Fuzzy channel-optimized vector quantization

Wen Jyi Hwang; Faa Jeng Lin; Chin Tsai Lin

doi:10.1109/4234.898723

Fuzzy channel-optimized vector quantization

Wen Jyi Hwang, Faa Jeng Lin, Chin Tsai Lin

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data.

Original language	English
Pages (from-to)	408-410
Number of pages	3
Journal	IEEE Communications Letters
Volume	4
Issue number	12
DOIs	https://doi.org/10.1109/4234.898723
Publication status	Published - 2000 Dec
Externally published	Yes

ASJC Scopus subject areas

Modelling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/4234.898723

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title = "Fuzzy channel-optimized vector quantization",

abstract = "A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data.",

author = "Hwang, {Wen Jyi} and Lin, {Faa Jeng} and Lin, {Chin Tsai}",

note = "Funding Information: Manuscript received June 27, 2000. The associate editor coordinating the review of this letter and approving it for publication was Dr. P. Cosman. This work was supported by National Science Council of Taiwan under Grant NSC 89-2213-E-033-036.",

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AU - Hwang, Wen Jyi

AU - Lin, Faa Jeng

AU - Lin, Chin Tsai

N1 - Funding Information: Manuscript received June 27, 2000. The associate editor coordinating the review of this letter and approving it for publication was Dr. P. Cosman. This work was supported by National Science Council of Taiwan under Grant NSC 89-2213-E-033-036.

PY - 2000/12

Y1 - 2000/12

N2 - A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data.

AB - A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data.

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Fuzzy channel-optimized vector quantization

Abstract

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