The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 가변 속도 벡터 양자화기(VQ) 설계를 위한 새로운 경쟁 학습 알고리즘을 제시합니다. VRCL(가변 속도 경쟁 학습) 알고리즘이라고 하는 이 알고리즘은 속도 제약 조건에 따라 최소 평균 왜곡을 갖는 VQ를 설계합니다. VRCL은 웨이블릿 영역에서 가중치 벡터 훈련을 수행하므로 필요한 훈련 시간이 짧다. 또한 이 알고리즘은 기존의 다른 VQ 설계 알고리즘 및 경쟁 학습 알고리즘보다 더 나은 속도 왜곡 성능을 누리고 있습니다. 학습 알고리즘은 기존 설계 알고리즘에 비해 초기 코드워드 선택에 더 둔감합니다. 따라서 VRCL 알고리즘은 신호 압축 응용을 위한 기존 가변 속도 VQ 설계 알고리즘에 대한 효과적인 대안이 될 수 있습니다.
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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
부
@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},}
부
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 -