The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, deux algorithmes efficaces de recherche de livres de codes pour la quantification vectorielle (VQ) sont présentés. Le premier algorithme de recherche rapide utilise la propriété de compacité de l'énergie du signal de transformation orthogonale. Sur le domaine transformé, l'algorithme utilise les relations géométriques entre le vecteur d'entrée et le mot de code pour éliminer de nombreux mots de code improbables. Le deuxième algorithme, qui transforme uniquement les composants principaux, est proposé pour alléger une certaine surcharge de calcul et la quantité de stockage. La relation entre les composantes principales et le vecteur d'entrée est utilisée dans le deuxième algorithme. Étant donné que les deux algorithmes proposés rejettent les mots de code qui ne peuvent pas être le mot de code le plus proche, ils produisent le même résultat qu'un algorithme de recherche complète conventionnel. Les résultats de simulation confirment l'efficacité des algorithmes proposés.
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SeongJoon BAEK, Koeng-Mo SUNG, "Two Fast Nearest Neighbor Searching Algorithms for Vector Quantization" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 10, pp. 2569-2575, October 2001, doi: .
Abstract: In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy of orthogonal transformation. On the transformed domain, the algorithm uses geometrical relations between the input vector and codeword to discard many unlikely codewords. The second algorithm, which transforms principal components only, is proposed to alleviate some calculation overhead and the amount of storage. The relation between the principal components and the input vector is utilized in the second algorithm. Since both of the proposed algorithms reject those codewords that are impossible to be the nearest codeword, they produce the same output as conventional full search algorithm. Simulation results confirm the effectiveness of the proposed algorithms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_10_2569/_p
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@ARTICLE{e84-a_10_2569,
author={SeongJoon BAEK, Koeng-Mo SUNG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Two Fast Nearest Neighbor Searching Algorithms for Vector Quantization},
year={2001},
volume={E84-A},
number={10},
pages={2569-2575},
abstract={In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy of orthogonal transformation. On the transformed domain, the algorithm uses geometrical relations between the input vector and codeword to discard many unlikely codewords. The second algorithm, which transforms principal components only, is proposed to alleviate some calculation overhead and the amount of storage. The relation between the principal components and the input vector is utilized in the second algorithm. Since both of the proposed algorithms reject those codewords that are impossible to be the nearest codeword, they produce the same output as conventional full search algorithm. Simulation results confirm the effectiveness of the proposed algorithms.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Two Fast Nearest Neighbor Searching Algorithms for Vector Quantization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2569
EP - 2575
AU - SeongJoon BAEK
AU - Koeng-Mo SUNG
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2001
AB - In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy of orthogonal transformation. On the transformed domain, the algorithm uses geometrical relations between the input vector and codeword to discard many unlikely codewords. The second algorithm, which transforms principal components only, is proposed to alleviate some calculation overhead and the amount of storage. The relation between the principal components and the input vector is utilized in the second algorithm. Since both of the proposed algorithms reject those codewords that are impossible to be the nearest codeword, they produce the same output as conventional full search algorithm. Simulation results confirm the effectiveness of the proposed algorithms.
ER -