Support Vector Regression Based Video Quality Prediction

To measure the quality of experience (QoE) of a video, the current approaches of objective quality metrics development focus on how to design a video quality model, which considers the effects of the extracted features and models the Human Visual System (HVS). However, video quality metrics which try to model the HVS confronts a fact that HVS is too complicated and not well understood to model. In this paper, instead of modeling the objective quality metrics with some functions, we proposed to build a video quality metrics using the support vector machines (SVMs) supervised learning. With the proposed SVM based video quality prediction, it allows a much better approximation to the NTIA-VQM and MOS values, compared to the previous G.1070-based video quality prediction. We further investigated how to choose the certain features which can be efficiently used as SVM input variables.

[1]  Jun Okamoto,et al.  No reference video-quality-assessment model for video streaming services , 2010, 2010 18th International Packet Video Workshop.

[2]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[3]  Ihor O. Kirenko,et al.  A no-reference blocking artifact measure for adaptive video processing , 2005, 2005 13th European Signal Processing Conference.

[4]  Huicheng Lian,et al.  No-Reference Video Quality Measurement with Support Vector Regression , 2009, Int. J. Neural Syst..

[5]  Tao Liu,et al.  Extending G.1070 for video quality monitoring , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[6]  Stephen Wolf,et al.  Video Quality Measurement Techniques , 2002 .

[7]  Weisi Lin,et al.  Objective Image Quality Assessment Based on Support Vector Regression , 2010, IEEE Transactions on Neural Networks.

[8]  Stefan Winkler,et al.  A no-reference perceptual blur metric , 2002, Proceedings. International Conference on Image Processing.

[9]  Zhan Ma,et al.  Perceptual Quality Assessment of Video Considering Both Frame Rate and Quantization Artifacts , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Yinwei Zhan,et al.  Based on SVM Automatic Measures of Fingerprint Image Quality , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[11]  Rui J. P. de Figueiredo,et al.  A nonlinear image contrast sharpening approach based on Munsell's scale , 2006, IEEE Transactions on Image Processing.

[12]  Sitaram Bhagavathy,et al.  Efficient frame complexity estimation and application to G.1070 vide quality monitoring , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[13]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[14]  P. Cosman,et al.  A Generalized Linear Model for MPEG-2 Packet-Loss Visibility , 2004 .

[15]  Amy R. Reibman,et al.  Predicting packet-loss visibility using scene characteristics , 2007, Packet Video 2007.