IEICE TRANSACTIONS on Information
Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks
Summary :
Multiple description (MD) coding is an attractive framework for robust information transmission over non-prioritized and unpredictable networks. In this paper, a novel MD image coding scheme is proposed based on convolutional neural networks (CNNs), which aims to improve the reconstructed quality of side and central decoders. For this purpose initially, a given image is encoded into two independent descriptions by sub-sampling. Such a design can make the proposed method compatible with the existing image coding standards. At the decoder, in order to achieve high-quality of side and central image reconstruction, three CNNs, including two side decoder sub-networks and one central decoder sub-network, are adopted into an end-to-end reconstruction framework. Experimental results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality. The proposed method demonstrates better rate central and side distortion performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.10 pp.2543-2546
- Publication Date
- 2018/10/01
- Publicized
- 2018/07/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8028
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Ting ZHANG
Beijing Jiaotong University
Huihui BAI
Beijing Jiaotong University
Mengmeng ZHANG
North China University of Technology
Yao ZHAO
Beijing Jiaotong University
Keyword
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Ting ZHANG, Huihui BAI, Mengmeng ZHANG, Yao ZHAO, "Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2543-2546, October 2018, doi: 10.1587/transinf.2018EDL8028.
Abstract: Multiple description (MD) coding is an attractive framework for robust information transmission over non-prioritized and unpredictable networks. In this paper, a novel MD image coding scheme is proposed based on convolutional neural networks (CNNs), which aims to improve the reconstructed quality of side and central decoders. For this purpose initially, a given image is encoded into two independent descriptions by sub-sampling. Such a design can make the proposed method compatible with the existing image coding standards. At the decoder, in order to achieve high-quality of side and central image reconstruction, three CNNs, including two side decoder sub-networks and one central decoder sub-network, are adopted into an end-to-end reconstruction framework. Experimental results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality. The proposed method demonstrates better rate central and side distortion performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8028/_p
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@ARTICLE{e101-d_10_2543,
author={Ting ZHANG, Huihui BAI, Mengmeng ZHANG, Yao ZHAO, },
journal={IEICE TRANSACTIONS on Information},
title={Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks},
year={2018},
volume={E101-D},
number={10},
pages={2543-2546},
abstract={Multiple description (MD) coding is an attractive framework for robust information transmission over non-prioritized and unpredictable networks. In this paper, a novel MD image coding scheme is proposed based on convolutional neural networks (CNNs), which aims to improve the reconstructed quality of side and central decoders. For this purpose initially, a given image is encoded into two independent descriptions by sub-sampling. Such a design can make the proposed method compatible with the existing image coding standards. At the decoder, in order to achieve high-quality of side and central image reconstruction, three CNNs, including two side decoder sub-networks and one central decoder sub-network, are adopted into an end-to-end reconstruction framework. Experimental results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality. The proposed method demonstrates better rate central and side distortion performance.},
keywords={},
doi={10.1587/transinf.2018EDL8028},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 2543
EP - 2546
AU - Ting ZHANG
AU - Huihui BAI
AU - Mengmeng ZHANG
AU - Yao ZHAO
PY - 2018
DO - 10.1587/transinf.2018EDL8028
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - Multiple description (MD) coding is an attractive framework for robust information transmission over non-prioritized and unpredictable networks. In this paper, a novel MD image coding scheme is proposed based on convolutional neural networks (CNNs), which aims to improve the reconstructed quality of side and central decoders. For this purpose initially, a given image is encoded into two independent descriptions by sub-sampling. Such a design can make the proposed method compatible with the existing image coding standards. At the decoder, in order to achieve high-quality of side and central image reconstruction, three CNNs, including two side decoder sub-networks and one central decoder sub-network, are adopted into an end-to-end reconstruction framework. Experimental results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality. The proposed method demonstrates better rate central and side distortion performance.
ER -
English
