Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding

Hisashi AOMORI; Kohei KAWAKAMI; Tsuyoshi OTAKE; Nobuaki TAKAHASHI; Masayuki YAMAUCHI; Mamoru TANAKA

doi:10.1093/ietfec/e88-a.10.2607

Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding

Hisashi AOMORI, Kohei KAWAKAMI, Tsuyoshi OTAKE, Nobuaki TAKAHASHI, Masayuki YAMAUCHI, Mamoru TANAKA

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The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.10 pp.2607-2614

Publication Date: 2005/10/01

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DOI: 10.1093/ietfec/e88-a.10.2607

Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)

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Hisashi AOMORI, Kohei KAWAKAMI, Tsuyoshi OTAKE, Nobuaki TAKAHASHI, Masayuki YAMAUCHI, Mamoru TANAKA, "Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding" in IEICE TRANSACTIONS on Fundamentals, vol. E88-A, no. 10, pp. 2607-2614, October 2005, doi: 10.1093/ietfec/e88-a.10.2607.
Abstract: The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.10.2607/_p

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@ARTICLE{e88-a_10_2607,
author={Hisashi AOMORI, Kohei KAWAKAMI, Tsuyoshi OTAKE, Nobuaki TAKAHASHI, Masayuki YAMAUCHI, Mamoru TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding},
year={2005},
volume={E88-A},
number={10},
pages={2607-2614},
abstract={The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.},
keywords={},
doi={10.1093/ietfec/e88-a.10.2607},
ISSN={},
month={October},}

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TY - JOUR
TI - Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2607
EP - 2614
AU - Hisashi AOMORI
AU - Kohei KAWAKAMI
AU - Tsuyoshi OTAKE
AU - Nobuaki TAKAHASHI
AU - Masayuki YAMAUCHI
AU - Mamoru TANAKA
PY - 2005
DO - 10.1093/ietfec/e88-a.10.2607
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2005
AB - The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.
ER -