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Multi-Task Rank Learning for Image Quality Assessment
Xu, Long1; Li, Jia2,3; Lin, Weisi4; Zhang, Yongbing5; Ma, Lin6; Fang, Yuming7; Yan, Yihua1
2017-09-01
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume27Issue:9Pages:1833-1843
AbstractIn practice, images are distorted by more than one distortion. For image quality assessment (IQA), existing machine learning (ML)-based methods generally establish a unified model for all the distortion types, or each model is trained independently for each distortion type, which is therefore distortion aware. In distortion-aware methods, the common features among different distortions are not exploited. In addition, there are fewer training samples for each model training task, which may result in overfitting. To address these problems, we propose a multi-task learning framework to train multiple IQA models together, where each model is for each distortion type; however, all the training samples are associated with each model training task. Thus, the common features among different distortion types and the said underlying relatedness among all the learning tasks are exploited, which would benefit the generalization ability of trained models and prevent overfitting possibly. In addition, pairwise image quality ranking instead of image quality rating is optimized in our learning task, which is fundamentally departed from traditional ML-based IQA methods toward better performance. The experimental results confirm that the proposed multi-task rank-learning-based IQA metric is prominent against all state-of-the-art nonreference IQA approaches.
SubtypeArticle
KeywordImage Quality Assessment (Iqa) Machine Learning (Ml) Mean Opinion Score (Mos) Pairwise Comparison Rank Learning
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation (NSFC) of China(61202242 ; National Natural Science Foundation (NSFC) of China(61202242 ; CAS ; CAS ; National Natural Science Foundation of China(61370113 ; National Natural Science Foundation of China(61370113 ; 61572461) ; 61572461) ; U1201255 ; U1201255 ; U1301257 ; U1301257 ; 61571212 ; 61571212 ; 11433006) ; 11433006) ; National Natural Science Foundation (NSFC) of China(61202242 ; National Natural Science Foundation (NSFC) of China(61202242 ; CAS ; CAS ; National Natural Science Foundation of China(61370113 ; National Natural Science Foundation of China(61370113 ; 61572461) ; 61572461) ; U1201255 ; U1201255 ; U1301257 ; U1301257 ; 61571212 ; 61571212 ; 11433006) ; 11433006)
DOI10.1109/TCSVT.2016.2543099
WOS KeywordVISUAL SALIENCY ESTIMATION ; NATURAL SCENE STATISTICS ; ALGORITHMS ; REGRESSION ; FRAMEWORK ; JPEG2000 ; DOMAIN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation (NSFC) of China(61202242 ; National Natural Science Foundation (NSFC) of China(61202242 ; CAS ; CAS ; National Natural Science Foundation of China(61370113 ; National Natural Science Foundation of China(61370113 ; 61572461) ; 61572461) ; U1201255 ; U1201255 ; U1301257 ; U1301257 ; 61571212 ; 61571212 ; 11433006) ; 11433006) ; National Natural Science Foundation (NSFC) of China(61202242 ; National Natural Science Foundation (NSFC) of China(61202242 ; CAS ; CAS ; National Natural Science Foundation of China(61370113 ; National Natural Science Foundation of China(61370113 ; 61572461) ; 61572461) ; U1201255 ; U1201255 ; U1301257 ; U1301257 ; 61571212 ; 61571212 ; 11433006) ; 11433006)
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000409531400001
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/20087
Collection太阳物理研究部
Affiliation1.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
2.Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
3.Beihang Univ, Int Res Inst Multidisciplinary Sci, Beijing 100191, Peoples R China
4.Nanyang Technol Univ, Dept Comp Engn, Singapore 639798, Singapore
5.Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518005, Peoples R China
6.Huawei Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China
7.Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330032, Jiangxi, Peoples R China
Recommended Citation
GB/T 7714
Xu, Long,Li, Jia,Lin, Weisi,et al. Multi-Task Rank Learning for Image Quality Assessment[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2017,27(9):1833-1843.
APA Xu, Long.,Li, Jia.,Lin, Weisi.,Zhang, Yongbing.,Ma, Lin.,...&Yan, Yihua.(2017).Multi-Task Rank Learning for Image Quality Assessment.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,27(9),1833-1843.
MLA Xu, Long,et al."Multi-Task Rank Learning for Image Quality Assessment".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 27.9(2017):1833-1843.
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