| Blind Image Quality Assessment via Deep Learning | |
Hou, Weilong1; Gao, Xinbo1; Tao, Dacheng2,3 ; Li, Xuelong4
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| 2015-06-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 卷号 | 26期号:6页码:1275-1286 |
| 摘要 | This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model's effectiveness, efficiency, and robustness. |
| 文章类型 | Article |
| 关键词 | Deep Learning Image Quality Assessment (Iqa) Natural Scene Statistics (Nss) No Reference |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TNNLS.2014.2336852 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NATURAL SCENE STATISTICS ; NEURAL-NETWORK ; DOMAIN ; REPRESENTATION ; INFORMATION ; FRAMEWORK |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000354957000013 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/25058 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hou, Weilong,Gao, Xinbo,Tao, Dacheng,et al. Blind Image Quality Assessment via Deep Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(6):1275-1286. |
| APA | Hou, Weilong,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2015).Blind Image Quality Assessment via Deep Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(6),1275-1286. |
| MLA | Hou, Weilong,et al."Blind Image Quality Assessment via Deep Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.6(2015):1275-1286. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Blind Image Quality （5671KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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