Localized Video Compression for Machine Vision

  • Moshe Porat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1998)


A three-dimensional vector quantization system is introduced suitable for video compression. The basic characteristics of slow or repeated scenes in robot vision are used as the basic assumptions of the proposed approach. Accordingly, the localized history of the sequence is used to create localized codebooks, thus representing current visual information as transformed versions of previous details. The results indicate a high compression ratio with high quality of the perceived sequence. The structure of the algorithm is mostly parallel, making it suitable for efficient hardware implementation.


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  1. 1.
    IEEE Trans. Image Processing, Special Issue on Sequence Coding, (September 1994). 278Google Scholar
  2. 2.
    C. S. Choi, H. Harashima, T. Takebe: Analysis and synthesis of facial expressions in knowledge-based coding of facial image sequences. IEEE ICASSP (1991). 278Google Scholar
  3. 3.
    M. Kunt, A. Ikonomopoulos, M. Kocher: Second-generation image-coding techniques. Proc. of the IEEE, 73 (1985) 549–573. 278CrossRefGoogle Scholar
  4. 4.
    C. I Podilchuk, N. S. Jayant, P. Noll: Sparse codebooks for the quantization of nondominant sub-bands in image coding. IEEE ICASSP, (1990) 2101–2104. 278Google Scholar
  5. 5.
    G. R. Giunta, T.R. Reed, M. Kunt: Image sequence coding using oriented edges. Image Communication, 2 (1990) 429–440. 278Google Scholar
  6. 6.
    MC. I. Podilchuk, N. S. Jayant, N. Farvardin: 3-D subband coding of video. IEEE Trans. on Image Processing, 4 (1995) 125–139. 278CrossRefGoogle Scholar
  7. 7.
    J.W. Woods, S.D. O’Neil: Subband coding of images. IEEE Trans. on Signal Processing, ASSP-34 (1986) 1278–1288. 278Google Scholar
  8. 8.
    M. Porat, Y.Y. Zeevi: The generalized gabor scheme in biological and machine vision. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-10 (1988) 452–468. 278CrossRefGoogle Scholar
  9. 9.
    N. Katzir, M. Lindenbaum, M. Porat: Curve segmentation under partial occlusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-16 (1994) 513–519. 279CrossRefGoogle Scholar
  10. 10.
    M. Porat, Y.Y. Zeevi: Localized Texture processing in vision: analysis and synthesis in the gaborian space. IEEE Trans. on Biomedical Engineering, BME-36 (1989) 115–129. 279CrossRefGoogle Scholar
  11. 11.
    Y. L. Linde, A. Buzo, R.M. Gray: An algorithm for vector quantizer design. IEEE Trans. on Communication, 28 (1980) 84–95. 279CrossRefGoogle Scholar
  12. 12.
    S. Panchanathan, M. Goldberg: Adaptive algorithms for image coding using vector quantization. Signal processing: Image Communication 4, (1991) 81–92. 282CrossRefGoogle Scholar
  13. 13.
    M. Goldberg, H.-F. Sun: Image sequence coding by three-dimensional block vector quantization. IEEE Proceedings, 133, Pt. F, No. 5 (1986) 482–486. 282Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Moshe Porat
    • 1
  1. 1.Department of Electrical EngineeringTechnion-Israel Institute of TechnologyHaifaIsrael

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