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Interpreting Plenoptic Images as Multi-View Sequences for Improved Compression
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Realistic3D)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.ORCID iD: 0000-0003-3751-6089
2017 (English)In: ICIP 2017, IEEE, 2017, p. 4557-4561Conference paper, Published paper (Refereed)
Abstract [en]

Over the last decade, advancements in optical devices have made it possible for new novel image acquisition technologies to appear. Angular information for each spatial point is acquired in addition to the spatial information of the scene that enables 3D scene reconstruction and various post-processing effects. Current generation of plenoptic cameras spatially multiplex the angular information, which implies an increase in image resolution to retain the level of spatial information gathered by conventional cameras. In this work, the resulting plenoptic image is interpreted as a multi-view sequence that is efficiently compressed using the multi-view extension of high efficiency video coding (MV-HEVC). A novel two dimensional weighted prediction and rate allocation scheme is proposed to adopt the HEVC compression structure to the plenoptic image properties. The proposed coding approach is a response to ICIP 2017 Grand Challenge: Light field Image Coding. The proposed scheme outperforms all ICME contestants, and improves on the JPEG-anchor of ICME with an average PSNR gain of 7.5 dB and the HEVC-anchor of ICIP 2017 Grand Challenge with an average PSNR gain of 2.4 dB.

Place, publisher, year, edition, pages
IEEE, 2017. p. 4557-4561
Keywords [en]
Light field, plenoptic, MV-HEVC
Identifiers
URN: urn:nbn:se:miun:diva-31455DOI: 10.1109/ICIP.2017.8297145ISI: 000428410704138Scopus ID: 2-s2.0-85045337163ISBN: 978-1-5090-2175-8 (print)OAI: oai:DiVA.org:miun-31455DiVA, id: diva2:1134955
Conference
24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), Beijing, China 17-20 September 2017
Note

Accepted paper.

Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2021-03-19Bibliographically approved
In thesis
1. High Efficiency Light Field Image Compression: Hierarchical Bit Allocation and Shearlet-based View Interpolation
Open this publication in new window or tab >>High Efficiency Light Field Image Compression: Hierarchical Bit Allocation and Shearlet-based View Interpolation
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the years, the pursuit of capturing the precise visual information of a scenehas resulted in various enhancements in digital camera technology, such as highdynamic range, extended depth of field, and high resolution. However, traditionaldigital cameras only capture the spatial information of the scene and cannot pro-vide an immersive presentation of it. Light field (LF) capturing is a new-generationimaging technology that records the spatial and angular information of the scene. Inrecent years, LF imaging has become increasingly popular among the industry andresearch community mainly for two reasons: (1) the advancements made in optical and computational technology have facilitated the process of capturing and processing LF information and (2) LF data have the potential to offer various post-processing applications, such as refocusing at different depth planes, synthetic aperture, 3Dscene reconstruction, and novel view generation. Generally, LF-capturing devicesacquire large amounts of data, which poses a challenge for storage and transmissionresources. Off-the-shelf image and video compression schemes, built on assump-tions drawn from natural images and video, tend to exploit spatial and temporalcorrelations. However, 4D LF data inherit different properties, and hence there is aneed to advance the current compression methods to efficiently address the correla-tion present in LF data.

In this thesis, compression of LF data captured using a plenoptic camera andmulti-camera system (MCS) is considered. Perspective views of a scene capturedfrom different positions are interpreted as a frame of multiple pseudo-video se-quences and given as an input to a multi-view extension of high-efficiency videocoding (MV-HEVC). A 2D prediction and hierarchical coding scheme is proposedin MV-HEVC to improve the compression efficiency of LF data. To further increasethe compression efficiency of views captured using an MCS, an LF reconstructionscheme based on shearlet transform is introduced in LF compression. A sparse set of views is coded using MV-HEVC and later used to predict the remaining views by applying shearlet transform. The prediction error is also coded to further increase the compression efficiency. Publicly available LF datasets are used to benchmark the proposed compression schemes. The anchor scheme specified in the JPEG Plenocommon test conditions is used to evaluate the performance of the proposed scheme. Objective evaluations show that the proposed scheme outperforms state-of-the-art schemes in the compression of LF data captured using a plenoptic camera and an MCS. Moreover, the introduction of shearlet transform in LF compression further improves the compression efficiency at low bitrates, at which the human vision sys-tem is sensitive to the perceived quality.The work presented in this thesis has been published in four peer-reviewed con-ference proceedings and two scientific journals. The proposed compression solu-tions outlined in this thesis significantly improve the rate-distortion efficiency forLF content, which reduces the transmission and storage resources. The MV-HEVC-based LF coding scheme is made publicly available, which can help researchers totest novel compression tools and it can serve as an anchor scheme for future researchstudies. The shearlet-transform-based LF compression scheme presents a compre-hensive framework for testing LF reconstruction methods in the context of LF com-pression.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2021. p. 46
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 341
National Category
Information Systems
Identifiers
urn:nbn:se:miun:diva-41704 (URN)978-91-88947-81-9 (ISBN)
Public defence
2021-04-22, C312, Holmgatan 10, Sundsvall, 09:00 (English)
Opponent
Supervisors
Available from: 2021-03-23 Created: 2021-03-19 Last updated: 2021-03-23Bibliographically approved

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Ahmad, WaqasOlsson, RogerSjöström, Mårten

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Ahmad, W., Olsson, R. & Sjöström, M. (2017). Interpreting Plenoptic Images as Multi-View Sequences for Improved Compression.

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