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Spatiotemporal Statistics for Video Quality Assessment
Li, Xuelong; Guo, Qun; Lu, Xiaoqiang
作者部门光学影像学习与分析中心
2016-07-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
卷号25期号:7页码:3329-3342
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摘要It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types, which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are first extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression model afterward. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in the 3D-DCT domain that has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; and 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing full-reference VQA and reduced-reference VQA metrics.
文章类型Article
关键词Video Quality Assessment No-reference 3d-dct Natural Video Spatiotemporal Statistics
WOS标题词Science & Technology ; Technology
DOI10.1109/TIP.2016.2568752
收录类别SCI ; EI
关键词[WOS]NATURAL SCENE STATISTICS ; DCT DOMAIN ; IMAGE ; MECHANISMS ; PREDICTION ; VISIBILITY ; VISION ; SHAPE
语种英语
WOS研究方向Computer Science ; Engineering
项目资助者National Basic Research Program of China (973 Program)(2012CB719905) ; National Natural Science Foundation of China(61472413) ; Key Research Program of the Chinese Academy of Sciences(KGZD-EW-T03) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408)
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000377371700004
引用统计
被引频次:128[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/28158
专题光谱成像技术研究室
作者单位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
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Li, Xuelong,Guo, Qun,Lu, Xiaoqiang. Spatiotemporal Statistics for Video Quality Assessment[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(7):3329-3342.
APA Li, Xuelong,Guo, Qun,&Lu, Xiaoqiang.(2016).Spatiotemporal Statistics for Video Quality Assessment.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(7),3329-3342.
MLA Li, Xuelong,et al."Spatiotemporal Statistics for Video Quality Assessment".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.7(2016):3329-3342.
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