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What is the best depth-map compression for Depth Image Based Rendering?
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5698-5983
2017 (English)In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10425, p. 403-415Conference paper, Published paper (Refereed)
Abstract [en]

Many of the latest smart phones and tablets come with integrated depth sensors, that make depth-maps freely available, thus enabling new forms of applications like rendering from different view points. However, efficient compression exploiting the characteristics of depth-maps as well as the requirements of these new applications is still an open issue. In this paper, we evaluate different depth-map compression algorithms, with a focus on tree-based methods and view projection as application.

The contributions of this paper are the following: 1. extensions of existing geometric compression trees, 2. a comparison of a number of different trees, 3. a comparison of them to a state-of-the-art video coder, 4. an evaluation using ground-truth data that considers both depth-maps and predicted frames with arbitrary camera translation and rotation.

Despite our best efforts, and contrary to earlier results, current video depth-map compression outperforms tree-based methods in most cases. The reason for this is likely that previous evaluations focused on low-quality, low-resolution depth maps, while high-resolution depth (as needed in the DIBR setting) has been ignored up until now. We also demonstrate that PSNR on depth-maps is not always a good measure of their utility.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10425, p. 403-415
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10425
Keywords [en]
Depth map compression; Quadtree; Triangle tree; 3DVC; View projection
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-142064DOI: 10.1007/978-3-319-64698-5_34ISI: 000432084600034Scopus ID: 2-s2.0-85028463006ISBN: 9783319646978 (print)ISBN: 9783319646985 (electronic)OAI: oai:DiVA.org:liu-142064DiVA, id: diva2:1150797
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24
Funder
Swedish Research Council, 2014-5928
Note

VR Project: Learnable Camera Motion Models, 2014-5928

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-06-01Bibliographically approved

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Ogniewski, JensForssén, Per-Erik

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CiteExportLink to record
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Citation style
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