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 |
|---|---|
| Pages (from-to) | 1715-1721 |
| Number of pages | 7 |
| Journal | Conference Record / IEEE Global Telecommunications Conference |
| Volume | 3 |
| Publication status | Published - 1998 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Global and Planetary Change
Cite this
Entropy-constrained vector quantizer design algorithm using competitive learning technique. / Hwang, Wen Jyi; Leou, Maw Rong; Ye, Bo Yuan; Liao, Shi Chiang.
In: Conference Record / IEEE Global Telecommunications Conference, Vol. 3, 1998, p. 1715-1721.Research output: Contribution to journal › Article
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TY - JOUR
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
Y1 - 1998
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 - Article
AN - SCOPUS:0032260006
VL - 3
SP - 1715
EP - 1721
JO - Conference Record / IEEE Global Telecommunications Conference
JF - Conference Record / IEEE Global Telecommunications Conference
ER -