### Abstract

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.

Original language | English |
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Pages | 1715-1721 |

Number of pages | 7 |

Publication status | Published - 1998 Dec 1 |

Event | Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration - Sydney, NSW, Aust Duration: 1998 Nov 8 → 1998 Nov 12 |

### Conference

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

Period | 98/11/8 → 98/11/12 |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Global and Planetary Change

### Cite this

*Entropy-constrained vector quantizer design algorithm using competitive learning technique*. 1715-1721. Paper presented at 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.

Research output: Contribution to conference › Paper

}

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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M3 - Paper

AN - SCOPUS:0032260006

SP - 1715

EP - 1721

ER -