IEICE TRANSACTIONS on Fundamentals
Two-Dimensional Least Squares Lattice Algorithm for Linear Prediction
Summary :
In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.11 pp.2325-2329
- Publication Date
- 1997/11/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Category
- Digital Signal Processing
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Takayuki NAKACHI, Katsumi YAMASHITA, Nozomu HAMADA, "Two-Dimensional Least Squares Lattice Algorithm for Linear Prediction" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 11, pp. 2325-2329, November 1997, doi: .
Abstract: In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_11_2325/_p
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@ARTICLE{e80-a_11_2325,
author={Takayuki NAKACHI, Katsumi YAMASHITA, Nozomu HAMADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Two-Dimensional Least Squares Lattice Algorithm for Linear Prediction},
year={1997},
volume={E80-A},
number={11},
pages={2325-2329},
abstract={In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - Two-Dimensional Least Squares Lattice Algorithm for Linear Prediction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2325
EP - 2329
AU - Takayuki NAKACHI
AU - Katsumi YAMASHITA
AU - Nozomu HAMADA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1997
AB - In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.
ER -
English
