Training Quality-Aware Filters for No-Reference Image Quality Assessment

被引：39
作者：
Zhang, Lin [1 ,2 ,3 ]
Gu, Zhongyi [1 ,3 ]
Liu, Xiaoxu [1 ,3 ]
Li, Hongyu [1 ,3 ]
Lu, Jianwei [1 ,3 ,4 ]
机构：
[1] Tongji Univ, Shanghai, Peoples R China
[2] Nanjing Univ Sci & Technol, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing, Jiangsu, Peoples R China
[3] Tongji Univ, Sch Software Engn, Shanghai, Peoples R China
[4] Tongji Univ, Adv Inst Translat Med, Shanghai, Peoples R China
来源：
IEEE MULTIMEDIA | 2014年 / 21卷 / 04期
关键词：
Digital imaging; Feature extraction; Filtering; Image coding; Image quality; Multimedia; Natural scene statistics; No-reference image quality assessment; NR-IQA; Predictive models; Quality-aware filters; Random forest; Research and development; Sparse filtering; Training;
D O I：
10.1109/MMUL.2014.50
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The proposed general-purpose, no-reference image quality assessment (NR-IQA) model does not require prior knowledge about nondistorted reference images or the types of image distortions.
引用
收藏
页码：67 / 75
页数：9
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