IEICE TRANSACTIONS on Information
Transform-Based Vector Quantization Using Bitmap Search Algorithms
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.12 pp.2113-2121
- Publication Date
- 2000/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Image Pattern Recognition
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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
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@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},}
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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 -
English
