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JACIII Vol.11 No.10 pp. 1189-1196
doi: 10.20965/jaciii.2007.p1189
(2007)

Paper:

Effective Multiple Vector Quantization for Image Compression

Noritaka Shigei*, Hiromi Miyajima*, Michiharu Maeda**,
and Lixin Ma***

*Dept. of Electrical and Electronics Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima-shi 890-0065, Japan

**Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan

***University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China

Received:
October 29, 2006
Accepted:
June 30, 2007
Published:
December 20, 2007
Keywords:
vector quantization, image compression, competitive learning, multiple codebooks, compression rate
Abstract

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: “index inference” and “ranking based index coding.” It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.

Cite this article as:
Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda, and
and Lixin Ma, “Effective Multiple Vector Quantization for Image Compression,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.10, pp. 1189-1196, 2007.
Data files:
References
  1. [1] Y. Linde, A. Buzo, and R. M. Gray, “An Algorithm for Vector Quantizer Design,” IEEE Trans. Commun., Vol.28, pp. 84-95, 1980.
  2. [2] A. Gersho and R. M. Gray, “Vector Quantization and Signal Compression,” Kluwer, Boston, 1992.
  3. [3] T. M. Martinetz, S. G. Berkovich, and K. J. Schulten, “Neural Gas Network for Vector Quantization and Its Application to Time-Series Prediction,” IEEE Trans. Neural Networks, Vol.4, pp. 558-569, 1993.
  4. [4] T. Kohonen, “Self-Organizing Maps,” Springer-Verlag, Berlin Heidelberg, New York, 1997.
  5. [5] P. C. Cosman, R. M. Gray, and M. Vetterli, “Vector Quantization of Image Subbands: A survey,” IEEE Trans. Image Processing, Vol.5, pp. 202-225, 1996.
  6. [6] J. Jiang, “Image Compression with Neural Networks – A Survey,” Signal Processing: Image Comm., Vol.14, pp. 737-760, 1999.
  7. [7] C. Amerijckx, J.-D. Legat, and M. Verleysen, “Image Compression Using Self-Organizing Maps,” Systems Analysis Modelling Simulation, Vol.43, pp. 1529-1543, 2003.
  8. [8] S. Momose, K. Sano, and T. Nakamura, “Fast Codebook Design for Vector Quantization on Partitioned Space,” Proc of the 2nd Int. Conf. on Information Technology for Application, pp. 205-210, 2004.
  9. [9] N. Shigei, H. Miyajima, and M. Maeda, “Numerical Evaluation of Incremental Vector Quantization Using Stochastic Relaxation,” IEICE Trans. Fundamentals, Vol.E87-A, pp. 2364-2371, 2004.
  10. [10] N. Shigei, H. Miyajima, and M. Maeda, “A Multiple Vector Quantization Approach to Image Compression,” Proc of the 1st Int. Conf. on Natural Computation, 2005.

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Last updated on Oct. 20, 2021