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Transform Vector Quantization of Images in One Dimension

  • Remigiusz J. Rak
Conference paper
  • 585 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)

Abstract

In this paper there is enclosed a description of the hybrid system for black and white (256 by 256 pixels) images compression. The system includes the following procedures: image decomposition (8×8, 16×16 blocks), DCT transformation, “zig-zag” scanning, product code vector quantization (one- dimensional block) and a bit allocation. The standard LBG algorithm for codebook design has been enriched with the simulated annealing procedure for avoiding the local minima. Standard vector quantization in two-dimensional transform space has been investigated for comparison.

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References

  1. 1.
    Abdelwahab, A.A., Kwatra, S.C.: Image Data Compression with Vector Quantization in the Transform Domain. IEEE Int. Conf. on Commun. ICC’86, Toronto, 1986Google Scholar
  2. 2.
    Aizawa, K., Harashima, H., Miakawa, H.: Adaptive Discrete Cosine Transform Coding with Vector Quantization for Color Images. Proc. ICASSP’ 86, Tokyo, Japan, 1986Google Scholar
  3. 3.
    Aizawa, K., Harashima, H., Miakawa, H.: Adaptive Discrete Cosine Transform Coding with Vector Quantization. PCS’86 Picture Coding Symp., Tokyo, Japan, 1986Google Scholar
  4. 4.
    Bellifemine, F., Picco, R.: 2D-DCT coding with Pyramidal Vector Quantization. Picture Coding Symp., Torino, Italy, 1988Google Scholar
  5. 5.
    Cho, N.I., Lee, S.U.: A fast 4×4 DCT for the recursive 2-D DCT. IEEE Trans. Sign. Processing vol.40, Sept.1992Google Scholar
  6. 6.
    Clarke, R.J.: Digital Compression of Still Images and Video. Academic Press, 1996Google Scholar
  7. 7.
    Flanagan, J.K., Morrell, D.R., Frost, R.L., Read, C.J., Nelson, B.E.: Vector Quantization Codebook Generation Using Simulated Annealing. ICASSP, Glasgow, Scotland, May 1989Google Scholar
  8. 8.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers,1992Google Scholar
  9. 9.
    Gotze, M.: Adaptive Vector Quantization of Images in the Discrete Cosine Transform Domain. Picture Coding Symp. PCS’86, Tokyo, Japan, 1986Google Scholar
  10. 10.
    Marescq, J.P., Labit, C.: Vector Quantization in Transformed Image Coding. Int. Conf. on Acoust. Speech and Sgn. Proc., ICASSP’86, Tokyo, Japan, 1986.Google Scholar
  11. 11.
    Rabbani, M., Jones P.W.: Digital Image Compression Techniques. SPIE Optical Engineering Press, 1991Google Scholar
  12. 12.
    Rak, R.J.: Signal Compression based on Fourier Transform Vector Quantization. Mediterranean Electrotechnical Conf. MELECON’94, Antalya, Turkee, 1994Google Scholar
  13. 13.
    Rak, R.J.: A System For Transform Vector Coding of Images. 3rd International Conference on Signal Processing ICSP’96, Bejjing, China, 1996Google Scholar
  14. 14.
    Rak, R.J.: Wavelet Transform Vector Quantization of Images. 13th International Conference on Signal Processing DSP97, Santorini, Greece, 1997Google Scholar
  15. 15.
    Rao, K.R., Yip, P.: Discrete Cosine Transform. Academic Press 1990.Google Scholar
  16. 16.
    Saito, T., Takeo, H., Aizawa, K., Harashima, H., Miyakawa, H.: Discrete Cosinte Transform Coding System Using Gain/Shape Vector Quantizers and its application to Image Coding. Picture Coding Symposium, PCS’86, Tokyo, Japan, 1986Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Remigiusz J. Rak
    • 1
  1. 1.Warsaw University of TechnologyWarsawPoland

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