Perceptual Quality Metric Design for Wireless Image and Video Communication

The evolution of advanced radio transmission technologies for third generation mobile radio systems has paved the way for the delivery of mobile multimedia services. In particular, wireless image and video applications are among the most popular services offered on modern mobile devices to support communication beyond the traditional voice services. The large amount of data necessary to represent the visual content and the scarce bandwidth of the wireless channel impose new challenges for the network operator to deliver high quality image and video services. Link layer metrics have conventionally been used to monitor the received signal quality but were found to not accurately reflect the visual quality as it is perceived by the end-user. These metrics thus need to be replaced by suitable metrics that measure the overall impairments induced during image or video communication and accurately relate them to subjectively perceived quality. In this thesis, we focus on objective metrics that are able to quantify the end-to-end visual quality in wireless image and video communication. Such metrics may then be utilised to support the efficient use of link adaptation and resource management techniques and thus guarantee a certain quality of service to the user. The thesis is divided into four parts. The first part contributes an extensive survey and classification of contemporary image and video quality metrics that may be applicable in a communication context. The second part then discusses the development of the Normalised Hybrid Image Quality Metric (NHIQM) that we propose for prediction of visual quality degradations induced during wireless communication. The metric is based on a set of structural features, which are deployed to quantify artifacts that may occur in a wireless communication system and also are well aligned to characteristics of the human visual system (HVS). In the third part, three metric designs are discussed that utilise the same structural feature set as a basis for quality prediction. Incorporation of further HVS characteristics into the metric design will then improve even more the visual quality prediction performance. The design and validation of all proposed metrics is supported by subjective quality experiments that we conducted in two independent laboratories. Comparison to other state of the art visual quality metrics reveals the ability of the proposed metrics to accurately predict visual quality in a wireless communication system. The last part contributes an application of NHIQM for filter design. In particular, the filtering performance of a de-blocking de-ringing post filter for H.263 video sequences is analysed with regards to visual quality of the filtered sequence when applying appropriate filter parameter combinations.

[1]  Olivier Verscheure,et al.  Perceptual quality measure using a spatiotemporal model of the human visual system , 1996, Electronic Imaging.

[2]  David Soldani,et al.  QoS and QoE Management in UMTS Cellular Systems: Soldani/QoS and QoE Management in UMTS Cellular Systems , 2006 .

[3]  Wei-Ying Ma,et al.  Blur determination in the compressed domain using DCT information , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[4]  Sanjit K. Mitra,et al.  No-reference video quality metric based on artifact measurements , 2005, IEEE International Conference on Image Processing 2005.

[5]  Hans-Jurgen Zepernick,et al.  Multi-resolution Structural Degradation Metrics for Perceptual Image Quality Assessment , 2007, PCS 2007.

[6]  Jean-Bernard Martens,et al.  Multidimensional modeling of image quality , 2002, Proc. IEEE.

[7]  Jens-Rainer Ohm Multimedia Communication Technology: Representation,Transmission and Identification of Multimedia Signals , 2004 .

[8]  Zhou Wang,et al.  Blind measurement of blocking artifacts in images , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[9]  Zhou Wang,et al.  No-reference perceptual quality assessment of JPEG compressed images , 2002, Proceedings. International Conference on Image Processing.

[10]  Huib de Ridder Minkowski-metrics as a combination rule for digital-image-coding impairments , 1992 .

[11]  Hans-Jurgen Zepernick,et al.  Objective Hybrid Image Quality Metric for In-Service Quality Assessment , 2005 .

[12]  H.-J. Zepernick,et al.  Perceptual evaluation of motion JPEG2000 quality over wireless channels , 2006, Joint IST Workshop on Mobile Future, 2006 and the Symposium on Trends in Communications. SympoTIC '06..

[13]  Jean-Bernard Martens,et al.  A single-ended blockiness measure for JPEG-coded images , 2002, Signal Process..

[14]  Lina J. Karam,et al.  Human Visual System Based No-Reference Objective Image Sharpness Metric , 2006, 2006 International Conference on Image Processing.

[15]  Hans-Jürgen Zepernick,et al.  An Artificial Neural Network for Quality Assessment in Wireless Imaging Based on Extraction of Structural Information , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

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

[17]  Ulrich Engelke,et al.  Perceptual Quality Assessment of Wireless Video Applications , 2006 .

[18]  Zhou Wang,et al.  Reduced-reference image quality assessment using a wavelet-domain natural image statistic model , 2005, IS&T/SPIE Electronic Imaging.

[19]  常厚飞,et al.  Left or Right? , 2018, Citizenship and Contemporary Direct Democracy.

[20]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[21]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[22]  Alan C. Bovik,et al.  . Efficient DCT-domain blind measurement and reduction of blocking artifacts , 2002, IEEE Trans. Circuits Syst. Video Technol..

[23]  J A Solomon,et al.  Model of visual contrast gain control and pattern masking. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  Sanjit K. Mitra,et al.  Video quality objective metric using data hiding , 2002, 2002 IEEE Workshop on Multimedia Signal Processing..

[25]  H. Zepernick,et al.  Quality Assessment of an Adaptive Filter for Artifact Reduction in Mobile Video Sequences , 2007, 2007 2nd International Symposium on Wireless Pervasive Computing.

[26]  R. Vemuri,et al.  An analysis on the effect of image features on lossy coding performance , 2000, IEEE Signal Processing Letters.

[27]  Stefan Winkler,et al.  Digital Video Quality: Vision Models and Metrics , 2005 .

[28]  James Hu,et al.  DVQ: A digital video quality metric based on human vision , 2001 .

[29]  Tubagus Maulana Kusuma,et al.  On the development of a reduced-reference perceptual image quality metric , 2005, 2005 Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05).

[30]  RECOMMENDATION ITU-R BS.1387-1 - Method for objective measurements of perceived audio quality , 2002 .

[31]  Sethuraman Panchanathan,et al.  A Framework for Advanced Video Traces: Evaluating Visual Quality for Video Transmission Over Lossy Networks , 2006, EURASIP J. Adv. Signal Process..

[32]  A. M. Rohaly,et al.  Automatic detection of regions of interest in complex video sequences , 2001, IS&T/SPIE Electronic Imaging.

[33]  H.R. Wu,et al.  A generalized block-edge impairment metric for video coding , 1997, IEEE Signal Processing Letters.

[34]  Edward H. Adelson,et al.  PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .

[35]  H.-J. Zepernick,et al.  Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics , 2007, 2007 2nd International Symposium on Wireless Pervasive Computing.

[36]  T. Vlachos,et al.  Detection of blocking artifacts in compressed video , 2000 .

[37]  Rosa Lancini,et al.  Subjective quality evaluation of video sequences by using motion information , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[38]  Stefan Winkler,et al.  Perceived Audiovisual Quality of Low-Bitrate Multimedia Content , 2006, IEEE Transactions on Multimedia.

[39]  Hans-Jurgen Zepernick,et al.  A reduced-reference perceptual quality metric for in-service image quality assessment , 2003, SympoTIC'03. Joint 1st Workshop on Mobile Future and Symposium on Trends in Communications.

[40]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[41]  Bernd Girod,et al.  What's wrong with mean-squared error? , 1993 .

[42]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[43]  Hong Ren Wu,et al.  Digital Video Image Quality and Perceptual Coding , 2005 .

[44]  Methods for objective and subjective assessment of quality Perceptual evaluation of speech quality ( PESQ ) : An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs , 2002 .

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

[46]  Hans-Jürgen Zepernick,et al.  Regional attention to structural degradations for perceptual image quality metric design , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[48]  Makoto Miyahara,et al.  Objective picture quality scale for video images (PQSvideo): definition of distortion factors , 2000, Visual Communications and Image Processing.

[49]  Jorge E. Caviedes,et al.  No-reference sharpness metric based on local edge kurtosis , 2002, Proceedings. International Conference on Image Processing.

[50]  Patrick C. Teo,et al.  Perceptual image distortion , 1994, Proceedings of 1st International Conference on Image Processing.

[51]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[52]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[53]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[54]  Tubagus Maulana Kusuma,et al.  Reduced-reference metric design for objective perceptual quality assessment in wireless imaging , 2009, Signal Process. Image Commun..

[55]  Andrew P. Bradley,et al.  Perceptual quality metrics applied to still image compression , 1998, Signal Process..

[56]  Patrick Le Callet,et al.  Pseudo no reference image quality metric using perceptual data hiding , 2006, Electronic Imaging.

[57]  Stefan Winkler,et al.  Segmentation-driven perceptual quality metrics , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[58]  Sanjit K. Mitra,et al.  Perceptual analysis of video impairments that combine blocky, blurry, noisy, and ringing synthetic artifacts , 2005, IS&T/SPIE Electronic Imaging.

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

[60]  Sheila S. Hemami,et al.  Effects of spatial correlations and global precedence on the visual fidelity of distorted images , 2006, Electronic Imaging.

[61]  Lina J. Karam,et al.  A ROBUST IMAGE SHARPNESS METRIC BASED ON KURTOSIS MEASUREMENT OF WAVELET COEFFICIENTS , 2005 .

[62]  Huib de Ridder,et al.  Percentage scaling: a new method for evaluating multiply impaired images , 2000, Electronic Imaging.

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

[64]  Z. L. Budrikis,et al.  Picture Quality Prediction Based on a Visual Model , 1982, IEEE Trans. Commun..

[65]  Zhou Wang,et al.  Quality-aware images , 2006, IEEE Transactions on Image Processing.

[66]  Thrasyvoulos N. Pappas,et al.  Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions , 2008, IEEE Transactions on Image Processing.

[67]  Ashraf A. Kassim,et al.  Digital Video Image Quality and Perceptual Coding , 2005, J. Electronic Imaging.

[68]  M. P. Hollier,et al.  Models of Human Perception , 1999 .

[69]  Anastasios Kourtis,et al.  Evaluation of video quality based on objectively estimated metric , 2005, Journal of Communications and Networks.

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

[71]  Arthur R. Weeks Fundamentals of electronic image processing , 1996, SPIE/IEEE series on imaging science and engineering.

[72]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[73]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.