Multimedia Systems

, Volume 9, Issue 4, pp 353–364 | Cite as

A visual attention model for adapting images on small displays

  • Li-Qun Chen
  • Xing Xie
  • Xin Fan
  • Wei-Ying Ma
  • Hong-Jiang Zhang
  • He-Qin Zhou


Image adaptation, one of the essential problems in adaptive content delivery for universal access, has been actively explored for some time. Most existing approaches have focused on generic adaptation with a view to saving file size under constraints in client environment and have hardly paid attention to user perceptions of the adapted result. Meanwhile, the major limitation on the user’s delivery context is moving away from data volume (or time-to-wait) to screen size because of the galloping development of hardware technologies. In this paper, we propose a novel method for adapting images based on user attention. A generic and extensible image attention model is introduced based on three attributes (region of interest, attention value, and minimal perceptible size) associated with each attention object. A set of automatic modeling methods are presented to support this approach. A branch-and-bound algorithm is also developed to find the optimal adaptation efficiently. Experimental results demonstrate the usefulness of the proposed scheme and its potential application in the future.


Image adaptation Attention model Region-of-interest Attention value Minimal perceptible size Information fidelity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chandra S, Gehani A, Ellis CS, Vahdat A (2001) Transcoding characteristics of Web images. Proc SPIE (Multimedia Comput Network 2001) 4312:135-149Google Scholar
  2. 2.
    Chen JL, Zhou BY, Shi J, Zhang HJ, Wu QF (2001) Function-based object model towards website adaptation. In: Proceedings of the 10th international World Wide Web conference, Hong Kong, May 2001, pp 587-596Google Scholar
  3. 3.
    Chen XR, Zhang HJ (2001) Text area detection from video frames. In: Proceedings of the 2nd IEEE Pacific-Rim conference on multimedia (PCM2001), Beijing, October 2001, pp 222-228Google Scholar
  4. 4.
    Christopoulos C, Skodras A, Ebrahimi T (2000) The JPEG2000 still image coding system: an overview. IEEE Trans Consumer Electron 46(4):1103-1127Google Scholar
  5. 5.
    Fan X, Xie X, Ma WY, Zhang HJ, Zhou HQ (2003) Visual attention based image browsing on mobile devices. In: Proceedings of the IEEE international conference on multimedia and expo (ICME 03), Baltimore, July 2003Google Scholar
  6. 6.
    Fox A, Gribble S, Brewer EA, Amir E (1996) Adapting to network and client variability via on-demand dynamic distillation. In: Proceedings of the 7th international conference on architectural support for programming languages and operating systems. Cambridge, MA, October 1996, pp 160-170Google Scholar
  7. 7.
    Han R, Bhagwat P, Lamaire R, Mummert T, Perret V, Rubas J (1998) Dynamic adaptation in an image transcoding proxy for mobile Web access. IEEE Pers Commun 5(6):8-17Google Scholar
  8. 8.
    ISO/IEC JTC1/SC29/WG11/N4242 (2001) ISO/IEC 15938-5 FDIS Information technology - multimedia content description interface - Part 5: Multimedia description schemes. Sydney, July 2001Google Scholar
  9. 9.
    ISO/IEC JTC1/SC29/WG11/N4674 (2002) MPEG-7 Overview. Jeju, Korea, March 2002Google Scholar
  10. 10.
    ISO/IEC JTC1/SC29/WG11/N4819 (2002) MPEG-21 Digital item adaptation. Fairfax, VA, May 2002Google Scholar
  11. 11.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Analysis Mach Intell 20(11):1254-1259Google Scholar
  12. 12.
    Itti L, Koch C (1999) A comparison of feature combination strategies for saliency-based visual attention system. Proc SPIE (Hum Vis Electron Imag IV) 3644:473-482Google Scholar
  13. 13.
    Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2(3):194-203Google Scholar
  14. 14.
    Lee K, Chang HS, Chun SS, Choi L, Sull S (2001) Perception-based image transcoding for universal multimedia access. In: Proceedings of the 8th international conference on image processing (ICIP-2001), Thessaloniki, Greece, October 2001, 2:475-478Google Scholar
  15. 15.
    Li SZ, Zhu L, Zhang ZQ, Blake A, Zhang HJ, Shum H (2002) Statistical learning of multi-view face detection. In: Proceedings of the 7th European conference on computer vision (ECCV 2002), Copenhagen, May 2002, 4:67-81Google Scholar
  16. 16.
    Ma WY, Bedner I, Chang G, Kuchinsky A, Zhang HJ (2000) A framework for adaptive content delivery in heterogeneous network environments. Proc SPIE (Multimedia Comput Network 2000) 3969:86-100Google Scholar
  17. 17.
    Ma YF, Lu L, Zhang HJ, Li MJ (2002) A user attention model for video summarization. In: Proceedings of the 10th ACM international conference on multimedia, Juan-les-Pins, France, December 2002, pp 533-542Google Scholar
  18. 18.
    Mohan R, Smith JR, Li CS (1999) Adapting multimedia internet content for universal access. IEEE Trans Multimedia 1(1):104-114Google Scholar
  19. 19.
    Salah AA, Alpaydin E, Akarun L (2002) A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans Pattern Analysis Mach Intell 24(3):420-425 Google Scholar
  20. 20.
    Smith JR, Mohan R, Li CS (1998) Content-based transcoding of images in the Internet. In: Proceedings of the 5th international conference on image processing (ICIP-98), Chicago, October 1998, 3:7-11Google Scholar
  21. 21.
    World Wide Web Consortium (1999) Web content accessibility guidelines 1.0. May 1999, Scholar

Copyright information

© Springer-Verlag Berlin/Heidelberg 2003

Authors and Affiliations

  • Li-Qun Chen
    • 1
  • Xing Xie
    • 2
  • Xin Fan
    • 1
  • Wei-Ying Ma
    • 2
  • Hong-Jiang Zhang
    • 2
  • He-Qin Zhou
    • 1
  1. 1.Dept. of AutomationUniversity of Science and Technology of ChinaHefeiP.R. China
  2. 2.5/F Sigma CenterMicrosoft Research AsiaBeijingP.R. China

Personalised recommendations