### 摘要

A novel full-search variable-rate vector quantizer (VQ) design algorithm using competitive learning technique is presented. The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can design a VQ having minimum average distortion subject to a rate constraint. The ECCL algorithm enjoys a better rate-distortion performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) design algorithm subject to the same rate and storage size constraints. In addition, the learning algorithm is more insensitive to the selection of initial codewords as compared with the ECVQ algorithm. Therefore, the ECCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

原文 | 英語 |
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頁面 | 1715-1721 |

頁數 | 7 |

出版狀態 | 已發佈 - 1998 十二月 1 |

事件 | Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration - Sydney, NSW, Aust 持續時間: 1998 十一月 8 → 1998 十一月 12 |

### 會議

會議 | Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration |
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城市 | Sydney, NSW, Aust |

期間 | 98/11/8 → 98/11/12 |

### 指紋

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Global and Planetary Change

### 引用此文

*Entropy-constrained vector quantizer design algorithm using competitive learning technique*. 1715-1721. 論文發表於 Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration, Sydney, NSW, Aust, .

**Entropy-constrained vector quantizer design algorithm using competitive learning technique.** / Hwang, Wen Jyi; Leou, Maw Rong; Ye, Bo Yuan; Liao, Shi Chiang.

研究成果: 會議貢獻類型 › 紙

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TY - CONF

T1 - Entropy-constrained vector quantizer design algorithm using competitive learning technique

AU - Hwang, Wen Jyi

AU - Leou, Maw Rong

AU - Ye, Bo Yuan

AU - Liao, Shi Chiang

PY - 1998/12/1

Y1 - 1998/12/1

N2 - A novel full-search variable-rate vector quantizer (VQ) design algorithm using competitive learning technique is presented. The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can design a VQ having minimum average distortion subject to a rate constraint. The ECCL algorithm enjoys a better rate-distortion performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) design algorithm subject to the same rate and storage size constraints. In addition, the learning algorithm is more insensitive to the selection of initial codewords as compared with the ECVQ algorithm. Therefore, the ECCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

AB - A novel full-search variable-rate vector quantizer (VQ) design algorithm using competitive learning technique is presented. The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can design a VQ having minimum average distortion subject to a rate constraint. The ECCL algorithm enjoys a better rate-distortion performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) design algorithm subject to the same rate and storage size constraints. In addition, the learning algorithm is more insensitive to the selection of initial codewords as compared with the ECVQ algorithm. Therefore, the ECCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

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