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Geometry-aware view reconstruction network for light field image compression

Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue an...

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Autores principales: Zhang, Youzhi, Wan, Lifei, Mao, Yifan, Huang, Xinpeng, Liu, Deyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789090/
https://www.ncbi.nlm.nih.gov/pubmed/36564515
http://dx.doi.org/10.1038/s41598-022-26887-4
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author Zhang, Youzhi
Wan, Lifei
Mao, Yifan
Huang, Xinpeng
Liu, Deyang
author_facet Zhang, Youzhi
Wan, Lifei
Mao, Yifan
Huang, Xinpeng
Liu, Deyang
author_sort Zhang, Youzhi
collection PubMed
description Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue and under-exploitation of LF rich structure information limit the view reconstruction qualities, which further influence LF compression efficiency. In order to alleviate these problems, in this paper, we propose a geometry-aware view reconstruction network for LF compression. In our method, only sparsely-sampled LF views are encoded, which are further used as priors to reconstruct the un-sampled LF views at the decoder side. The proposed reconstruction process contains two stages including geometry-aware reconstruction and texture refinement. The geometry-aware reconstruction stage utilizes a multi-stream framework, which can fully explore LF spatial-angular, location and geometry information. The texture refinement stage can adequately fuse such rich LF information to further improve LF reconstruction quality. Comprehensive experimental results validate the superiority of the proposed method. The rate-distortion performance and the perceptual quality of reconstructed views further demonstrate that the proposed method can save more bitrate while increasing LF reconstruction quality.
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spelling pubmed-97890902022-12-25 Geometry-aware view reconstruction network for light field image compression Zhang, Youzhi Wan, Lifei Mao, Yifan Huang, Xinpeng Liu, Deyang Sci Rep Article Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue and under-exploitation of LF rich structure information limit the view reconstruction qualities, which further influence LF compression efficiency. In order to alleviate these problems, in this paper, we propose a geometry-aware view reconstruction network for LF compression. In our method, only sparsely-sampled LF views are encoded, which are further used as priors to reconstruct the un-sampled LF views at the decoder side. The proposed reconstruction process contains two stages including geometry-aware reconstruction and texture refinement. The geometry-aware reconstruction stage utilizes a multi-stream framework, which can fully explore LF spatial-angular, location and geometry information. The texture refinement stage can adequately fuse such rich LF information to further improve LF reconstruction quality. Comprehensive experimental results validate the superiority of the proposed method. The rate-distortion performance and the perceptual quality of reconstructed views further demonstrate that the proposed method can save more bitrate while increasing LF reconstruction quality. Nature Publishing Group UK 2022-12-23 /pmc/articles/PMC9789090/ /pubmed/36564515 http://dx.doi.org/10.1038/s41598-022-26887-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhang, Youzhi
Wan, Lifei
Mao, Yifan
Huang, Xinpeng
Liu, Deyang
Geometry-aware view reconstruction network for light field image compression
title Geometry-aware view reconstruction network for light field image compression
title_full Geometry-aware view reconstruction network for light field image compression
title_fullStr Geometry-aware view reconstruction network for light field image compression
title_full_unstemmed Geometry-aware view reconstruction network for light field image compression
title_short Geometry-aware view reconstruction network for light field image compression
title_sort geometry-aware view reconstruction network for light field image compression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789090/
https://www.ncbi.nlm.nih.gov/pubmed/36564515
http://dx.doi.org/10.1038/s41598-022-26887-4
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AT maoyifan geometryawareviewreconstructionnetworkforlightfieldimagecompression
AT huangxinpeng geometryawareviewreconstructionnetworkforlightfieldimagecompression
AT liudeyang geometryawareviewreconstructionnetworkforlightfieldimagecompression