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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, kaedah pengkuantitian vektor terkelas (CVQ) menggunakan pengelas berasaskan arah baru dicadangkan. Pengelas baharu menggunakan ukuran herotan yang berkaitan dengan sudut antara vektor untuk menentukan persamaan vektor. Ukuran herotan adalah mudah dan mencukupi untuk mengklasifikasikan pelbagai jenis tepi selain daripada jenis garis tunggal dan lurus, yang mengehadkan saiz blok imej kepada saiz yang agak kecil. Keputusan simulasi menunjukkan bahawa teknik yang dicadangkan boleh mencapai kualiti persepsi yang lebih baik dan integriti kelebihan pada saiz blok yang lebih besar, berbanding dengan CVQ lain. Ia ditunjukkan apabila dimensi vektor ditukar daripada 16(4
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Salinan
Chou-Chen WANG, Chin-Hsing CHEN, "Classified Vector Quantization for Image Compression Using Direction Classification" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 3, pp. 535-542, March 1999, doi: .
Abstract: In this paper, a classified vector quantization (CVQ) method using a novel direction based classifier is proposed. The new classifier uses a distortion measure related to the angle between vectors to determine the similarity of vectors. The distortion measure is simple and adequate to classify various edge types other than single and straight line types, which limit the size of image block to a rather small size. Simulation results show that the proposed technique can achieve better perceptual quality and edge integrity at a larger block size, as compared to other CVQs. It is shown when the vector dimension is changed from 16(4
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_3_535/_p
Salinan
@ARTICLE{e82-a_3_535,
author={Chou-Chen WANG, Chin-Hsing CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Classified Vector Quantization for Image Compression Using Direction Classification},
year={1999},
volume={E82-A},
number={3},
pages={535-542},
abstract={In this paper, a classified vector quantization (CVQ) method using a novel direction based classifier is proposed. The new classifier uses a distortion measure related to the angle between vectors to determine the similarity of vectors. The distortion measure is simple and adequate to classify various edge types other than single and straight line types, which limit the size of image block to a rather small size. Simulation results show that the proposed technique can achieve better perceptual quality and edge integrity at a larger block size, as compared to other CVQs. It is shown when the vector dimension is changed from 16(4
keywords={},
doi={},
ISSN={},
month={March},}
Salinan
TY - JOUR
TI - Classified Vector Quantization for Image Compression Using Direction Classification
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 535
EP - 542
AU - Chou-Chen WANG
AU - Chin-Hsing CHEN
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 1999
AB - In this paper, a classified vector quantization (CVQ) method using a novel direction based classifier is proposed. The new classifier uses a distortion measure related to the angle between vectors to determine the similarity of vectors. The distortion measure is simple and adequate to classify various edge types other than single and straight line types, which limit the size of image block to a rather small size. Simulation results show that the proposed technique can achieve better perceptual quality and edge integrity at a larger block size, as compared to other CVQs. It is shown when the vector dimension is changed from 16(4
ER -