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Low Complexity Video Compression Using Moving Edge Detection Based on DCT Coefficients

  • Chanyul Kim
  • Noel E. O’Connor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges using only DCT coefficients. The detection, whilst being very efficient, also allows efficient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB compared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method finds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera.

Keywords

Low complexity video compression Moving edge DCT 

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References

  1. 1.
    14496-10, H.: Advanced video coding. Technical report, ITU-T (2003)Google Scholar
  2. 2.
    Tourapis, H.-Y.C., Tourapis, A.M.: Fast motion estimation within the h.264 codec. In: Proceedings of International Conference on Multimedia and Expo, 2003. ICME 2003, vol. 3, pp. 517–520 (July 2003)Google Scholar
  3. 3.
    gon Kim, D., jung Yoo, C., bae Chang, O., mi Kim, E., Choi, J.R.: Improved fast mode decision algorithm for variable macro block motion compensation in h.264. In: International Symposium on Information Technology Convergence, 2007. ISITC 2007, Joenju, pp. 184–187 (November 2007)Google Scholar
  4. 4.
    Wong, H.M., Au, O.C., Chang, A., Yip, S.K., Ho, C.W.: Fast mode decision and motion estimation for h.264 (FMDME). In: IEEE Proceedings of International Symposium on Circuits and Systems, 2006. ISCAS 2006 (May 2006)Google Scholar
  5. 5.
    Hiratsuka, S., Goto, S., Baba, T., Ikenaga, T.: Video coding algorithm based on adaptive tree for low electricity consumption. In: Proceedings of the 2004 IEEE Asia-Pacific Conference on Circuits and Systems, 2004, vol. 1, pp. 5–8 (December 2004)Google Scholar
  6. 6.
    Magli, E., Mancin, M., Merello, L.: Low-complexity video compression for wireless sensor networks. In: Proceedings of 2003 International Conference on Multimedia and Expo, 2003. ICME 2003, vol. 3, pp. 585–588 (July 2003)Google Scholar
  7. 7.
    Sriram Sankaran, R.A., Khokhar, A.A.: Adaptive multifoveation for low-complexity video compression with a stationary camera perspective. In: Proc. SPIE, vol. 5685, p. 1007 (2005)Google Scholar
  8. 8.
    Wyner, A., Ziv, J.: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 22, 1–11 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19, 471–480 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Puri, R., Ramchandran, K.: Prism: A new robust video coding architecture based on distributed compression principles. In: Proc. Allerton Conf. (October 2002)Google Scholar
  11. 11.
    Aaron, A., Rane, S., Zhang, R., Girod, B.: Wyner-ziv coding for video: applications to compression and error resilience. In: Proceedings of Data Compression Conference, 2003. DCC 2003, pp. 93–102 (March 2003)Google Scholar
  12. 12.
    Aaron, A., Rane, S., Setton, E., Girod, B.: Transform-domain wyner-ziv codec for video. In: Proceedings of SPIE Visual Communications and Image Processing Conference, San Jose, USA (January 2004)Google Scholar
  13. 13.
    Artigas, X., Ascenso, J., Dalai, M., Klomp, S., Kubasov, D., Ouaret, M.: The discover codec: Architecture, techniques and evaluation. In: Picture Coding Symposium, Lisbon, Portugal (2007)Google Scholar
  14. 14.
    Chi, M.C., Chen, M.J., Yeh, C.H., Jhu, J.A.: Region-of-interest video coding based on rate and distortion variations for h.263+. Image Commun. 23(2), 127–142 (2008)Google Scholar
  15. 15.
    Kim, C., O’Connor, N.E.: Low complexity intra video coding using transform domain prediction. In: International Conference on Visualization, Imaging, and Image Processing, Palma de Mallorca, Spain (2008)Google Scholar
  16. 16.
    Labit, C., Marescq, J.P.: Temporal adaptive vector quantization for image sequence coding. In: SPIE, Advances in Image Processing, Hague, Netherlands, vol. 804 (April 1989)Google Scholar
  17. 17.
    Kim, C., O’Connor, N.E.: Reducing complexity and memory accesses in motion compensation interpolation in video codecs. In: China-Ireland International Conference on Information and Communications (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chanyul Kim
    • 1
  • Noel E. O’Connor
    • 1
  1. 1.CLARITY: Centre for Sensor Web TechnologiesDublin City University, GlasnevinDublinIreland

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