Quality metric to assess video streaming service over TCP considering temporal location of pauses

There is a wide range of video services over complex transmission networks, and in some cases end users fail to receive an acceptable quality level. In this paper, the different factors that degrade users' quality of experience (QoE) in video streaming service that use TCP as transmission protocol are studied. In this specific service, impairment factors are: number of pauses, their duration and temporal location. In order to measure the effect that each temporal segment has in the overall video quality, subjective tests. Because current subjective test methodologies are not adequate to assess video streaming over TCP, some recommendations are provided here. At the application layer, a customized player is used to evaluate the behavior of player buffer, and consequently, the end user QoE. Video subjective test results demonstrate that there is a close correlation between application parameters and subjective scores. Based on this fact, a new metrics named VsQM is defined, which considers the importance of temporal location of pauses to assess the user QoE of video streaming service. A useful application scenario is also presented, in which the metrics proposed herein is used to improve video services.

[1]  Alan C. Bovik,et al.  Temporal hysteresis model of time varying subjective video quality , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Jukka Häkkinen,et al.  What do users really perceive: probing the subjective image quality , 2006, Electronic Imaging.

[3]  Judith Redi,et al.  Comparing subjective image quality measurement methods for the creation of public databases , 2010, Electronic Imaging.

[4]  Bongsoon Kang,et al.  New video enhancement preprocessor using the region-of-interest for the videoconferencing , 2010, IEEE Transactions on Consumer Electronics.

[5]  Alan C. Bovik,et al.  Visual Importance Pooling for Image Quality Assessment , 2009, IEEE Journal of Selected Topics in Signal Processing.

[6]  Hiroshi Noborio,et al.  Design and Evaluation of Hybrid Congestion Control Mechanism for Video Streaming , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[7]  Andrew Perkis,et al.  Attention modeling for video quality assessment: Balancing global quality and local quality , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[8]  Rocky K. C. Chang,et al.  Measuring the quality of experience of HTTP video streaming , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.

[9]  Masayuki Murata,et al.  Non Bandwidth-intrusive Video Streaming over TCP , 2011, 2011 Eighth International Conference on Information Technology: New Generations.

[10]  Cesson Sévigné NEW QUALITY EVALUATION METHOD SUITED TO MULTIMEDIA CONTEXT SAMVIQ , 2006 .

[11]  Miska M. Hannuksela,et al.  Perceptual quality assessment based on visual attention analysis , 2009, ACM Multimedia.

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

[13]  Chuang Lin,et al.  Enabling on-demand internet video streaming services to multi-terminal users in large scale , 2009, IEEE Transactions on Consumer Electronics.

[14]  Noël Crespi,et al.  QoE Aware Service Delivery in Distributed Environment , 2011, 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications.

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

[16]  Xiao-Hong Peng,et al.  An Objective Approach to Measuring Video Playback Quality in Lossy Networks using TCP , 2011, IEEE Communications Letters.

[17]  Jianping Pan,et al.  Performance analysis of TCP-friendly AIMD algorithms for multimedia applications , 2005, IEEE Transactions on Multimedia.

[18]  Dong-Hwan Har,et al.  Subjective image quality assessment based on objective image quality measurement factors , 2011, IEEE Transactions on Consumer Electronics.

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