Autocorrelation analysis of spatial features for mobile video services

The growing deployment of mobile video applications is calling for new methods to measure quality as perceived by humans. The suitable quality metrics may explore spatial and temporal video features that quantify the presence of related artifacts in an examined mobile video service. Considering a video as a sequence of images, computation of spatial features for each involved image would pose significant strain on system resources such as battery power and potentially induce large computational load in the mobile terminal. In this paper, we therefore examine the progression of spatial features in mobile videos over time using an autocorrelation approach. This enables us to reveal the duration over which spatial feature values of a mobile video may be considered as constant. In analogy to the characterization of mobile radio channels, we refer to this duration as the coherence time of an examined spatial feature for a given video. The provided numerical results illustrate that large reductions in the frequency of computing spatial features in mobile videos may be obtained. This in turn reduces the consumption of resources in the mobile terminal.

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

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

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

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[7]  Touradj Ebrahimi,et al.  Perceptual quality assessment for video watermarking , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

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

[9]  Ulrich Engelke,et al.  Perceptual Quality Metric Design for Wireless Image and Video Communication , 2008 .

[10]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

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

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

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

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

[15]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

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

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