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This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.9 pp.1781-1789

- Publication Date
- 2000/09/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Image Processing, Image Pattern Recognition

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Wen-Jyi HWANG, Maw-Rong LEOU, Shih-Chiang LIAO, Chienmin OU, "A Novel Competitive Learning Technique for the Design of Variable-Rate Vector Quantizers with Reproduction Vector Training in the Wavelet Domain" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 9, pp. 1781-1789, September 2000, doi: .

Abstract: This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_9_1781/_p

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@ARTICLE{e83-d_9_1781,

author={Wen-Jyi HWANG, Maw-Rong LEOU, Shih-Chiang LIAO, Chienmin OU, },

journal={IEICE TRANSACTIONS on Information},

title={A Novel Competitive Learning Technique for the Design of Variable-Rate Vector Quantizers with Reproduction Vector Training in the Wavelet Domain},

year={2000},

volume={E83-D},

number={9},

pages={1781-1789},

abstract={This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.},

keywords={},

doi={},

ISSN={},

month={September},}

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TY - JOUR

TI - A Novel Competitive Learning Technique for the Design of Variable-Rate Vector Quantizers with Reproduction Vector Training in the Wavelet Domain

T2 - IEICE TRANSACTIONS on Information

SP - 1781

EP - 1789

AU - Wen-Jyi HWANG

AU - Maw-Rong LEOU

AU - Shih-Chiang LIAO

AU - Chienmin OU

PY - 2000

DO -

JO - IEICE TRANSACTIONS on Information

SN -

VL - E83-D

IS - 9

JA - IEICE TRANSACTIONS on Information

Y1 - September 2000

AB - This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

ER -