109/TIP.2012.2197011]

Reinhard E, Ward G, Pattanaik S and Debevec P. 2010. High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting. Amsterdam: Elsevier

Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241 [ DOI: 10.1007/978-3-319-24574-428 http://dx.doi.org/10.1007/978-3-319-24574-428 ]

Sandić-StankovićD, Kukolj D and Le Callet P. 2015a. DIBR synthesized image quality assessment based on morphological wavelets//Proceedings of the 7th International Workshop on Quality of Multimedia Experience. Pilos, Greece: IEEE: 1-6 [ DOI: 10.1109/QoMEX.2015.7148143 http://dx.doi.org/10.1109/QoMEX.2015.7148143 ]

Sandić-StankovićD, Kukolj D and Le Callet P. 2015b. DIBR synthesized image quality assessment based on morphological pyramids//Proceedings of 2015 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video. Lisbon, Portugal: IEEE: 1-4 [ DOI: 10.1109/3DTV.2015.7169368 http://dx.doi.org/10.1109/3DTV.2015.7169368 ]

Sandić-Stanković D, Kukolj D and Le Callet P. 2016. Multi-scale synthesized view assessment based on morphological pyramids. Journal of Electrical Engineering, 67(1): 3-11 [DOI: 10.1515/jee-2016-0001]

Sang Q, Qi H, Wu X, Li C and Bovik A C. 2014. No-reference image blur index based on singular value curve. Journal of Visual Communication and Image Representation, 25(7): 1625-1630 [DOI: 10.1016/j.jvcir.2014.08.002]

Shao F, Gao Y, Jiang Q P, Jiang G Y and Ho Y S. 2018. Multistage pooling for blind quality prediction of asymmetric multiply-distorted stereoscopic images. IEEE Transactions on Multimedia, 20(10): 2605-2619 [DOI: 10.1109/TMM.2018.2817072]

Shao F, Tian W J, Lin W S, Jiang G Y and Dai Q H. 2017. Learning sparse representation for no-reference quality assessment of multiply distorted stereoscopic images. IEEE Transactions on Multimedia, 19(8): 1821-1836 [DOI: 10.1109/TMM.2017.2685240]

Sheikh H R, Wang Z, Cormack L and Bovik A C. 2006a. LIVE image quality assessment database release 2 [DB/OL]. [2020-07-08] . https://live.ece.utexas.edu/research/quality/ https://live.ece.utexas.edu/research/quality/

Sheikh H R and Bovik A C. 2006b. Image information and visual quality. Transactions on Image Processing, 15(2): 430-444 [DOI: 10.1109/TIP.2005.859378]

Shi G M, Wan W F, Wu J J, Xie X M, Dong W S and Wu H R. 2019a. SISRSet: single image super-resolution subjective evaluation test and objective quality assessment. Neurocomputing, 360: 37-51 [DOI: 10.1016/j.neucom.2019.06.027]

Shi L K, Zhao S Y and Chen Z B. 2019b. Belif: blind quality evaluator of light field image with tensor structure variation index//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 3781-3785 [ DOI: 10.1109/ICIP.2019.8803559 http://dx.doi.org/10.1109/ICIP.2019.8803559 ]

Shi S, Zhang X, Wang S Q, Xiong R Q and Ma S W. 2015. Study on subjective quality assessment of screen content images//Proceedings of the 2015 Picture Coding Symposium. Cairns, Australia: IEEE: 75-79 [ DOI: 10.1109/PCS.2015.7170050 http://dx.doi.org/10.1109/PCS.2015.7170050 ]

Siahaan E, Hanjalic A and Redi J A. 2018. Semantic-aware blind image quality assessment. Signal Processing: Image Communication, 60: 237-252 [DOI: 10.1016/j.image.2017.10.009]

Sim K, Yang J C, Lu W and Gao X B. 2021. Blind stereoscopic image quality evaluator based on binocular semantic and quality channels. IEEE Transactions on Multimedia, 24: 1389-1398 [DOI: 10.1109/TMM.2021.3064240]

Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition//Proceedings of the 3rd International Conference on Learning Representations. San Diego, USA: [s. n.]: 1-14

Song R, Ko H and Jay Kuo C C. 2015. MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source. Journal of Information Science and Engineering, 31: 1593-1611

Su S L, Yan Q S, Zhu Y, Zhang C, Ge X, Sun J Q and Zhang Y N. 2020. Blindly assess image quality in the wild guided by a self-adaptive hyper network//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3664-3673 [ DOI: 10.1109/CVPR42600.2020.00372 http://dx.doi.org/10.1109/CVPR42600.2020.00372 ]

Su Y C and Korhonen J. 2020. Blind natural image quality prediction using convolutional neural networks and weighted spatial pooling//Proceedings of 2020 IEEE International Conference on Image Processing. Abu Dhabi, United Arab:IEEE: 191-195 [ DOI: 10.1109/ICIP40778.2020.9190789 http://dx.doi.org/10.1109/ICIP40778.2020.9190789 ]

Sui X J, Ma K D, Yao Y R and Fang Y M. 2021. Perceptual quality assessment of omnidirectional images as moving camera videos. IEEE Transactions on Visualization and Computer Graphics: 1-11 [ DOI: 10.1109/TVCG.2021.3050888 http://dx.doi.org/10.1109/TVCG.2021.3050888 ]

Sun S M, Yu T, Xu J H, Zhou W and Chen Z B. 2021a. GraphIQA: learning distortion graph representations for blind image quality assessment. Journal of Latex Class Files, 14(8): 1-13

Sun W, Gu K, Ma S W, Zhu W H, Liu N and Zhai G T. 2018. A large-scale compressed 360-degree spherical image database: from subjective quality evaluation to objective model comparison//Proceedings ofthe 20th IEEE International Workshop on Multimedia Signal Processing. Vancouver, Canada: IEEE: 1-6 [ DOI: 10.1109/MMSP.2018.8547102 http://dx.doi.org/10.1109/MMSP.2018.8547102 ]

Sun W, Liao Q, X J H and Zhou F. 2018. SPSIM: a superpixel-based similarity index for full-reference image quality assessment. IEEE Transactions on Image Processing, 27(9): 4232-4244 [DOI: 10.1109/TIP.2018.2837341]

Sun W, Min X K, Ma S W and Zhai G T. 2021b. Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training [DB/OL]. [2021-05-30] . https://arxiv.org/pdf/2105.14550.pdf https://arxiv.org/pdf/2105.14550.pdf

Sun W, Min X K, Zhai G T, Gu K, Duan H Y and Ma S W. 2020. MC360IQA: a multi-channel CNN for blind 360-degree image quality assessment. IEEE Journal of Selected Topics in Signal Processing, 14(1): 64-77 [DOI: 10.1109/JSTSP.2019.2955024]

Talebi H and Milanfar P. 2018. NIMA: neural image assessment. IEEE Transactions on Image Processing, 27(8): 3998-4011 [DOI: 10.1109/TIP.2018.2831899]

Tang L, Sun K, Liu L, Wang G and Liu Y. 2019. A reduced-reference quality assessment metric for super-resolution reconstructed images with information gain and texture similarity. Signal Processing: Image Communication, 79: 32-39 [DOI: 10.1016/j.image.2019.08.004]

Tarel J P, Hautiere N, Caraffa L, Cord A, Halmaoui H and Gruyer D. 2012. Vision enhancement in homogeneous and heterogeneous fog. IEEE Intelligent Transportation Systems Magazine, 4(2): 6-20 [DOI: 10.1109/MITS.2012.2189969]

Tarel J P, Hautière N, Cord A, Gruyer D and Halmaoui H. 2010. Improved visibility of road scene images under heterogeneous fog//Proceedings of 2010 IEEE Intelligent Vehicles Symposium. La Jolla, USA: IEEE: 478-485 [ DOI: 10.1109/IVS.2010.5548128 http://dx.doi.org/10.1109/IVS.2010.5548128 ]

Tian S S, Zhang L, Morin L and Déforges O. 2019. A benchmark of DIBR synthesized view quality assessment metrics on a new database for immersive media applications. IEEE Transactions on Multimedia, 21(5): 1235-1247 [DOI: 10.1109/TMM.2018.2875307]

Tian Y, Zeng H Q, Hou J H, Chen J and Ma K K. 2020. Light field image quality assessment via the light field coherence. IEEE Transactions on Image Processing, 29: 7945-7956 [DOI: 10.1109/TIP.2020.3008856]

Tursun O T, Akyüz A O, Erdem A and Erdem E. 2015. The state of the art in HDR deghosting: a survey and evaluation. Computer Graphics Forum, 34(2): 683-707 [DOI: 10.1111/cgf.12593]

Varga D, Saupe D and Szirányi T. 2018. Deeprn: a content preserving deep architecture for blind image quality assessment//Proceedings of 2018 IEEE International Conference on Multimedia and Expo. San Diego, USA: IEEE: 1-6. [ DOI: 10.1109/ICME.2018.8486528 http://dx.doi.org/10.1109/ICME.2018.8486528 ]

Viola I and Ebrahimi T. 2018. VALID: visual quality assessment for light field images dataset//Proceedings of the 10th International Conference on Quality of Multimedia Experience. Cagliari, Italy: IEEE: 1-3 [ DOI: 10.1109/QoMEX.2018.8463388 http://dx.doi.org/10.1109/QoMEX.2018.8463388 ]

Virtanen T, Nuutinen M, Vaahteranoksa M, Oittinen P and Häkkinen J. 2015. CID2013: a database for evaluating no-reference image quality assessment algorithms. IEEE Transactions on Image Processing, 24(1): 390-402 [DOI: 10.1109/TIP.2014.2378061]

Wang G C, Li L D, Li Q H, Gu K, Lu Z L and Qian J S. 2017a. Perceptual evaluation of single-image super-resolution reconstruction//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 3145-3149 [ DOI: 10.1109/ICIP.2017.8296862 http://dx.doi.org/10.1109/ICIP.2017.8296862 ]

Wang G C, Wang Z Y, Gu K, Li L D, Xia Z F and Wu L F. 2020a. Blind quality metric of DIBR-synthesized images in the discrete wavelet transform domain. IEEE Transactions on Image Processing, 29: 1802-1814 [DOI: 10.1109/TIP.2019.2945675]

Wang H T, Chen T L, Wang Z Y and Ma K D. 2020b. I am going MAD: maximum discrepancy competition for comparing classifiers adaptively [EB/OL]. [2020-02-25] . https://arxiv.org/pdf/2002.10648.pdf https://arxiv.org/pdf/2002.10648.pdf

Wang JH, Rehman A, Zeng K, Wang S Q and Wang Z. 2015. Quality prediction of asymmetrically distorted stereoscopic 3D images. IEEE Transactions on Image Processing, 24(11): 3400-3414 [DOI: 10.1109/TIP.2015.2446942]

Wang J H, Wang S Q, Ma K D and Wang Z. 2017b. Perceptual depth quality in distorted stereoscopic images. IEEE Transactions on Image Processing, 26(3): 1202-1215 [DOI: 10.1109/TIP.2016.2642791]

Wang S Q, Gu K, Zeng K, Wang Z and Lin W S. 2018a. Objective quality assessment and perceptual compression of screen content images. IEEE Computer Graphics and Applications, 38(1): 47-58 [DOI: 10.1109/MCG.2016.46]

Wang S Q, Gu K, Zhang X, Lin W S, Zhang L, Ma S W and Gao W. 2016. Subjective and objective quality assessment of compressed screen content images. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 6(4): 532-543 [DOI: 10.1109/JETCAS.2016.2598756]

Wang S Q, Gu K, Zhang X F, Lin W S, Ma S W and Gao W. 2018b. Reduced-reference quality assessment of screen content images. IEEE Transactions on Circuits and Systems for Video Technology, 28(1): 1-14 [DOI: 10.1109/TCSVT.2016.2602764]

Wang X, Cao L, Zhu Y Y, Zhang Y, Jiang J M and Kwong S. 2017c. Study of subjective and objective quality assessment for screen content images//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 750-754 [ DOI: 10.1109/ICIP.2017.8296381 http://dx.doi.org/10.1109/ICIP.2017.8296381 ]

Wang Z and Bovik A C. 2011b. Reduced-and no-reference image quality assessment. IEEE Signal Processing Magazine, 28(6): 29-40 [DOI: 10.1109/MSP.2011.942471]

Wang Z, Bovik A C, Sheikh H R and Simoncelli E P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612 [DOI: 10.1109/TIP.2003.819861]

Wang Z and Li Q. 2011a. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 20(5): 1185-1198 [DOI: 10.1109/TIP.2010.2092435]

Wang Z and Simoncelli E P. 2008. Maximum differentiation (MAD) competition: a methodology for comparing computational models of perceptual quantities. Journal of Vision, 8(12): #8 [DOI: 10.1167/8.12.8]

Wang Z H, Li D Q and Ma K D. 2021b. Semi-supervised deep ensembles for blind image quality assessment [EB/OL]. [2021-06-26] . https://arxiv.org/pdf/2106.14008.pdf https://arxiv.org/pdf/2106.14008.pdf

Wang Z H, Tang Z R, Zhang J G and Fang Y M. 2021a. Learning from synthetic data for opinion-free blind image quality assessment in the wild[EB/OL]. [2021-06-26] . https://arxiv.org/pdf/2106.14076.pdf https://arxiv.org/pdf/2106.14076.pdf

Wu J J. 2014. Image Information Perception and Quality Assessment Based on the Human Visual System. Xi′an: Xidian University

吴金建. 2014. 基于人类视觉系统的图像信息感知和图像质量评价. 西安: 西安电子科技大学

Wu J J, Lin W S, Shi G M and Liu A M. 2013a. Perceptual quality metric with internal generative mechanism. IEEE Transactions on Image Processing, 22(1): 43-54 [DOI: 10.1109/TIP.2012.2214048]

Wu J J, Lin W S, Shi G M and Liu A M. 2013b. Reduced-reference Image quality assessment with visual information fidelity. IEEE Transactions on Multimedia, 15(7): 1700-1705 [DOI: 10.1109/TMM.2013.2266093]

Wu J J, Ma J P, Liang F H, Dong W S, Shi G M and Lin W S. 2020a. End-to-end blind image quality prediction with cascaded deep neural network. IEEE Transactions on Image Processing, 29: 7414-7426 [DOI: 10.1109/TIP.2020.3002478]

Wu J J, Zhang M, Li L D, Dong W S, Shi G M and Lin W S. 2019a. No-reference image quality assessment with visual pattern degradation. Information Sciences, 504: 487-500 [DOI: 10.1016/j.ins.2019.07.061]

Wu Q B, Li H L, Meng F M and Ngan K N. 2018. A perceptually weighted rank correlation indicator for objective image quality assessment. IEEE Transactions on Image Processing, 27(5): 2499-2513 [DOI: 10.1109/TIP.2018.2799331]

Wu Q B, Wang L, Ngan K N, Li H L, Meng F M and Xu L F. 2020b. Subjective and objective de-raining quality assessment towards authentic rain image. IEEE Transactions on Circuits and Systems for Video Technology, 30(11): 3883-3897 [DOI: 10.1109/TCSVT.2020.2972566]

Wu Q, Wang L, Ngan K N, Li H and Meng F. 2019b. Beyond synthetic data: a blind deraining quality assessment metric towards authentic rain image//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 2364-2368 [ DOI: 10.1109/ICIP.2019.8803329 http://dx.doi.org/10.1109/ICIP.2019.8803329 ]

Xing L, Cai L, Zeng H, Chen J, Zhu J and Hou J. 2017. A multi-scale contrast-based image quality assessment model for multi-exposure image fusion. Signal Processing, 145: 233-240 [DOI: 10.1016/j.sigpro.2017.12.013]

Xu J H, Lin C Y, Zhou W and Chen Z B. 2018. Subjective quality assessment of stereoscopic omnidirectional image//Proceedings of the 19th Pacific-Rim Conference on Multimedia. Hefei, China: Springer: 589-599 [ DOI: 10.1007/978-3-030-00776-8_54 http://dx.doi.org/10.1007/978-3-030-00776-8_54 ]

Xu J H, Zhou W and Chen Z B. 2020b. Blind omnidirectional image quality assessment with viewport oriented graph convolutional networks [EB/OL]. [2020-07-08] . https://arxiv.org/pdf/2002.09140v1.pdf https://arxiv.org/pdf/2002.09140v1.pdf

Xu J H, Zhou W, Chen Z B, Ling S Y and Le Callet P. 2021. Binocular rivalry oriented predictive autoencoding network for blind stereoscopic image quality measurement. IEEE Transactions on Instrumentation and Measurement, 70: #5001413 [DOI: 10.1109/TIM.2020.3026443]

Xu J T, Ye P, Li Q H, Du H Q, Liu Y and Doermann D. 2016. Blind image quality assessment based on high order statistics aggregation. IEEE Transactions on Image Processing, 25(9): 4444-4457 [DOI: 10.1109/TIP.2016.2585880]

Xu M, Li C, Zhang S Y and Le Callet P. 2020a. State-of-the-art in 360° video/image processing: perception, assessment and compression. IEEE Journal of Selected Topics in Signal Processing, 14(1): 5-26 [DOI: 10.1109/JSTSP.2020.2966864]

Xue W F, Mou X Q, Zhang L, Bovik A C and Feng X C. 2014a. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, 23(11): 4850-4862 [DOI: 10.1109/TIP.2014.2355716]

Xue W F, Zhang L and Mou X Q. 2013. Learning without human scores for blind image quality assessment//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: 995-1002 [ DOI: 10.1109/CVPR.2013.133 http://dx.doi.org/10.1109/CVPR.2013.133 ]

Xue W F, Zhang L, Mou X Q and Bovik A C. 2014b. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Transactions on Image Processing, 23(2): 684-695 [DOI: 10.1109/TIP.2013.2293423]

Yan B, Bare B, Ma C X, Li K and Tan W M. 2019b. Deep objective quality assessment driven single image super-resolution. IEEE Transactions on Multimedia, 21(11): 2957-2971 [DOI: 10.1109/TMM.2019.2914883]

Yan B, Bare B and Tan W M.2019a. Naturalness-aware deep no-reference image quality assessment. IEEE Transactions on Multimedia, 21(10): 2603-2615 [DOI: 10.1109/TMM.2019.2904879]

Yan J B. 2018. No-Reference Stereoscopic Image Quality Assessment Based on Binocular Visual Perception. Nanchang: Jiangxi University of Finance and Economics

鄢杰斌. 2018. 基于双目视觉感知的无参考3维图像视觉质量评价. 南昌: 江西财经大学

Yan J B, Fang Y M, Du R G, Zeng Y and Zuo Y F. 2020a. No reference quality assessment for 3D synthesized views by local structure variation and global naturalness change. IEEE Transactions on Image Processing, 29: 7443-7453 [DOI: 10.1109/TIP.2020.3003218]

Yan J B, Fang Y M, Huang L P, Min X K, Yao Y R andZhai G T. 2020b. Blind stereoscopic image quality assessment by deep neural network of multi-level feature fusion//Proceedings of 2020 IEEE International Conference on Multimedia and Expo. London, UK: IEEE: 1-6 [ DOI: 10.1109/ICME46284.2020.9102888 http://dx.doi.org/10.1109/ICME46284.2020.9102888 ]

Yan J B, Zhong Y, Fang Y M, Wang Z Y and Ma K D. 2021. Exposing semantic segmentation failures via maximum discrepancy competition. International Journal of Computer Vision, 129(5): 1768-1786 [DOI: 10.1007/s11263-021-01450-2]

Yang C Y, Ma C and Yang M H. 2014. Single-image super-resolution: a benchmark//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer: 372-386 [ DOI: 10.1007/978-3-319-10593-2_25 http://dx.doi.org/10.1007/978-3-319-10593-2_25 ]

Yang H, Fang Y M and Lin W S. 2015. Perceptual quality assessment of screen content images. IEEE Transactions on Image Processing, 24(11): 4408-4421 [DOI: 10.1109/TIP.2015.2465145]

Yang S, Jiang Q P, Lin W S and Wang Y T. 2019a. SGDNet: an end-to-end saliency-guided deep neural network for no-reference image quality assessment//Proceedings of the 27th ACM International Conference on Multimedia. Nice, France: ACM: 1383-1391 [ DOI: 10.1145/3343031.3350990 http://dx.doi.org/10.1145/3343031.3350990 ]

Yang W H, Tan R T, Feng J S, Liu J Y, Guo Z M and Yan S C. 2017. Deep joint rain detection and removal from a single image//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 1685-1694 [ DOI: 10.1109/CVPR.2017.183 http://dx.doi.org/10.1109/CVPR.2017.183 ]

Yang W H, Tan R T, Wang S Q, Fang Y M and Liu J Y. 2021a. Single image deraining: from model-based to data-driven and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11): 4059-4077 [DOI: 10.1109/TPAMI.2020.2995190]

Yang W H, Wang W J, Huang H F, Wang S Q and Liu J Y. 2021b. Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Transactions on Image Processing, 30: 2072-2086 [DOI: 10.1109/TIP.2021.3050850]

Yang W M, Zhang X C, Tian Y P, Wang W, Xue J H and Liao Q M. 2019b. Deep learning for single image super-resolution: a brief review. IEEE Transactions on Multimedia, 21(12): 3106-3121 [DOI: 10.1109/TMM.2019.2919431]

Ye P, Kumar J, Kang L and Doermann D. 2012. Unsupervised feature learning framework for no-reference image quality assessment//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE: 1098-1105 [ DOI: 10.1109/CVPR.2012.6247789 http://dx.doi.org/10.1109/CVPR.2012.6247789 ]

Ye P, Kumar J and Doermann D. 2014. Beyond human opinion scores: blind image quality assessment based on synthetic scores//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 1-8 [ DOI: 10.1109/CVPR.2014.540 http://dx.doi.org/10.1109/CVPR.2014.540 ]

Yeganeh H, Rostami M and Wang Z. 2015. Objective quality assessment of interpolated natural images. IEEE Transactions on Image Processing, 24(11): 4651-4663 [DOI: 10.1109/TIP.2015.2456638]

Yeganeh H and Wang Z. 2013. Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing, 22(2): 657-667 [DOI: 10.1109/TIP.2012.2221725]

Ying Z Q, Niu H R, Gupta P, Mahajan D, Ghadiyaram D and Bovik A. 2020. From patches to pictures (PaQ-2-PiQ): mapping the perceptual space of picture quality//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3572-3582 [ DOI: 10.1109/CVPR42600.2020.00363 http://dx.doi.org/10.1109/CVPR42600.2020.00363 ]

You J Y and Korhonen J. 2021. Transformer for image quality assessment[EB/OL]. [2020-12-30] . https://arxiv.org/vc/arxiv/papers/2101/2101.01097v1.pdf https://arxiv.org/vc/arxiv/papers/2101/2101.01097v1.pdf

Yu F, Seff A, Zhang Y D, Song S R, Funkhouser T and Xiao J X. 2015. LSUN: construction of a large-scale image dataset using deep learning with humans in the loop [EB/OL]. [2020-06-10] . https://arxiv.org/pdf/1506.03365.pdf https://arxiv.org/pdf/1506.03365.pdf

Yue G, Yan W and Zhou T. 2019a. Referenceless quality evaluation of tone-mapped HDR and multi-exposure fused images. IEEE Transactions on Industrial Informatics, 16(3): 1764-1775 [DOI: 10.1109/TII.2019.2927527]

Yue G, Hou C and Zhou T. 2019b. Bling quality assessment of tone-mapped images considering colorfulness, naturalness, and structure. IEEE Transactions on Industrial Electronics, 66(5): 3784-2793 [DOI: 10.1109/TIE.2018.2851984]

Zeng H, Zhang L and Bovik A C. 2018. Blind image quality assessment with a probabilistic quality representation//Proceedings of the 25th IEEE International Conference on Image Processing. Athens, Greece: IEEE: 609-613 [ DOI: 10.1109/ICIP.2018.8451285 http://dx.doi.org/10.1109/ICIP.2018.8451285 ]

Zeng K and Wang Z. 2013. Perceptual evaluation of image denoising algorithms//Proceedings of 2013 Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE: 1351-1355 [ DOI: 10.1109/ACSSC.2013.6810514 http://dx.doi.org/10.1109/ACSSC.2013.6810514 ]

Zeyde R, Elad M and Protter M. 2010. On single image scale-up using sparse-representations//Proceedings of the 7th International Conference on Curves and Surfaces. Avignon: Springer: 711-730 [ DOI: 10.1007/978-3-642-27413-8_47 http://dx.doi.org/10.1007/978-3-642-27413-8_47 ]

Zhai G T and Min X K. 2020. Perceptual image quality assessment: a survey. Science China Information Sciences, 63(11): #211301 [DOI: 10.1007/s11432-019-2757-1]

Zhang C, Cheng W and Hirakawa K. 2019a. Corrupted reference image quality assessment of denoised images. IEEE Transactions on Image Processing, 28(4): 1732-1747 [DOI: 10.1109/TIP.2018.2878326]

Zhang K, Zhu D, Jing J and Gao X. 2019b. Learning a cascade regression for no-reference super-resolution image quality assessment//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 5386-6250 [ DOI: 10.1109/ICIP.2019.8802967 http://dx.doi.org/10.1109/ICIP.2019.8802967 ]

Zhang L, Shen Y and Li H Y. 2014. VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10): 4270-4281 [DOI: 10.1109/TIP.2014.2346028]

Zhang L, Zhang L and Bovik A C. 2015. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8): 2579-2591 [DOI: 10.1109/TIP.2015.2426416]

Zhang L, Zhang L, Mou X Q and Zhang D. 2011. FSIM: a feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8): 2378-2386 [DOI: 10.1109/TIP.2011.2109730]

Zhang W X, Li D Q, Ma C, Zhai G T, Yang X K and Ma K D. 2021b. Continual learning for blind image quality assessment[EB/OL]. [2021-02-19] . https://arxiv.org/pdf/2102.09717.pdf https://arxiv.org/pdf/2102.09717.pdf

Zhang W X, Ma K D, Yan J, Deng D X and Wang Z. 2020a. Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Transactions on Circuits and Systems for Video Technology, 30(1): 36-47 [DOI: 10.1109/TCSVT.2018.2886771]

Zhang W X, Ma K D, Zhai G T and Yang X K. 2020b. Learning to blindly assess image quality in the laboratory and wild//Proceedings of 2020 IEEE International Conference on Image Processing. Abu Dhabi, United Arab: IEEE: 111-115 [ DOI: 10.1109/ICIP40778.2020.9191278 http://dx.doi.org/10.1109/ICIP40778.2020.9191278 ]

Zhang W X, Ma K D, Zhai G T and Yang X K. 2021a. Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Transactions on Image Processing, 30: 3474-3486[DOI: 10.1109/TIP.2021.3061932]

Zhao S Y, Zhang L, Huang S Y, Shen Y and Zhao S J. 2020a. Dehazing evaluation: real-world benchmark datasets, criteria, and baselines. IEEE Transactions on Image Processing, 29: 6947-6962 [DOI: 10.1109/TIP.2020.2995264]

Zhao T S, Lin Y T, Xu Y W, Chen W L and Wang Z. 2020b. Learning-based quality assessment for image super-resolution[EB/OL]. [2020-12-16] . https://arxiv.org/pdf/2012.08732.pdf https://arxiv.org/pdf/2012.08732.pdf

Zheng L R, Shen L Q, Chen J N, An P and Luo J. 2019. No-reference quality assessment for screen content images based on hybrid region features fusion. IEEE Transactions on Multimedia, 21(8): 2057-2070 [DOI: 10.1109/TMM.2019.2894939]

Zhou F, Yao R G, Liu B Z and Qiu G P. 2019a. Visual quality assessment for super-resolved images: database and method. IEEE Transactions on Image Processing, 28(7): 3528-3541 [DOI: 10.1109/TIP.2019.2898638]

Zhou J M, Jiang G Y, Mao X Y, Yu M, Shao F, Peng Z J and Zhang Y. 2011. Subjective quality analyses of stereoscopic images in 3DTV system//Proceedings of 2011 Visual Communications and Image Processing. Tainan, China: IEEE: 1-4 [ DOI: 10.1109/VCIP.2011.6115913 http://dx.doi.org/10.1109/VCIP.2011.6115913 ]

Zhou W, Chen Z B and Li W P. 2019b. Dual-stream interactive networks for no-reference stereoscopic image quality assessment. IEEE Transactions on Image Processing, 28(8): 3946-3958 [DOI: 10.1109/TIP.2019.2902831]

Zhou W, Jiang Q P, Wang Y W, Chen Z B and Li W P. 2020a. Blind quality assessment for image superresolution using deep two-stream convolutional networks. Information Sciences, 528: 205-218 [DOI: 10.1016/j.ins.2020.04.030]

Zhou W, Shi L K, Chen Z B and Zhang J L. 2020b. Tensor oriented no-reference light field image quality assessment. IEEE Transactions on Image Processing, 29: 4070-4084 [DOI: 10.1109/TIP.2020.2969777]

Zhou W, Xu J H, Jiang Q P and Chen Z B. 2021a. No-reference quality assessment for 360-degree images by analysis of multifrequency information and local-global naturalness. IEEE Transactions on Circuits and Systems for Video Technology [ DOI: 10.1109/TCSVT.2021.3081182 http://dx.doi.org/10.1109/TCSVT.2021.3081182 ]

Zhou Y. 2019. Study on the Objective Quality Assessment Metrics of Virtual View Synthesis. Xuzhou: China University of Mining and Technology

周玉. 2019. 面向虚拟视角合成的客观质量评价方法研究. 徐州: 中国矿业大学

Zhou Y, Li L D, Wang S Q, Wu J J, Fang Y M and Gao X B. 2019c. No-reference quality assessment for view synthesis using DoG-based edge statistics and texture naturalness. IEEE Transactions on Image Processing, 28(9): 4566-4579 [DOI: 10.1109/TIP.2019.2912463]

Zhou Y, Sun Y J, Li L D, Gu K and Fang Y M. 2021b. Omnidirectional image quality assessment by distortion discrimination assisted multi-stream network. IEEE Transactions on Circuits and Systems for Video Technology [ DOI: 10.1109/TCSVT.2021.3081162 http://dx.doi.org/10.1109/TCSVT.2021.3081162 ]

Zhu H C, Li L D, Wu J J, Dong W S and Shi G M. 2020. MetaIQA: deep meta-learning for no-reference image quality assessment//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 14131-14140 [ DOI: 10.1109/CVPR42600.2020.01415 http://dx.doi.org/10.1109/CVPR42600.2020.01415 ]

Zhu W H, Zhai G T, Min X K, Hu M H, Liu J, Guo G D and Yang X K. 2019. Multi-channel decomposition in tandem with free-energy principle for reduced-reference image quality assessment. IEEE Transactions on Multimedia, 21(9): 2334-2346 [DOI: 10.1109/TMM.2019.2902484]