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, kami mencadangkan algoritma carian bitmap pantas untuk mengurangkan kerumitan pengiraan teknik pengkuantitian vektor (VQ) berasaskan transformasi, yang mencapai kualiti yang lebih baik dalam imej yang dibina semula daripada VQ biasa. Dengan mengalih keluar kata kod yang tidak mungkin dalam setiap langkah, kaedah carian bitmap, yang bermula daripada bitmap yang paling ketara kemudian yang penting berturut-turut, boleh menjimatkan lebih daripada 90% pengiraan VQ yang diubah biasa. Dengan menggunakan penguraian nilai tunggal (SVD) VQ sebagai contoh, analisis teori dan hasil simulasi menunjukkan bahawa kaedah carian peta bit yang dicadangkan secara mendadak mengurangkan pengiraan dan mencapai herotan yang tidak kelihatan dalam imej yang dibina semula.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Jar-Ferr YANG, Yu-Hwe LEE, Jen-Fa HUANG, Zhong-Geng LEE, "Transform-Based Vector Quantization Using Bitmap Search Algorithms" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 12, pp. 2113-2121, December 2000, doi: .
Abstract: In this paper, we propose fast bitmap search algorithms to reduce the computational complexity of transform-based vector quantization (VQ) techniques, which achieve better quality in reconstructed images than the ordinary VQ. By removing the unlikely codewords in each step, the bitmap search method, which starts from the most significant bitmap then the successive significant ones, can save more than 90% computation of the ordinary transformed VQ. By applying to the singular value decomposition (SVD) VQ as an example, theoretical analyses and simulation results show that the proposed bitmap search methods dramatically reduce the computation and achieve invisible distortion in the reconstructed images.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_12_2113/_p
Salinan
@ARTICLE{e83-d_12_2113,
author={Jar-Ferr YANG, Yu-Hwe LEE, Jen-Fa HUANG, Zhong-Geng LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Transform-Based Vector Quantization Using Bitmap Search Algorithms},
year={2000},
volume={E83-D},
number={12},
pages={2113-2121},
abstract={In this paper, we propose fast bitmap search algorithms to reduce the computational complexity of transform-based vector quantization (VQ) techniques, which achieve better quality in reconstructed images than the ordinary VQ. By removing the unlikely codewords in each step, the bitmap search method, which starts from the most significant bitmap then the successive significant ones, can save more than 90% computation of the ordinary transformed VQ. By applying to the singular value decomposition (SVD) VQ as an example, theoretical analyses and simulation results show that the proposed bitmap search methods dramatically reduce the computation and achieve invisible distortion in the reconstructed images.},
keywords={},
doi={},
ISSN={},
month={December},}
Salinan
TY - JOUR
TI - Transform-Based Vector Quantization Using Bitmap Search Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 2113
EP - 2121
AU - Jar-Ferr YANG
AU - Yu-Hwe LEE
AU - Jen-Fa HUANG
AU - Zhong-Geng LEE
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2000
AB - In this paper, we propose fast bitmap search algorithms to reduce the computational complexity of transform-based vector quantization (VQ) techniques, which achieve better quality in reconstructed images than the ordinary VQ. By removing the unlikely codewords in each step, the bitmap search method, which starts from the most significant bitmap then the successive significant ones, can save more than 90% computation of the ordinary transformed VQ. By applying to the singular value decomposition (SVD) VQ as an example, theoretical analyses and simulation results show that the proposed bitmap search methods dramatically reduce the computation and achieve invisible distortion in the reconstructed images.
ER -