Sparse representation for blind image quality assessment
文献类型：会议论文
| 作者 | He, Lihuo ; Tao, Dacheng ; Li, Xuelong ; Gao, Xinbo |
| 出版日期 | 2012 |
| 会议名称 | 2012 ieee conference on computer vision and pattern recognition, cvpr 2012 |
| 会议日期 | june 16, 2012 - june 21, 2012 |
| 会议地点 | providence, ri, united states |
| 页码 | 1146-1153 |
| 英文摘要 | blind image quality assessment (biqa) is an important yet difficult task in image processing related applications. existing algorithms for universal biqa learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. although these algorithms are promising, they face the following problems: 1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; 2) they are sensitive to different datasets; and 3) they have to be retrained when new training samples are available. in this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (nss) feature. it consists of three key steps: extracting nss features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. thorough experiments on standard databases show that the proposed algorithm outperforms representative biqa algorithms and some full-reference metrics. |
| 收录类别 | CPCI（ISTP） ; EI |
| 产权排序 | 3 |
| 会议录 | 2012 ieee conference on computer vision and pattern recognition, cvpr 2012
 |
| 会议录出版者 | ieee computer society, 2001 l street n.w., suite 700, washington, dc 20036-4928, united states |
| 会议录出版地 | united states |
| 语种 | 英语 |
| ISSN号 | 10636919 |
| ISBN号 | 9781467312264 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/20542]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 推荐引用方式 GB/T 7714 | He, Lihuo,Tao, Dacheng,Li, Xuelong,et al. Sparse representation for blind image quality assessment[C]. 见:2012 ieee conference on computer vision and pattern recognition, cvpr 2012. providence, ri, united states. june 16, 2012 - june 21, 2012. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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