Learning to Rank for Blind Image Quality Assessment
文献类型：期刊论文
| 作者 | Gao, Fei1; Tao, Dacheng2 ; Gao, Xinbo3; Li, Xuelong4
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| 刊名 | ieee transactions on neural networks and learning systems
 |
| 出版日期 | 2015-10-01 |
| 卷号 | 26期号:10页码:2275-2290 |
| 关键词 | Image quality assessment (IQA) learning preferences learning to rank multiple kernel learning (MKL) universal blind IQA (BIQA) |
| 英文摘要 | blind image quality assessment (biqa) aims to predict perceptual image quality scores without access to reference images. state-of-the-art biqa methods typically require subjects to score a large number of images to train a robust model. however, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. to combat these limitations, this paper explores and exploits preference image pairs (pips) such as the quality of image i-a is better than that of image i-b for training a robust biqa model. the preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such pips can be generated at a very low cost. the proposed biqa method is one of learning to rank. we first formulate the problem of learning the mapping from the image features to the preference label as one of classification. in particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. a simple but effective strategy to estimate perceptual image quality scores is then presented. experiments show that the proposed biqa method is highly effective and achieves a performance comparable with that of state-of-the-art biqa algorithms. moreover, the proposed method can be easily extended to new distortion categories. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | natural scene statistics ; framework ; algorithms ; domain |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000362358800005 |
| 公开日期 | 2015-11-17 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/25482]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Video & Image Proc Syst Lab, Xian 710071, Shaanxi, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Fei,Tao, Dacheng,Gao, Xinbo,et al. Learning to Rank for Blind Image Quality Assessment[J]. ieee transactions on neural networks and learning systems,2015,26(10):2275-2290. |
| APA | Gao, Fei,Tao, Dacheng,Gao, Xinbo,&Li, Xuelong.(2015).Learning to Rank for Blind Image Quality Assessment.ieee transactions on neural networks and learning systems,26(10),2275-2290. |
| MLA | Gao, Fei,et al."Learning to Rank for Blind Image Quality Assessment".ieee transactions on neural networks and learning systems 26.10(2015):2275-2290. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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