Adaptive vector quantizer for image compression using self-organization approach

Oscal T.C. Chen; Bing J. Sheu; Wai Chi Fang

doi:10.1109/ICASSP.1992.226039

Adaptive vector quantizer for image compression using self-organization approach

Oscal T.C. Chen
, Bing J. Sheu
, Wai Chi Fang

University of Southern California

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.

Original language	English
Title of host publication	ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	385-388
Number of pages	4
ISBN (Electronic)	0780305329
DOIs	https://doi.org/10.1109/ICASSP.1992.226039
State	Published - 1992
Externally published	Yes
Event	1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States Duration: 23 03 1992 → 26 03 1992

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
ISSN (Print)	1520-6149

Conference

Conference	1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
Country/Territory	United States
City	San Francisco
Period	23/03/92 → 26/03/92

Bibliographical note

Publisher Copyright:
© 1992 IEEE.

Access to Document

10.1109/ICASSP.1992.226039

Cite this

Chen, O. T. C., Sheu, B. J., & Fang, W. C. (1992). Adaptive vector quantizer for image compression using self-organization approach. In ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing (pp. 385-388). Article 226039 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.1992.226039

Chen, Oscal T.C. ; Sheu, Bing J. ; Fang, Wai Chi. / Adaptive vector quantizer for image compression using self-organization approach. ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing. Institute of Electrical and Electronics Engineers Inc., 1992. pp. 385-388 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{463d32a4be984864b27a2e41b5de2814,

title = "Adaptive vector quantizer for image compression using self-organization approach",

abstract = "A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.",

author = "Chen, \{Oscal T.C.\} and Sheu, \{Bing J.\} and Fang, \{Wai Chi\}",

note = "Publisher Copyright: {\textcopyright} 1992 IEEE.; 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 ; Conference date: 23-03-1992 Through 26-03-1992",

year = "1992",

doi = "10.1109/ICASSP.1992.226039",

language = "英语",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "385--388",

booktitle = "ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing",

address = "美国",

}

Chen, OTC, Sheu, BJ & Fang, WC 1992, Adaptive vector quantizer for image compression using self-organization approach. in ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing., 226039, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2, Institute of Electrical and Electronics Engineers Inc., pp. 385-388, 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992, San Francisco, United States, 23/03/92. https://doi.org/10.1109/ICASSP.1992.226039

Adaptive vector quantizer for image compression using self-organization approach. / Chen, Oscal T.C.; Sheu, Bing J.; Fang, Wai Chi.
ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing. Institute of Electrical and Electronics Engineers Inc., 1992. p. 385-388 226039 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive vector quantizer for image compression using self-organization approach

AU - Chen, Oscal T.C.

AU - Sheu, Bing J.

AU - Fang, Wai Chi

PY - 1992

Y1 - 1992

N2 - A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.

AB - A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.

UR - https://www.scopus.com/pages/publications/84947661209

U2 - 10.1109/ICASSP.1992.226039

DO - 10.1109/ICASSP.1992.226039

M3 - 会议稿件

AN - SCOPUS:84947661209

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 385

EP - 388

BT - ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992

Y2 - 23 March 1992 through 26 March 1992

ER -

Chen OTC, Sheu BJ, Fang WC. Adaptive vector quantizer for image compression using self-organization approach. In ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing. Institute of Electrical and Electronics Engineers Inc. 1992. p. 385-388. 226039. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.1992.226039

Adaptive vector quantizer for image compression using self-organization approach

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