Study of Subjective and Objective Quality Assessment of Video

We present the results of a recent large-scale subjective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to assess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online.

[1]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[2]  A. Bovik A VISUAL INFORMATION FIDELITY APPROACH TO VIDEO QUALITY ASSESSMENT , 2005 .

[3]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[4]  Alan C. Bovik,et al.  Unifying analysis of full reference image quality assessment , 2008, 2008 15th IEEE International Conference on Image Processing.

[5]  Margaret H. Pinson,et al.  Comparing subjective video quality testing methodologies , 2003, Visual Communications and Image Processing.

[6]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[7]  D. C. Howell Statistical Methods for Psychology , 1987 .

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

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

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

[11]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

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

[13]  Eero P. Simoncelli,et al.  A model of neuronal responses in visual area MT , 1998, Vision Research.

[14]  Alan C. Bovik,et al.  A subjective study to evaluate video quality assessment algorithms , 2010, Electronic Imaging.

[15]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

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

[18]  Eero P. Simoncelli,et al.  Noise characteristics and prior expectations in human visual speed perception , 2006, Nature Neuroscience.

[19]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[20]  Alan C. Bovik,et al.  GAFFE: A Gaze-Attentive Fixation Finding Engine , 2008, IEEE Transactions on Image Processing.

[21]  Jean-Bernard Martens,et al.  Quality asessment of coded images using numerical category scaling , 1995, Other Conferences.

[22]  David J. Fleet,et al.  Phase-based disparity measurement , 1991, CVGIP Image Underst..

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

[24]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[25]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.

[26]  Stephan Wenger,et al.  H.264/AVC over IP , 2003, IEEE Trans. Circuits Syst. Video Technol..

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

[28]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Alfredo Restrepo,et al.  Localized measurement of emergent image frequencies by Gabor wavelets , 1992, IEEE Trans. Inf. Theory.

[30]  Philip Corriveau,et al.  Video Quality Experts Group , 2005 .

[31]  Zhou Wang,et al.  Video quality assessment using a statistical model of human visual speed perception. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  Miska M. Hannuksela,et al.  H.264/AVC in wireless environments , 2003, IEEE Trans. Circuits Syst. Video Technol..