No-reference quality assessment of highly compressed video sequences

In this paper we present a no-reference quality assessment algorithm for highly compressed H.264 videos. By analyzing the spatio-temporal artifacts and their effect on perceived visual quality we produced an array of viable predictors of quality. Using a feature selection method a small sub-set of features, each applied to a specific artefact-domain, was selected for optimal quality estimation. The features are mapped into video quality scores using a simple linear function, which is computationally efficient and minimizes the effect learning of content. The resulting algorithm is evaluated on content independent sets of highly compressed H.264 video sequences from the LIVE video database where it shows high correlation with the subjective scores.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Alberto Leon-Garcia,et al.  Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[3]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[4]  Mohamed-Chaker Larabi,et al.  Spatial-temporal Video Quality Metric based on an estimation of QoE , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[5]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

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

[7]  Fan Zhang,et al.  Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments , 2011, IEEE Transactions on Multimedia.

[8]  Rajiv Soundararajan,et al.  Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Zoran A. Ivanovski,et al.  Fusion of local degradation features for No-Reference Video Quality Assessment , 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

[10]  Alan C. Bovik,et al.  Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos , 2010, IEEE Transactions on Image Processing.

[11]  King Ngi Ngan,et al.  Video quality assessment by decoupling additive impairments and detail losses , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[12]  Rajiv Soundararajan,et al.  Study of Subjective and Objective Quality Assessment of Video , 2010, IEEE Transactions on Image Processing.

[13]  Zoran A. Ivanovski,et al.  No-reference image visual quality assessment using nonlinear regression , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[14]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[15]  Alan C. Bovik,et al.  Motion-based perceptual quality assessment of video , 2009, Electronic Imaging.

[16]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[17]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[18]  Damon M. Chandler,et al.  A spatiotemporal most-apparent-distortion model for video quality assessment , 2011, 2011 18th IEEE International Conference on Image Processing.

[19]  Alan C. Bovik,et al.  Blind quality assessment of videos using a model of natural scene statistics and motion coherency , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[20]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.