Abstract
A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
C. S. Inc., Cisco visual networking index: Forecast and methodology, 2011-2016 (June 2012), http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf
Meier, S., Barisch, M., Kirstädter, A., Schlosser, D., Duelli, M., Jarschel, M., Hoßfeld, T., Hoffmann, K., Hoffmann, M., Kellerer, W., Khan, A., Jurca, D., Kozu, K.: Provisioning and Operation of Virtual Networks. In: Electronic Communications of the EASST, Kommunikation in Verteilten Systemen 2011, vol. 37 (March 2011)
Klein, D., Zinner, T., Lange, S., Singeorzan, V., Schmid, M.: Video Quality Monitoring based on Precomputed Frame Distortions. In: IFIP/IEEE International Workshop on Quality of Experience Centric Management (QCMan), Ghent, Belgium (May 2013)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)
Schatz, R., Hoßfeld, T., Janowski, L., Egger, S.: From Packets to People: Quality of Experience as a New Measurement Challenge. In: Biersack, E., Callegari, C., Matijasevic, M. (eds.) Data Traffic Monitoring and Analysis. LNCS, vol. 7754, pp. 219–263. Springer, Heidelberg (2013)
Reibman, A., Vaishampayan, V., Sermadevi, Y.: Quality monitoring of video over a packet network. IEEE Transactions on Multimedia 6(2) (April 2004)
Tao, S., Apostopoloulos, J., Guerin, R.: Real-Time Monitoring of Video Quality in IP Networks. IEEE Transactions on Networking 16(6) (December 2008)
Naccari, M., Tagliasacchi, M., Tubaro, S.: No-Reference Video Quality Monitoring for H.264/AVC Coded Video. IEEE Transactions on Multimedia 11(5) (August 2009)
Apostolopoulos, J., Reibman, A.: The Challenge of Estimating Video Quality in Video Communication Applications [In the Spotlight]. IEEE Signal Processing Magazine 29(2), 160–158(2012)
Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment using structural distortion measurement. In: International Conference on Image Processing, vol. 3, pp. 65–68 (2002)
I. T. Union, ITU-T Recommendation P.910: Subjective video quality assessmentmethods for multimedia applications (April 2008), http://www.itu.int/rec/T-REC-P.910/en
xiph.org, Derf’s test media collection (March 2013), http://media.xiph.org/video/derf/
Zatt, B., Porto, M., Scharcanski, J., Bampi, S.: Gop structure adaptive to the video content for efficient H.264/AVC encoding. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3053–3056 (September 2010)
x264 - h264/avc encoder, http://www.videolan.org/developers/x264.html
G. Multimedia open source project, Mp4box, http://gpac.wp.mines-telecom.fr/mp4box/
Klaue, J., Rathke, B., Wolisz, A.: EvalVid - A Framework for Video Transmission and Quality Evaluation. In: Kemper, P., Sanders, W.H. (eds.) TOOLS 2003. LNCS, vol. 2794, pp. 255–272. Springer, Heidelberg (2003)
tcpdump/libcap, http://www.tcpdump.org/
windump/winpcap, http://www.winpcap.org/windump/
T. M. Project, Mencoder, http://mplayerhq.hu/design7/news.html .
Graphics and M.S.U. Media Lab, CMC department, Msu video quality measurement tool
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Klein, D., Zinner, T., Borchert, K., Lange, S., Singeorzan, V., Schmid, M. (2013). Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions. In: Bauschert, T. (eds) Advances in Communication Networking. EUNICE 2013. Lecture Notes in Computer Science, vol 8115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40552-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-40552-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40551-8
Online ISBN: 978-3-642-40552-5
eBook Packages: Computer ScienceComputer Science (R0)