NO-REFERENCE VIDEO QUALITY MEASUREMENT WITH SUPPORT VECTOR REGRESSION

被引：13

作者：

Lian, Huicheng ^{[1
]}

机构：

[1] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200072, Peoples R China

来源：

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS | 2009年 / 19卷 / 06期

关键词：

No-reference video quality measurement; support vector regression; linear regression; video quality; EXTREME LEARNING-MACHINE;

D O I：

10.1142/S0129065709002154

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel approach for no-reference video quality measurement is proposed in this paper. Firstly, various feature extraction methods are used to quantify the quality of videos. Then, a support vector regression model is trained and adopted to predict unseen samples. Six different regression models are compared with the support vector regression model. The experimental results indicate that the combination of different video quality features with a support vector regression model can outperform other methods for no-reference video quality measurement significantly.

引用

页码：457 / 464

页数：8

共 29 条

[1]

ANDREAS R, 2008, P 10 IEEE WORKSH MUL, P769

[2]

[Anonymous], 1987, LEARNING INTERNAL RE

[3]

[Anonymous], 2000, J143 ITUT

[4]

Chang C.-C., LIBSVM: a Library for Support Vector Machines

[5]

Chatterjee S., 1986, Stat. Sci., V1, P379, DOI [DOI 10.1214/SS/1177013622, 10.1214/ss/1177013622]

[6] Objective quality assessment of MPEG-2 video streams by using CBP neural networks [J].

Gastaldo, P ;

Rovetta, S ;

Zunino, R .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (04) :939-947

[7] Gene selection for cancer classification using support vector machines [J].

Guyon, I ;

Weston, J ;

Barnhill, S ;

Vapnik, V .

MACHINE LEARNING, 2002, 46 (1-3) :389-422

[8] AN IMPROVEMENT OF EXTREME LEARNING MACHINE FOR COMPACT SINGLE-HIDDEN-LAYER FEEDFORWARD NEURAL NETWORKS [J].

Huynh, Hieu Trung ;

Won, Yonggwan ;

Kim, Jung-Ja .

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2008, 18 (05) :433-441

[9] No-Reference Perceptual Video Quality Measurement for High Definition Videos Based on an Artificial Neural Network [J].

Jiang, Xiuhua ;

Meng, Fang ;

Xu, Jiangbo ;

Zhou, Wei .

ICCEE 2008: PROCEEDINGS OF THE 2008 INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING, 2008, :424-427

[10] A convolutional neural network approach for objective video quality assessment [J].

Le Callet, Patrick ;

Viard-Gaudin, Christian ;

Barba, Dominique .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (05) :1316-1327

← 1 2 3 →