Analysis of Fast Motion Estimation Algorithms

  • Peter Kuhn


A complexity and visual quality analysis of several fast motion estimation (ME) algorithms for the emerging MPEG-4 standard has been performed as a basis for HW/SW partitioning for the VLSI implementation of a portable multimedia terminal. While the computational complexity for the ME of previously standardized video coding schemes was predictable over time, the support of arbitrarily-shaped visual objects (VO), various coding options within MPEG-4, as well as content-dependent complexity (caused e.g. by summation truncation for SAD, cf. p30) now introduce content-(and therefore time-) dependent computational requirements, which cannot be determined analytically. Therefore, a new time-dependent complexity analysis method, based on statistical analysis of memory access bandwidth, arithmetic and control instruction counts utilized by a real processor, was developed (cf. chapter 3) and applied.


Motion Estimation VLSI Implementation Motion Estimation Algorithm Diamond Search Video Object Plane 
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  1. [Baek 96].
    Yunju Baek, Hwang-Soek Oh and Heung-Kyu Lee: „Block-matching critenon for efficient VLSI implementation for motion estimation“, IEE Electronics Letters, 20th June 1996, vol. 32, no. 13, Jun 1996, pp 1184–1185Google Scholar
  2. [Chen 95:1].
    Mei-Juan Chen, Liang-Gee Chen, Tzi-Dar Chiueh, Yung-Pin Lee: „A new block-matching criterion for motion estimation and its implementation“, IEEE Transactions on Circuits and Systems for Video Tech nology, vol. 5, no. 3, Jun. 1995, pp 231–236CrossRefGoogle Scholar
  3. [Cote 97].
    Guy Cote, Michael Gallant, Faouzi Kossentini: „Efficient Motion Vector Estimation and Coding for H.263-based very low bit rate video compression“, ITU-T SG 16, QI5-A-45, June 1997, 18 pGoogle Scholar
  4. [Ghar 90].
    H. Gharavi, M. Mills: „Block-Matching Motion Estimation Algonthms–New Results“, IEEE Transactions on Circuits and Systems”, Vol 37, No. 5, May 1990, p649–651CrossRefGoogle Scholar
  5. [LeeC 96].
    S. Lee and S.-I. Chae: „Motion Estimation algorithm using low resolution quantisation“, IEE Electronic Letters, vol. 32, no. 7, 28 th. Mar. 1996, pp 647–648Google Scholar
  6. [Kim 92].
    Joon-Seek Kim, Rae-Hong Park: „A fast feature-based block matching algorithm using integral projections“, IEEE Journal on Selected areas in communications, vol. 10, no. 5, Jun. 1992, pp 968–971CrossRefGoogle Scholar
  7. [Koga 81].
    T. Koga, K. linuma, A. Hirano, Y. IJima, T. Ishiguro: „Motion compensated interframe coding for video conferencing“, in Proc. NTC 81, pp. C 9.6.1–9. 6. 5Google Scholar
  8. [Kuhn 98a].
    Kuhn, P., Stechele, W.: “Complexity Analysis of the Emerging MPEG-4 Standard as a Basis for VLSI Implementation”, vol. SPIE 3309 Visual Communications and Image Processing, San Jose, Jan. 1998, pp. 498–509Google Scholar
  9. [Kuhn 98b].
    Kuhn P., et al.: “Complexity and PSNR-Comparison of several Fast Motion Estimation Algorithms for MPEG-4”, vol. SPIE 3460 Applications of Digital Image Processing XXI, San Diego, July 1998, pp 486–499Google Scholar
  10. [Liu 93:1].
    B. Liu, A. Zaccann: „New Fast Algorithms for the Estimation of Block Motion Vectors“, IEEE Trans. on Circuits and Systems for Video Technology”, Vol. 3, No. 2, April 1993, pp 148–157CrossRefGoogle Scholar
  11. [Nam 95].
    Kwon Moon Nam, Joon-Seek Kim Rae-Hong Park, Young Serk Shim: „A fast hierarchical motion vector estimation algorithm using mean pyramid“, IEEE Transactions on Circuits and Systems for Video Technology, vol. 5, no. 4, Aug. 1995, pp 344–351CrossRefGoogle Scholar
  12. [Nat 97].
    Balas Natarajan, Vasudev Bhaskaran, Konstantmos Konstantinides: „Low-Complexity Block-based motion estimation via One-Bit Transforms“, IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 4, Aug. 1997, pp 702–706CrossRefGoogle Scholar
  13. [MomVM].
    ISO/IEC JTC1/SC29/WG11/MPEG97/M2915: „Momusys Implementation of the VM (VM8971021)“, Fribourg, October 1997Google Scholar
  14. [M 3551].
    Kuhn P.: „A Complexity Analysis Tool. iprof (version 0.41)“, ISO/iEC JTC1/SC29/WG11/M3551, Dublin, July 1998Google Scholar
  15. [M 3204].
    Kuhn P.: „Complexity Analysis of single video tools of the MPEG-4 verification Model“, ISO/IEC JTC1/SC29/WGI 1/M3204, San Jose, USA, Jan. 1998Google Scholar
  16. [M 2863].
    Kuhn P.: „A Complexity Analysis Tool: iprof (version 0.3)“, ISO/IEC JTC1/SC29/WG11/M2863, Fribourg ( CH ), Switzerland, October 1997Google Scholar
  17. [M 1056].
    Kuhn, P.:,,A portable Instruction Level Profiler for Complexity Analysis - Software“, ISO/i EC JTCI/SC29/WG11 MPEG96/M1056, Tampere, Finland, 1996Google Scholar
  18. [M 0921].
    Kuhn, P.: „A portable Instruction Level Profiler for Complexity Analysis Documentation“, ISO/IEC JTC1/SC29/WG1 I MPEG96/M0921, Tampere, Finland, 1996Google Scholar
  19. [M 0838].
    Kuhn, P.: „Instrumentation Tools and Methods for MPEG-4 VM: Review and a new Proposal“, ISO/IEC JTCI/SC29/WGI 1 MPEG96/M0838, Firence, Italy, 1996Google Scholar

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© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Peter Kuhn
    • 1
  1. 1.Technical University of MunichGermany

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