Vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree

Kam Fai Chan Alton; Kam Tim Woo; Chi Wah Kok

Vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree

Kam Fai Chan Alton, Kam Tim Woo, Chi Wah Kok

Department of Electronic and Computer Engineering

Research output: Contribution to journal › Conference article published in journal › peer-review

Abstract

A vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree is presented. Misclassification problem associated with hyperplane decision is eliminated by a multi-level back-tracing algorithm. The vector quantization complexity is further lowered by a novel relative distance quantization rule. Triangular inequality is applied to lower bound the search distance, thus eliminated all the sub-tree in the k-dimensional search tree during back-tracing. Vector quantization image coding results are presented which showed the proposed algorithm outperform other algorithms in literature both in PSNR and computation time.

Original language	English
Article number	7071990
Journal	European Signal Processing Conference
Volume	2002-March
Publication status	Published - 27 Mar 2002
Event	11th European Signal Processing Conference, EUSIPCO 2002 - Toulouse, France Duration: 3 Sept 2002 → 6 Sept 2002

Bibliographical note

Publisher Copyright:
© 2002 EUSIPCO.

Cite this

@article{e7d1ad7a1a1e4808a1fe5b271e71cd13,

title = "Vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree",

abstract = "A vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree is presented. Misclassification problem associated with hyperplane decision is eliminated by a multi-level back-tracing algorithm. The vector quantization complexity is further lowered by a novel relative distance quantization rule. Triangular inequality is applied to lower bound the search distance, thus eliminated all the sub-tree in the k-dimensional search tree during back-tracing. Vector quantization image coding results are presented which showed the proposed algorithm outperform other algorithms in literature both in PSNR and computation time.",

author = "Alton, \{Kam Fai Chan\} and Woo, \{Kam Tim\} and Kok, \{Chi Wah\}",

note = "Publisher Copyright: {\textcopyright} 2002 EUSIPCO.; 11th European Signal Processing Conference, EUSIPCO 2002 ; Conference date: 03-09-2002 Through 06-09-2002",

year = "2002",

month = mar,

day = "27",

language = "English",

volume = "2002-March",

journal = "European Signal Processing Conference",

issn = "2219-5491",

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T1 - Vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree

AU - Alton, Kam Fai Chan

AU - Woo, Kam Tim

AU - Kok, Chi Wah

PY - 2002/3/27

Y1 - 2002/3/27

N2 - A vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree is presented. Misclassification problem associated with hyperplane decision is eliminated by a multi-level back-tracing algorithm. The vector quantization complexity is further lowered by a novel relative distance quantization rule. Triangular inequality is applied to lower bound the search distance, thus eliminated all the sub-tree in the k-dimensional search tree during back-tracing. Vector quantization image coding results are presented which showed the proposed algorithm outperform other algorithms in literature both in PSNR and computation time.

AB - A vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree is presented. Misclassification problem associated with hyperplane decision is eliminated by a multi-level back-tracing algorithm. The vector quantization complexity is further lowered by a novel relative distance quantization rule. Triangular inequality is applied to lower bound the search distance, thus eliminated all the sub-tree in the k-dimensional search tree during back-tracing. Vector quantization image coding results are presented which showed the proposed algorithm outperform other algorithms in literature both in PSNR and computation time.

UR - https://www.scopus.com/pages/publications/84960932291

M3 - Conference article published in journal

AN - SCOPUS:84960932291

SN - 2219-5491

VL - 2002-March

JO - European Signal Processing Conference

JF - European Signal Processing Conference

M1 - 7071990

T2 - 11th European Signal Processing Conference, EUSIPCO 2002

Y2 - 3 September 2002 through 6 September 2002

ER -

Vector quantization fast search algorithm using hyperplane based k-dimensional multi-node search tree

Abstract

Bibliographical note

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