Quality-aware predictor-based adaptation of still images for the multimedia messaging service

Abstract

The Multimedia Messaging Service (MMS) allows users with heterogeneous terminals to exchange structured messages composed of text, images, sound, and video. The MMS market is growing rapidly, posing the problem of MMS adaptation, which is necessary to ensure terminal interoperability. Message adaptation involves technological challenges, especially considering the high volume of messages that this service can handle. In this work, we propose novel predictor-based dynamic programming approaches to MMS adaptation, which provide a framework for explicit maximization of the user experience, rather than relying on heuristics to deliver adapted messages satisfactorily. We show that the proposed solutions lead to noticeably superior image quality and faster transcoding times than comparable algorithms offered in products currently on the market and those described in the literature.

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References

  1. 1.

    ImageMagick and Magick+ + (2013) http://www.imagemagick.org. Accessed 10 February 2013

  2. 2.

    The Independent JPEG Group (2013) http://www.ijg.org. Accessed 10 February 2013

  3. 3.

    Clark-Dickson P (2012) SMS will remain more popular than mobile messaging apps over next five years. Informa telecoms and media. http://blogs.informatandm.com/4971/press-releasesms-will-remain-moreQ3popular-than-mobile-messaging-apps-over-next-five-years. Accessed 10 February 2013

  4. 4.

    mobiThinking (2012) Global mobile statistics 2012 Part C: Mobile marketing, advertising and messaging. http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats/c. Accessed 10 February 2013

  5. 5.

    Portio research (2012) Mobile messaging futures 2012–2016. http://www.portioresearch.com/en/reports/current-portfolio/mobile-messaging-futures-2012-2016.aspx. Accessed 10 February 2013

  6. 6.

    3GPP TS 23.140 V6.16.0: multimedia message service (MMS); functional description; stage 2 (Release 6) (2009). http://www.3gpp.org/ftp/Specs

  7. 7.

    Acharya T, Tsai PS (2004) JPEG 2000 standard for image compression: concepts, algorithms, VLSI architectures. Wiley-Interscience

  8. 8.

    Bellman R (2003) Dynamic programming. Dover, New York

    Google Scholar 

  9. 9.

    Blackman RB, Tukey J (1959) The measurement of power spectra, from the point of view of communications engineering. Dover

  10. 10.

    Chen RCS, Yang SJH, Zhang J (2010) Enhancing the precision of content analysis in content adaptation using entropy-based fuzzy reasoning. Expert Syst Appl 37:5706–5719

    Article  Google Scholar 

  11. 11.

    Coulombe S, Grassel G (2004) Multimedia adaptation for the multimedia messaging service. IEEE Communication Magazine 42(7):120–126

    Article  Google Scholar 

  12. 12.

    Coulombe S, Pigeon S (2009) Quality-aware selection of quality factor and scaling parameters in jpeg image transcoding. In: Procs. IEEE 2009 computational intelligence for multimedia, signal, and video processing (CIMSVP)

  13. 13.

    Coulombe S, Pigeon S (2010) Low-complexity transcoding of JPEG images with near-optimal quality using a predictive quality factor and scaling parameters. IEEE Trans Image Process 19(3):712–721

    Article  MathSciNet  Google Scholar 

  14. 14.

    Dieckmann A, Dippold K, Dietrich H (2009) Compensatory versus noncompensatory models for predicting consumer preferences. Judgm Decis Mak 4(3):200–213

    Google Scholar 

  15. 15.

    Dugad D, Ahuja A (2001) A fast scheme for image size change in the compressed domain. IEEE Trans Circ Syst Video Technol 11(4):461–474

    Article  Google Scholar 

  16. 16.

    Han R, Bhagwat P, LaMaire R, Mummert T, Perret V, Rubas J (1998) Dynamic adaptation in an image transcoding proxy for mobile web browsing. IEEE Pers Commun Mag 5(6):8–17

    Article  Google Scholar 

  17. 17.

    Hillier FS, Lieberman GJ (2009) Introduction to operations research, 9th edn. McGraw-Hill Science

  18. 18.

    Ishihara T (2002) The distribution of the sum and the product of independent uniform random variables distributed at different intervals. Trans Japanese Soc Ind Appl Math 12(3):197–207

    Google Scholar 

  19. 19.

    Dwyer J III (2011) MMS to prosper as mobile marketing becomes mainstream. Wireless Week

  20. 20.

    Lee L, Anderson R (2009) A comparison of compensatory and non-compensatory decision making strategies in IT project portfolio management. http://aisel.aisnet.org/irwitpm2009/9

  21. 21.

    Louafi H, Coulombe S, Chandra U (2012) Quality prediction-based dynamic content adaptation framework applied to collaborative mobile presentations. EEE Trans Mobile Comput 99:1–1. (preprints) doi:10.1109/TMC.2012.173

    Google Scholar 

  22. 22.

    Louafi H, Coulombe S, Chandra U (2013) Efficient near-optimal dynamic content adaptation applied to JPEG slides presentations in mobile web conferencing. In: Procs. IEEE international conference on advanced information networking and applications (AINA-2013), pp 724–731

  23. 23.

    Mohan R, Smith JR, Li CS (1999) Adapting multimedia internet content for universal access. IEEE Trans Multimedia 1(1):104–114

    Article  Google Scholar 

  24. 24.

    Mukherjee J, Mitra SK (2002) Image resizing in the compressed domain using subband DCT. IEEE Trans Circ Syst Video Technol 12(7):620–627

    Article  Google Scholar 

  25. 25.

    Nokia (2003) How to create MMS services. Whitepaper

  26. 26.

    Open Mobile Alliance (2007) Standard transcoding interface specification version 1.0. OMA-TS-STI-V1_0-20070515-A

  27. 27.

    Open Mobile Alliance (2011) Multimedia messaging service architecture version 1.3. OMA-AD-MMS-V1_3-20110913-A

  28. 28.

    Open Mobile Alliance (2011) Multimedia messaging service client transactions. OMA-TS-MMS_CTR-V1_3-20110913-A

  29. 29.

    Open Mobile Alliance (2011) Multimedia messaging service conformance document. OMA-TS-MMS_CONF-V1_3-20110913-A

  30. 30.

    Pannu P, Tomar Y (2010) ICT4D information communication technology for development. IK International Pvt Ltd

  31. 31.

    Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Dover

  32. 32.

    Pennebaker WB, Mitchell JL (1992) JPEG still image data compression standard. Kluwer Academic Publishers

  33. 33.

    Pigeon S, Coulombe S (2008) Computationally efficient algorithms for predicting the file size of jpeg images subject to changes of quality factor and scaling. In: Procs. 24th Queen’s University Biennial symposium on communications

  34. 34.

    Pigeon S, Coulombe S (2011) Efficient clustering-based algorithm for predicting file size and structural similarity of tanscoded JPEG images. In: Procs. IEEE international symposium on multimedia (ISM), pp 137–142

  35. 35.

    Pigeon S, Coulombe S (2011) Optimal quality-aware predictor-based adaptation of multimedia messages. In: Procs. the 6th IEEE Int. conf. on intelligent data acquisition and advanced computing systems: technology and applications, pp 496–499

  36. 36.

    Rezazadeh S, Coulombe S (2010) Low-complexity computation of visual information fidelity in the discrete wavelet domain. In: IEEE International conference onacoustics speech and signal processing (ICASSP) 2010, pp 2438–2441

  37. 37.

    Rezazadeh S, Coulombe S (2012) A novel discrete wavelet domain error-based image quality metric with enhanced perceptual performance. Int J Comput Electr Eng 4(2):390–395

    Article  Google Scholar 

  38. 38.

    Ridge J (2003) Efficient transform-domain size and resolution reduction of images. Signal Process Image Commun 18(8):621–639

    Article  Google Scholar 

  39. 39.

    Sheikh H, Bovik A, de Veciana G (2005) An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process 14(12):2117–2128

    Article  Google Scholar 

  40. 40.

    Sheikh HR, Seshadrinathan K, Moorthy AK, Wang Z, Bovik AC, Cormack LK (2013) Image and video quality assessment research at LIVE. http://live.ece.utexas.edu/research/quality. Accessed 10 February 2013

  41. 41.

    Springer MD, Thompson WE (1966) The distribution of products of independent random variables. SIAM J Appl Math 14(3):511–526

    Article  MATH  MathSciNet  Google Scholar 

  42. 42.

    Springer MD, Thompson WE (1970) The distribution of the products of beta, gamma and Gaussian random variables. SIAM J Appl Math 18(4):721–737

    Article  MATH  MathSciNet  Google Scholar 

  43. 43.

    Taubman D, Marcellin M (2002) JPEG2000: image compression fundamentals, standards and practice. Springer

  44. 44.

    Wang Z, Bovick AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  45. 45.

    Wang Z, Bovik A (2006) Modern image quality assessment. Synthesis lectures on image, video, & multimedia processing. Morgan & Claypool. http://books.google.ca/books?id=F6lYVwyZJz4C

  46. 46.

    Wang Z, Bovik AC, Simoncelli EP (2005) Structural approaches to image quality assessment, 2nd edn. Academic Press

  47. 47.

    Wang Z, Li Q (2011) Information content weighting for perceptual image quality assessment. IEEE Trans Image Process 20(5):1185–1198

    Article  MathSciNet  Google Scholar 

  48. 48.

    Yan WQ, Kankanhalli MS (2007) Multimedia simplification for optimized MMS synthesis. ACM Trans Multimed Comput Commun Appl (TOMCCAP) 3(1)

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Acknowledgements

This work was funded by Vantrix Corporation and by the Natural Sciences and Engineering Research Council of Canada under the Collaborative Research and Development Program (NSERC-CRD 428942-11).

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Correspondence to Stéphane Coulombe.

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Pigeon, S., Coulombe, S. Quality-aware predictor-based adaptation of still images for the multimedia messaging service. Multimed Tools Appl 72, 1841–1865 (2014). https://doi.org/10.1007/s11042-013-1481-1

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Keywords

  • MMS
  • Image adaptation
  • JPEG
  • Optimization
  • Predictor
  • Dynamic programming
  • SSIM