Multimedia Tools and Applications

, Volume 76, Issue 5, pp 7175–7195 | Cite as

A content-adaptive video quality assessment method for online media service

Article
  • 200 Downloads

Abstract

Video quality assessment is an important issue for Internet Content Providers (ICPs) to improve their service. Some research has been done on objective video quality assessment, but real-time evaluation is still a difficult task. This paper discusses a real-time content-adaptive evaluation method to evaluate the Quality of Experiment (QoE) for online media services. The method is named as Motion Degree of Video Content (MDVC) which is defined to make the video content measureable and computable. It analyzes the relationship between the information entropy gain and the frame size of I, P and B frames, and then evaluates the motion degree of a video clip. Then with the help of a multimedia service simulation platform, a QoE evaluation model is established to map network QoS to QoE. The model is adjusted by MDVC so as to fit different video content dynamically. In particular, to ensure that the evaluation model fits the real-world conditions, PlanetLab is adopted to monitor the real QoS on Internet. Finally we compare the content-adaptive QoE evaluation model with the actual MOS values to verify the feasibility and fitness of the model. Results show that the correlation coefficient reaches 0.91.

Keywords

Quality of experiment Quality of service QoE online measurement Content-adaptive 

References

  1. 1.
    Chun, B., Culler, D., Roscoe, T., Bavier, A., Peterson, L., Wawrzoniak, M., & Bowman, M. (2003). Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review, 2003. Available: https://www.planet-lab.org/
  2. 2.
    Dobrian F, Awan A, Joseph D, Ganjam A, Zhan J, Berkeley UC (2011) Understanding the impact of video quality on user engagement. World 41(4):362–373Google Scholar
  3. 3.
    Fuzheng Y, Jiarun S, Shuai W, Hong RW (2012) Content-adaptive packet-layer model for quality assessment of networked video services. IEEE J Sel Top Sign Proces 6(6):672–683CrossRefGoogle Scholar
  4. 4.
    Garcia MN, Schleicher R, Raake A (2011) Impairment-factor-based audiovisual quality model for IPTV: influence of video resolution, degradation type, and content type. EURASIP J Image Video Process 2011:1–14Google Scholar
  5. 5.
    ​ITU (2004) Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference. Rapport Technique, International Telecommunication Union, ITU-T Rec. J.144Google Scholar
  6. 6.
    ITU (2006) Mean opinion score (MOS) terminology. International Telecommunication Union, Geneva, ITU-T Rec. P. 800.1, 2006Google Scholar
  7. 7.
    ITU (2008) Full reference (FR) and reduced reference (RR) calibration methods for video transmission systems with constant misalignment of spatial and temporal domains with constant gain and offset. ITU-T Rec. J.244Google Scholar
  8. 8.
    Jain R (2004) Quality of experience. IEEE Multimedia 11(1):96–97CrossRefGoogle Scholar
  9. 9.
    Klaue J, Rathke B, Wolisz A (2003) EvalVid - a framework for video transmission and quality evaluation. In: Proceedings of the 13th international conference on modelling techniques and tools for computer performance evaluation, Urbana, 2003, pp 255–272Google Scholar
  10. 10.
    Li W, Issa O, Liu H, Speranza F, Renaud R (2009) Quality assessment of video content for hd iptv applications. International symposium on multimedia, San Diego, 2009, pp 517–522Google Scholar
  11. 11.
    Liu Y, Kurceren R, Budhia U (2006) Video classification for video quality prediction. J Zhejiang Univ (Sci) 7(5):919–926CrossRefMATHGoogle Scholar
  12. 12.
    Pinson M, Wolf S, Tripathi N, Koh C (2010) The consumer digital video library. Paper presented at the 5th international workshop on video processing and quality metrics for consumer electronics (VPQM), Scottsdale, 2010Google Scholar
  13. 13.
    Rahrer T, Fiandra R, Wright S (2006) Triple-play service quality of experience (QoE) requirements, TR-126, DSL Forum, 13 Dec 2006Google Scholar
  14. 14.
    Richardson IE (2001) MPEG-4 Visual. H. 264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia, 99-157. Wiley, UKGoogle Scholar
  15. 15.
    Sommers J, Barford P, Duffield N, Ron A (2005) Improving accuracy in end-to-end packet loss measurement. SIGCOMM Comput Commun Rev 35(4):157–168CrossRefGoogle Scholar
  16. 16.
    Sullivan G, Wiegand T, Marpe D, Luthra A (2004) Text of ISO/IEC 14496-10 Advanced Video Coding 3rd Edition. ISO/IEC JTC 1/SC 29/WG11 N6540, July 2004Google Scholar
  17. 17.
    Video Quality Experts Group (2006) VQEG. [Online]. http://www.vqeg.org/
  18. 18.
    Wang C, Jiang X, Wang Y (2011) Content-related features for video quality assessment based on bit streams. In: 2011 International Conference on Automation and Robotics, ICAR 2011, December 1, 2011 - December 2, 2011. Springer Verlag, DubaiGoogle Scholar
  19. 19.
    Welch J, Clark J (2006), RFC 4445: A proposed media delivery index (MDI). Retrieved from http://tools.ietf.org/html/rfc4445
  20. 20.
    Wolf S (2009) A full reference (FR) method using causality processing for estimating variable video delays. NTIA Technical Memorandum TM-10-463Google Scholar
  21. 21.
    Wolf MS (2011) Video quality model for variable frame delay (VQM VFD). National Telecommunications and Information Administration NTIA Technical Memorandum TM-11-482Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringTongji UniversityShanghaiChina

Personalised recommendations