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Scalable coding of plenoptic images by using a sparse set and disparities
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)ORCID iD: 0000-0003-3751-6089
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)
2016 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 25, no 1, 80-91 p., 7321029Article in journal (Refereed) Published
Abstract [en]

One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers.The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared to HEVC intra, and with over 20 percent compared to HEVC block copying mode.

Place, publisher, year, edition, pages
2016. Vol. 25, no 1, 80-91 p., 7321029
Keyword [en]
image coding, image reconstruction, interpolation, disparity-based interpolation, image inpainting, image reconstruction, lossy coding scheme, microlens image, photosensor, plenoptic images, scalable coding scheme, sparse image set, Cameras, Image coding, Image reconstruction, Lenses, Microoptics, Rendering (computer graphics), Scalability, HEVC, Plenoptic, compression, light field
National Category
Signal Processing Telecommunications
Identifiers
URN: urn:nbn:se:miun:diva-26204DOI: 10.1109/TIP.2015.2498406ISI: 000378330300006Scopus ID: 2-s2.0-85009468560Local ID: STCOAI: oai:DiVA.org:miun-26204DiVA: diva2:866677
Available from: 2015-11-03 Created: 2015-11-03 Last updated: 2017-08-22Bibliographically approved

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Li, YunSjöström, MårtenOlsson, RogerJennehag, Ulf
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