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
Neste artigo, são apresentados dois algoritmos eficientes de busca de livros de código para quantização vetorial (VQ). O primeiro algoritmo de busca rápida utiliza a propriedade de compacidade da energia do sinal de transformação ortogonal. No domínio transformado, o algoritmo usa relações geométricas entre o vetor de entrada e a palavra-código para descartar muitas palavras-código improváveis. O segundo algoritmo, que transforma apenas os componentes principais, é proposto para aliviar alguma sobrecarga de cálculo e a quantidade de armazenamento. A relação entre os componentes principais e o vetor de entrada é utilizada no segundo algoritmo. Como ambos os algoritmos propostos rejeitam as palavras-código que são impossíveis de serem a palavra-código mais próxima, eles produzem a mesma saída que o algoritmo de busca completa convencional. Os resultados da simulação confirmam a eficácia dos algoritmos propostos.
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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 -