Publication: LFC-SASR: Light Field Coding Using Spat...
Master data
Title: | LFC-SASR: Light Field Coding Using Spatial and Angular Super-Resolution |
Subtitle: | |
Abstract: | Light field imaging enables post-capture actions such as refocusing and changing view perspective by capturing both spatial and angular information. However, capturing richer information of the 3D scene results in a huge amount of data. To improve the compression efficiency of the existing light field compression methods, we investigate the impact of light field super-resolution approaches (both spatial and angular super-resolution) on the compression efficiency. To this end, firstly, we downscale light field images over (i) spatial resolution, (ii) angular resolution, and (iii) spatial-angular resolution and encode them using Versatile Video Coding (VVC). We then apply a set of light field super-resolution deep neural networks to reconstruct light field images in their full spatial-angular resolution and compare their compression efficiency. Experimental results show that encoding the low angular resolution light field image and applying angular super-resolution yield bitrate savings of 51.16% and 53.41% to maintain the same PSNR and SSIM, respectively, compared to encoding the light field image in high-resolution. |
Keywords: | Light field, Compression, Super-resolution, VVC |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 18.07.2022 (Print) |
Published by: |
ICMEW '22 Proceedings of the ICME Workshop on Hyper-Realistic Multimedia for Enhanced Quality of Experience
ICMEW '22 Proceedings of the ICME Workshop on Hyper-Realistic Multimedia for Enhanced Quality of Experience
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IEEE Computer Society Press;
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Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 1 - 6 |
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Keine Version vorhanden |
Publication date: | 18.07.2022 |
ISBN: | - |
ISSN: | - |
Homepage: | https://www.computer.org/csdl/proceedings-article/icmew/2022/09859373/1G4F0ndbVoQ |
Publication date: | 18.07.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/icmew56448.2022.9859373 |
Homepage: | https://www.computer.org/csdl/proceedings-article/icmew/2022/09859373/1G4F0ndbVoQ |
Open access |
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Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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