A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.

[1]  R. Snee,et al.  Ridge Regression in Practice , 1975 .

[2]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[3]  Hong Ren Wu,et al.  No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Christian Keimel,et al.  Challenges in crowd-based video quality assessment , 2012, 2012 Fourth International Workshop on Quality of Multimedia Experience.

[5]  Hua Yang,et al.  Perceptual quality measurement of video frames affected by both packet losses and coding artifacts , 2009, 2009 International Workshop on Quality of Multimedia Experience.

[6]  Amy R. Reibman,et al.  Quality monitoring of video over a packet network , 2004, IEEE Transactions on Multimedia.

[7]  Peter Lambert,et al.  ViQID: A No-Reference bit stream-based visual quality impairment detector , 2010, 2010 Second International Workshop on Quality of Multimedia Experience (QoMEX).

[8]  Pamela C. Cosman,et al.  A Versatile Model for Packet Loss Visibility and its Application to Packet Prioritization , 2010, IEEE Transactions on Image Processing.

[9]  Margaret H. Pinson,et al.  A new standardized method for objectively measuring video quality , 2004, IEEE Transactions on Broadcasting.

[10]  D. Hinkle,et al.  Applied statistics for the behavioral sciences , 1979 .

[11]  Gerard de Haan,et al.  No-Reference Metric Design With Machine Learning for Local Video Compression Artifact Level , 2011, IEEE Journal of Selected Topics in Signal Processing.

[12]  Stefano Tubaro,et al.  Subjective Quality Assessment of H.264/AVC Video Streaming with Packet Losses , 2011, EURASIP J. Image Video Process..

[13]  Tao Liu,et al.  A Novel Video Quality Metric for Low Bit-Rate Video Considering Both Coding and Packet-Loss Artifacts , 2009, IEEE Journal of Selected Topics in Signal Processing.

[14]  Pamela C. Cosman,et al.  Predicting H.264 Packet Loss Visibility using a Generalized Linear Model , 2006, 2006 International Conference on Image Processing.

[15]  Pamela C. Cosman,et al.  Network-Based Model for Video Packet Importance Considering Both Compression Artifacts and Packet Losses , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[16]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[17]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[18]  Tamer Shanableh,et al.  Prediction of Structural Similarity Index of Compressed Video at a Macroblock Level , 2011, IEEE Signal Processing Letters.

[19]  Muhammad Shahid,et al.  A no-reference machine learning based video quality predictor , 2013, 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).

[20]  Yan Yang,et al.  A no-reference video quality metric by using inter-frame encoding characters , 2011, 2011 The 14th International Symposium on Wireless Personal Multimedia Communications (WPMC).

[21]  Sheila S. Hemami,et al.  A scalable wavelet-based video distortion metric and applications , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[23]  Alexander Raake,et al.  No-reference video quality assessment for SD and HD H.264/AVC sequences based on continuous estimates of packet loss visibility , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[24]  Kai Zeng,et al.  Quality-aware video based on robust embedding of intra- and inter-frame reduced-reference features , 2010, 2010 IEEE International Conference on Image Processing.

[25]  R. Tibshirani.  The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.

[26]  Christian Keimel,et al.  Design of no-reference video quality metrics with multiway partial least squares regression , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.