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Titolo:
Rotated partial distance search for faster vector quantization encoding
Autore:
McNames, J;
Indirizzi:
Portland State Univ, Dept Elect & Comp Engn, Portland, OR 97207 USA Portland State Univ Portland OR USA 97207 mp Engn, Portland, OR 97207 USA
Titolo Testata:
IEEE SIGNAL PROCESSING LETTERS
fascicolo: 9, volume: 7, anno: 2000,
pagine: 244 - 246
SICI:
1070-9908(200009)7:9<244:RPDSFF>2.0.ZU;2-8
Fonte:
ISI
Lingua:
ENG
Soggetto:
ALGORITHM;
Keywords:
eigenvector method; k-d tree; nearest neighbor; principal components analysis (PCA); vector quantization encoding;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
--discip_EC--
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: McNames, J Portland State Univ, Dept Elect & Comp Engn, Portland, OR 97207USA Portland State Univ Portland OR USA 97207 rtland, OR 97207 USA
Citazione:
J. McNames, "Rotated partial distance search for faster vector quantization encoding", IEEE SIG PL, 7(9), 2000, pp. 244-246

Abstract

Partial distance search (PDS) is a method of reducing the amount of computation required for vector quantization encoding. The method is simple and general enough to be incorporated into many fast encoding algorithms. This paper describes a simple improvement to PDS based on principal components analysis (PCB), which rotates the codebook without altering the interpoint distances. Like PDS, this new method fan be used to improve many fast encoding algorithms. The algorithm decreases the decoding time of PDS by as much as 44 %, and decreases the decoding time of k-d trees by as much as 66% on common vector quantization benchmarks.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 02/12/20 alle ore 17:41:50