### Abstract

Summary form only given, as follows. Tree-search vector quantization is an effective coding scheme at high bit rates to circumvent the high complexity of codevector search. The authors present a binary tree-search codebook design method suitable for gain/shape vector quantization. Conventional codebooks are often designed using the LBG (Linde-Buzo-Gray) algorithm when input signal distribution is unknown. In the LBG algorithm, codebook performance depends on the vectors used for the first iteration update. A splitting technique is usually used to determine the initial vectors if the input vectors have various norms or various average values. However, such a splitting technique is not available for gain/shape vector quantization, since input vectors have uniform norms and sometimes have average values of zero. When the codebook has binary tree structure, the training sequence, a set of input vectors, can be divided into two groups by using a hyperplane testing algorithm. This hyperplane is characterized by a first principal component vector. Validity of the proposed partition procedure is proved with the mean-square-error criterion.

Original language | English |
---|---|

Title of host publication | IEEE 1988 Int Symp on Inf Theory Abstr of Pap |

Place of Publication | New York, NY, USA |

Publisher | Publ by IEEE |

Pages | 163-164 |

Number of pages | 2 |

Volume | 25 n 13 |

Publication status | Published - 1988 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*IEEE 1988 Int Symp on Inf Theory Abstr of Pap*(Vol. 25 n 13, pp. 163-164). New York, NY, USA: Publ by IEEE.

**Mean square error binary tree gain-shape vector quantization via hyperplane testing.** / Watanabe, Hiroshi; Yashima, Yoshiyuki.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*IEEE 1988 Int Symp on Inf Theory Abstr of Pap.*vol. 25 n 13, Publ by IEEE, New York, NY, USA, pp. 163-164.

}

TY - GEN

T1 - Mean square error binary tree gain-shape vector quantization via hyperplane testing

AU - Watanabe, Hiroshi

AU - Yashima, Yoshiyuki

PY - 1988

Y1 - 1988

N2 - Summary form only given, as follows. Tree-search vector quantization is an effective coding scheme at high bit rates to circumvent the high complexity of codevector search. The authors present a binary tree-search codebook design method suitable for gain/shape vector quantization. Conventional codebooks are often designed using the LBG (Linde-Buzo-Gray) algorithm when input signal distribution is unknown. In the LBG algorithm, codebook performance depends on the vectors used for the first iteration update. A splitting technique is usually used to determine the initial vectors if the input vectors have various norms or various average values. However, such a splitting technique is not available for gain/shape vector quantization, since input vectors have uniform norms and sometimes have average values of zero. When the codebook has binary tree structure, the training sequence, a set of input vectors, can be divided into two groups by using a hyperplane testing algorithm. This hyperplane is characterized by a first principal component vector. Validity of the proposed partition procedure is proved with the mean-square-error criterion.

AB - Summary form only given, as follows. Tree-search vector quantization is an effective coding scheme at high bit rates to circumvent the high complexity of codevector search. The authors present a binary tree-search codebook design method suitable for gain/shape vector quantization. Conventional codebooks are often designed using the LBG (Linde-Buzo-Gray) algorithm when input signal distribution is unknown. In the LBG algorithm, codebook performance depends on the vectors used for the first iteration update. A splitting technique is usually used to determine the initial vectors if the input vectors have various norms or various average values. However, such a splitting technique is not available for gain/shape vector quantization, since input vectors have uniform norms and sometimes have average values of zero. When the codebook has binary tree structure, the training sequence, a set of input vectors, can be divided into two groups by using a hyperplane testing algorithm. This hyperplane is characterized by a first principal component vector. Validity of the proposed partition procedure is proved with the mean-square-error criterion.

UR - http://www.scopus.com/inward/record.url?scp=0024124653&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024124653&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0024124653

VL - 25 n 13

SP - 163

EP - 164

BT - IEEE 1988 Int Symp on Inf Theory Abstr of Pap

PB - Publ by IEEE

CY - New York, NY, USA

ER -