Pairwise comparison and rank learning for image quality assessment
文献类型：期刊论文
| 作者 | Xu, Long1 ; Li, Jia2; Lin, Weisi3; Zhang, Yun4; Zhang, Yongbing5; Yan, Yihua1
|
| 刊名 | DISPLAYS
 |
| 出版日期 | 2016-09-01 |
| 卷号 | 44页码:21-26 |
| 关键词 | Image quality assessment Machine learning Rank learning Pairwise comparison |
| 英文摘要 | To know what kinds of image features are crucial for image quality assessment (IQA) and how these features affect the human visual system (HVS) is still largely beyond human knowledge. Hence, machine learning (ML) is employed to build IQA by simulating the HVS behavior in IQA processes. Support vector machine/regression (SVM/SVR) is a major member of ML. It has been successfully applied to IQA recently. As to image quality rating, the human's opinion about it is not always reliable. In fact, the subjects cannot precisely rate the small difference of image quality in subjective testing, resulting in unreliable Mean Opinion Scores (MOSs). However, they can easily identify the better/worse one from two given images, even their qualities do not differ much. In this sense, the human's opinion on pairwise comparison (PC) of image quality is more reliable than image quality rating. Thus, PC has been exploited in developing IQA metrics. In this paper, a rank learning optimization framework is firstly developed to model IQA. Particularly, the PCs of image quality instead of numerical ratings are incorporated into the optimization framework. Then, a novel no-reference (NR)-IQA is proposed to infer image quality in terms of image quality ranks. By importing rank learning theory and PC into IQA, a fundamental and meaningful departure from the existing framework of IQA could be expected. The experimental results confirm that the proposed Pairwise Rank Learning based Image Quality Metric (PRLIQM) can achieve comparable performance over the state-of-the-art NR-IQA approaches. (C) 2016 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology ; Physical Sciences |
| 类目[WOS] | Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Optics |
| 研究领域[WOS] | Computer Science ; Engineering ; Instruments & Instrumentation ; Optics |
| 关键词[WOS] | NATURAL SCENE STATISTICS ; DCT DOMAIN ; REGRESSION ; ALGORITHM ; JPEG2000 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000383009900004 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/5262]  |
| 专题 | 国家天文台_太阳物理研究部 |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Solar Act, Natl Astron Observ, 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.Nanyang Technol Univ, Singapore 639798, Singapore 4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 5.Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518005, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Long,Li, Jia,Lin, Weisi,et al. Pairwise comparison and rank learning for image quality assessment[J]. DISPLAYS,2016,44:21-26. |
| APA | Xu, Long,Li, Jia,Lin, Weisi,Zhang, Yun,Zhang, Yongbing,&Yan, Yihua.(2016).Pairwise comparison and rank learning for image quality assessment.DISPLAYS,44,21-26. |
| MLA | Xu, Long,et al."Pairwise comparison and rank learning for image quality assessment".DISPLAYS 44(2016):21-26. |
入库方式： OAI收割
来源：国家天文台
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