Fuzzy channel-optimized vector quantization for image coding

Wen Jyi Hwang, Chin Tsai Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel vector quantizer (VQ) design algorithm for noisy channels is presented in this paper. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, performs the codeword training using an optimal fuzzy clustering technique where the channel noise is taken into account. In the existing crisp channel-optimized VQ (CCOVQ) design algorithms, the quantization accuracy is traded for less sensitivity to channel noise. However, because of utilizing the optimal fuzzy clustering process for VQ design, the FCOVQ algorithm can effectively reduce the sensitivity to channel noise while maintaining the quantization accuracy. Therefore, given the same noisy channel, the FCOVQ can have better rate-distortion performance than that of the CCOVQ techniques.

Original languageEnglish
Title of host publicationImage and Multidimensional Signal ProcessingMultimedia Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1895-1898
Number of pages4
Volume4
ISBN (Electronic)0780362934
DOIs
Publication statusPublished - 2000 Jan 1
Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
Duration: 2000 Jun 52000 Jun 9

Other

Other25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
CountryTurkey
CityIstanbul
Period00/6/500/6/9

Fingerprint

Vector quantization
Image coding
Fuzzy clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Hwang, W. J., & Lin, C. T. (2000). Fuzzy channel-optimized vector quantization for image coding. In Image and Multidimensional Signal ProcessingMultimedia Signal Processing (Vol. 4, pp. 1895-1898). [859198] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2000.859198

Fuzzy channel-optimized vector quantization for image coding. / Hwang, Wen Jyi; Lin, Chin Tsai.

Image and Multidimensional Signal ProcessingMultimedia Signal Processing. Vol. 4 Institute of Electrical and Electronics Engineers Inc., 2000. p. 1895-1898 859198.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hwang, WJ & Lin, CT 2000, Fuzzy channel-optimized vector quantization for image coding. in Image and Multidimensional Signal ProcessingMultimedia Signal Processing. vol. 4, 859198, Institute of Electrical and Electronics Engineers Inc., pp. 1895-1898, 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000, Istanbul, Turkey, 00/6/5. https://doi.org/10.1109/ICASSP.2000.859198
Hwang WJ, Lin CT. Fuzzy channel-optimized vector quantization for image coding. In Image and Multidimensional Signal ProcessingMultimedia Signal Processing. Vol. 4. Institute of Electrical and Electronics Engineers Inc. 2000. p. 1895-1898. 859198 https://doi.org/10.1109/ICASSP.2000.859198
Hwang, Wen Jyi ; Lin, Chin Tsai. / Fuzzy channel-optimized vector quantization for image coding. Image and Multidimensional Signal ProcessingMultimedia Signal Processing. Vol. 4 Institute of Electrical and Electronics Engineers Inc., 2000. pp. 1895-1898
@inproceedings{658bb0b71c6a4cb2aba5fdfb85e9bb81,
title = "Fuzzy channel-optimized vector quantization for image coding",
abstract = "A novel vector quantizer (VQ) design algorithm for noisy channels is presented in this paper. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, performs the codeword training using an optimal fuzzy clustering technique where the channel noise is taken into account. In the existing crisp channel-optimized VQ (CCOVQ) design algorithms, the quantization accuracy is traded for less sensitivity to channel noise. However, because of utilizing the optimal fuzzy clustering process for VQ design, the FCOVQ algorithm can effectively reduce the sensitivity to channel noise while maintaining the quantization accuracy. Therefore, given the same noisy channel, the FCOVQ can have better rate-distortion performance than that of the CCOVQ techniques.",
author = "Hwang, {Wen Jyi} and Lin, {Chin Tsai}",
year = "2000",
month = "1",
day = "1",
doi = "10.1109/ICASSP.2000.859198",
language = "English",
volume = "4",
pages = "1895--1898",
booktitle = "Image and Multidimensional Signal ProcessingMultimedia Signal Processing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Fuzzy channel-optimized vector quantization for image coding

AU - Hwang, Wen Jyi

AU - Lin, Chin Tsai

PY - 2000/1/1

Y1 - 2000/1/1

N2 - A novel vector quantizer (VQ) design algorithm for noisy channels is presented in this paper. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, performs the codeword training using an optimal fuzzy clustering technique where the channel noise is taken into account. In the existing crisp channel-optimized VQ (CCOVQ) design algorithms, the quantization accuracy is traded for less sensitivity to channel noise. However, because of utilizing the optimal fuzzy clustering process for VQ design, the FCOVQ algorithm can effectively reduce the sensitivity to channel noise while maintaining the quantization accuracy. Therefore, given the same noisy channel, the FCOVQ can have better rate-distortion performance than that of the CCOVQ techniques.

AB - A novel vector quantizer (VQ) design algorithm for noisy channels is presented in this paper. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, performs the codeword training using an optimal fuzzy clustering technique where the channel noise is taken into account. In the existing crisp channel-optimized VQ (CCOVQ) design algorithms, the quantization accuracy is traded for less sensitivity to channel noise. However, because of utilizing the optimal fuzzy clustering process for VQ design, the FCOVQ algorithm can effectively reduce the sensitivity to channel noise while maintaining the quantization accuracy. Therefore, given the same noisy channel, the FCOVQ can have better rate-distortion performance than that of the CCOVQ techniques.

UR - http://www.scopus.com/inward/record.url?scp=0033708489&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033708489&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2000.859198

DO - 10.1109/ICASSP.2000.859198

M3 - Conference contribution

AN - SCOPUS:0033708489

VL - 4

SP - 1895

EP - 1898

BT - Image and Multidimensional Signal ProcessingMultimedia Signal Processing

PB - Institute of Electrical and Electronics Engineers Inc.

ER -