IEICE TRANSACTIONS on Fundamentals
Discrete Time Cellular Neural Networks with Two Types of Neuron Circuits for Image Coding and Their VLSI Implementations
Summary :
This paper described discrete time Cellular Neural Networks (DT-CNN) with two types of neuron circuits for image coding from an analog format to a digital format and their VLSI implementations. The image coding methods proposed in this paper have been investigated for a purpose of transmission of a coded image and restoration again without a large loss of an original image information. Each neuron circuti of a network receives one pixel of an input image, and processes it with binary outputs data fed from neighboring neuron circuits. Parallel dynamics quantization methods have been adopted for image coding methods. They are performed in networks to decide an output binary value of each neuron circuit according to output values of neighboring neuron circuits. Delayed binary outputs of neuron circuits in a neighborhood are directly connected to inputs of a current active neuron circuit. Next state of a network is computed form a current state at some neuron circuits in any time interval. Models of two types of neuron circuits and networks are presented and simulated to confirm an ability of proposed methods. Also, physical layout designs of coding chips have been done to show their possibility of VLSI realizations.
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- IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.8 pp.978-988
- Publication Date
- 1995/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Cong-Kha PHAM, Munemitsu IKEGAMI, Mamoru TANAKA, "Discrete Time Cellular Neural Networks with Two Types of Neuron Circuits for Image Coding and Their VLSI Implementations" in IEICE TRANSACTIONS on Fundamentals,
vol. E78-A, no. 8, pp. 978-988, August 1995, doi: .
Abstract: This paper described discrete time Cellular Neural Networks (DT-CNN) with two types of neuron circuits for image coding from an analog format to a digital format and their VLSI implementations. The image coding methods proposed in this paper have been investigated for a purpose of transmission of a coded image and restoration again without a large loss of an original image information. Each neuron circuti of a network receives one pixel of an input image, and processes it with binary outputs data fed from neighboring neuron circuits. Parallel dynamics quantization methods have been adopted for image coding methods. They are performed in networks to decide an output binary value of each neuron circuit according to output values of neighboring neuron circuits. Delayed binary outputs of neuron circuits in a neighborhood are directly connected to inputs of a current active neuron circuit. Next state of a network is computed form a current state at some neuron circuits in any time interval. Models of two types of neuron circuits and networks are presented and simulated to confirm an ability of proposed methods. Also, physical layout designs of coding chips have been done to show their possibility of VLSI realizations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_8_978/_p
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@ARTICLE{e78-a_8_978,
author={Cong-Kha PHAM, Munemitsu IKEGAMI, Mamoru TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Discrete Time Cellular Neural Networks with Two Types of Neuron Circuits for Image Coding and Their VLSI Implementations},
year={1995},
volume={E78-A},
number={8},
pages={978-988},
abstract={This paper described discrete time Cellular Neural Networks (DT-CNN) with two types of neuron circuits for image coding from an analog format to a digital format and their VLSI implementations. The image coding methods proposed in this paper have been investigated for a purpose of transmission of a coded image and restoration again without a large loss of an original image information. Each neuron circuti of a network receives one pixel of an input image, and processes it with binary outputs data fed from neighboring neuron circuits. Parallel dynamics quantization methods have been adopted for image coding methods. They are performed in networks to decide an output binary value of each neuron circuit according to output values of neighboring neuron circuits. Delayed binary outputs of neuron circuits in a neighborhood are directly connected to inputs of a current active neuron circuit. Next state of a network is computed form a current state at some neuron circuits in any time interval. Models of two types of neuron circuits and networks are presented and simulated to confirm an ability of proposed methods. Also, physical layout designs of coding chips have been done to show their possibility of VLSI realizations.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - Discrete Time Cellular Neural Networks with Two Types of Neuron Circuits for Image Coding and Their VLSI Implementations
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 978
EP - 988
AU - Cong-Kha PHAM
AU - Munemitsu IKEGAMI
AU - Mamoru TANAKA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1995
AB - This paper described discrete time Cellular Neural Networks (DT-CNN) with two types of neuron circuits for image coding from an analog format to a digital format and their VLSI implementations. The image coding methods proposed in this paper have been investigated for a purpose of transmission of a coded image and restoration again without a large loss of an original image information. Each neuron circuti of a network receives one pixel of an input image, and processes it with binary outputs data fed from neighboring neuron circuits. Parallel dynamics quantization methods have been adopted for image coding methods. They are performed in networks to decide an output binary value of each neuron circuit according to output values of neighboring neuron circuits. Delayed binary outputs of neuron circuits in a neighborhood are directly connected to inputs of a current active neuron circuit. Next state of a network is computed form a current state at some neuron circuits in any time interval. Models of two types of neuron circuits and networks are presented and simulated to confirm an ability of proposed methods. Also, physical layout designs of coding chips have been done to show their possibility of VLSI realizations.
ER -
English
