Skip to main content
SpringerLink
Log in

Seamless video access for mobile devices by content-aware utility-based adaptation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Today’s Internet multimedia services are characterized by heterogeneous networks, a wide range of terminals, diverse user preferences, and varying natural environment conditions. Heterogeneity of terminals, networks, and user preferences impose nontrivial challenges to the Internet multimedia services for providing seamless multimedia access particularly for mobile devices (e.g., laptops, tablet PCs, PDAs, mobile phones, etc.). Thus, it is essential that advanced multimedia technologies are developed to deal with these challenges. One of these technologies is video adaptation, which has gained significant importance with its main objective of enabling seamless access to video contents available over the Internet. Adaptation decision taking, which can be considered as the “brain” of video adaptation, assists video adaptation to achieve this objective. Scalable Video Coding (SVC) offers flexibility for video adaptation through providing a comprehensive set of scalability parameters (i.e., temporal, spatial, and quality) for producing scalable video streams. Deciding the best combination of scalability parameters to adapt a scalable video stream while satisfying a set of constraints (e.g., device specifics, network bandwidth, etc.) poses challenges for the existing adaptation services to enable seamless video access. To ease such challenges, an adaptation decision taking technique employing a utility-based approach to decide on the most adequate scalability parameters for adaptation operations is developed. A Utility Function (UF), which models the relationships among the scalability parameters and weights specifying the relative importance of these parameters considering video content characteristics (i.e., motion activity and structural feature), is proposed to assist the developed technique. In order to perform the developed adaptation decision taking technique, a video adaptation framework is also proposed in this paper. The adaptation experiments performed using the proposed framework prove the effectiveness of the framework to provide an important step towards enabling seamless video access for mobile devices to enhance viewing experience of users.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Akyol E., Tekalp A. M., and Civanlar M. R., 2007, “Content-aware Scalability-type Selection for Rate Adaptation of Scalable Video,” EURASIP Journ. on Applied Signal Proc. 2007, Article ID 10236.

  2. Bocheck P, Nakajima Y, Chang S-F (1999) Real-time Estimation of Subjective Utility Functions for MPEG-4 Video Objects. Proc. of IEEE Packet Video Workshop, New York, USA

    Google Scholar 

  3. Bouguet JY (1999) Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm. Intel Corporation, Microprocessor Research Labs, OpenCV Documentation

    Google Scholar 

  4. Canny JF (1986) A computational approach to edge detection. IEEE Trans Pat Analysis and Machine Intelligence 8:679–698

    Article  Google Scholar 

  5. Carvalho P (2004) Multimedia Content Adaptation for Universal Access. M.Sc Thesis, Faculdade de Engenharia da Universidade do Porto, Portugal

    Google Scholar 

  6. Chang SF (2002) Optimal Video Adaptation and Skimming Using a Utility-Based Framework. Tyrrhenian Int. Workshop on Digital Communications, Capri Island, Italy

    Google Scholar 

  7. Chang SF, Vetro A (2005) Video adaptation: Concepts, technologies and open issues. Proc IEEE Proc Special Issue on Advances in Video Coding and Delivery 93(1):148–158

    Google Scholar 

  8. Devore JL (1995) “Probability and statistics for engineering and the sciences”, 4th Edition, pp. 474–585, Duxbury

  9. Douglas A (1971) “Examples concerning efficient strategies for gaussian elimination,” 8: 3–4, Springer, Sep

  10. Eberhard M, Celetto L, Timmerer C, Quacchio E, Hellwagner H (2007) Performance Analysis of Scalable Video Adaptation: Generic versus Specific Approach. IEEE Trans on CSVT 17(9):50–53

    Google Scholar 

  11. Fleet DJ, Wiess Y (2006) Optical flow estimation in paragios. Handbook of Math. Models in Comput. Vision, Springer, p 239

    Google Scholar 

  12. Grigorescu C, Perkov N, Westenberg MA (2004) Contour and boundary detection improved by surround suppression of texture edges. Journal of Image and Vision Computing 22:583–679

    Article  Google Scholar 

  13. Hands DS (2004) “A basic multimedia quality model,” IEEE Trans. Multimedia, vol. 6, no. 6, Dec.

    Google Scholar 

  14. Hsiao MH, Chen YW, Chen HT, Chou KH, and Lee SY (2007) “Content-aware video adaptation under low-bitrate constraint,” EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 17179, May.

  15. Information Technology- Multimedia Frameworks-Part 7: Digital Item Adaptation, ISO\IEC Standard ISO-IEC 21000–7:2007, Dec. 2007.

  16. JSVM 9.13.1 Software, downloaded from CVS Server, garcon.ient.rwth-aachen.de/cvs/jv

  17. Lucas BD, and Kanade T (1981) “An iterative image registration technique with an application to stereo vision,” Int. Joint Conf. Artificial Intel pp. 674–679

  18. Mangasarian OL, and Wild EW (2004) “Feature selection in K-median clustering,” Workshop on Clustering High Dimensional Data and its Applications, pp. 23–28, La Buena Vista, FL

  19. Methodology for the Subjective Assessment of the Quality of Television Pictures, ITU-Recommendation BT.500-10, 2000, [Online]. Available: http://www.itu.org

  20. Methodology for the Subjective Assessment of the Quality of Television Pictures, ITU-Recommendation BT.500-11, 2002. [Online]. Available: http://www.itu.org

  21. Mukherjee D, Delfosse E, Kim J-G, Wang Y (2005) Optimal adaptation decision taking for terminal and network quality of service. IEEE Trans on Multimedia 7(7):454–462

    Article  Google Scholar 

  22. Onur OD, and Alatan AA (2005) “Video adaptation for transmission channels by utility modeling,” IEEE Int. Conf. on Multimedia and Expo, Amsterdam, The Netherlands, 6 Jul

  23. Onur OD, and Alatan AA (2007) “Video adaptation based on content characteristics and hardware capabilities,” Second Int. Workshop on Semantic Media Adaptation and Personalization, pp. 15–20, Uxbridge, UK, 17–18 Dec.

  24. Papari G., Campisi P, Petkov N, and Neri A (2006) “A multiscale approach to contour detection by texture suppression,” SPIE Image Proc.: Alg. and Syst., San Jose, CA, USA, Jan

  25. Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting 50(3):312–322

    Article  Google Scholar 

  26. Prangl M, Szkaliczki T, Hellwagner H (2007) “A framework for utility-based multimedia adaptation,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 17, No. 6, Jun

  27. Shechtman E, Caspi Y, Irani M (2005) Space-time super-resolution. IEEE Trans on Pattern Analysis and Machine Intelligence 4(27):531–545

    Article  Google Scholar 

  28. Sofokleous AA, Angelides MC (2008) DCAF: An MPEG-21 dynamic content adaptation framework. Springer Multimedia Tools Applications 40(2):151–182

    Article  Google Scholar 

  29. Thang C, Kim J-G, Kang JW, Yoo J-J (2009) SVC Adaptation: Standard Tools and Supporting Methods. Elsevier Signal Processing: Image Communication 24:214–228

    Google Scholar 

  30. Tomasi C, and Kanade T (1991) “Detection and Tracking of Point Features,” Carnegie Mellon University Technical Report CMU-CS-91-132, April

  31. Vetro A (2004) MPEG-21 Digital Item Adaptation: Enabling Universal Multimedia Access. IEEE Transactions on Multimedia 11(1):84–87

    Article  MathSciNet  Google Scholar 

  32. Wang Z, Bovik A, Sheikh H, and Simoncelli E (2004) “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Proc., pp. 600–612

  33. Wang Y, Kim J-G, Chang S-F, Kim H-M (2007) Utility-based video adaptation for universal multimedia access (UMA) and content-based utility function prediction for real-time video transcoding. IEEE Transactions on Multimedia 9:213–220

    Article  Google Scholar 

  34. Wolf S, and Pinson M (2002) “VQM Software and measurement techniques,” National telecommunications and information administration (NTIA’02) Report 02–392, Jun

  35. Yamazaki T (2001) Subjective video quality assessment for adaptive quality of service control. IEEE Int. Conf. Multimedia and Expo, Tokyo, Japan

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Departmant, Kirikkale University, Kirikkale, Turkey

    G. Nur

  2. I-Lab Multimedia Communications Research, Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, Surrey, UK

    H. Kodikara Arachchi, S. Dogan & A. M. Kondoz

Authors
  1. G. Nur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Kodikara Arachchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Dogan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. M. Kondoz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Nur.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nur, G., Arachchi, H.K., Dogan, S. et al. Seamless video access for mobile devices by content-aware utility-based adaptation. Multimed Tools Appl 70, 689–719 (2014). https://doi.org/10.1007/s11042-012-1120-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1120-2

Keywords

Search

Navigation