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A Novel Light Field Image Compression Method Using EPI Restoration Neural Network

Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane...

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Detalles Bibliográficos
Autores principales: Liu, Jinghuai, Zhang, Qian, Shen, Ang, Gao, Ying, Hou, Jiaqi, Wang, Bin, Yan, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209000/
https://www.ncbi.nlm.nih.gov/pubmed/35734347
http://dx.doi.org/10.1155/2022/8324438
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author Liu, Jinghuai
Zhang, Qian
Shen, Ang
Gao, Ying
Hou, Jiaqi
Wang, Bin
Yan, Tao
author_facet Liu, Jinghuai
Zhang, Qian
Shen, Ang
Gao, Ying
Hou, Jiaqi
Wang, Bin
Yan, Tao
author_sort Liu, Jinghuai
collection PubMed
description Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane image (EPI) contains a lot of low rank information, which is suitable for recovering the complete EPI from a part of EPI. In this paper, a light field image coding framework based on EPI restoration neural network has been proposed. Compared with previous algorithms, the proposed algorithm further takes advantage of the inherent similarity in light field images, and the proposed framework has higher performance and robustness. Experimental results show that the proposed method has superior performance compared to the state-of-the-art both in quantitatively and qualitatively.
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spelling pubmed-92090002022-06-21 A Novel Light Field Image Compression Method Using EPI Restoration Neural Network Liu, Jinghuai Zhang, Qian Shen, Ang Gao, Ying Hou, Jiaqi Wang, Bin Yan, Tao Biomed Res Int Research Article Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane image (EPI) contains a lot of low rank information, which is suitable for recovering the complete EPI from a part of EPI. In this paper, a light field image coding framework based on EPI restoration neural network has been proposed. Compared with previous algorithms, the proposed algorithm further takes advantage of the inherent similarity in light field images, and the proposed framework has higher performance and robustness. Experimental results show that the proposed method has superior performance compared to the state-of-the-art both in quantitatively and qualitatively. Hindawi 2022-06-13 /pmc/articles/PMC9209000/ /pubmed/35734347 http://dx.doi.org/10.1155/2022/8324438 Text en Copyright © 2022 Jinghuai Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Jinghuai
Zhang, Qian
Shen, Ang
Gao, Ying
Hou, Jiaqi
Wang, Bin
Yan, Tao
A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title_full A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title_fullStr A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title_full_unstemmed A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title_short A Novel Light Field Image Compression Method Using EPI Restoration Neural Network
title_sort novel light field image compression method using epi restoration neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209000/
https://www.ncbi.nlm.nih.gov/pubmed/35734347
http://dx.doi.org/10.1155/2022/8324438
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