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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: 2019-11-19Bibliographically approved
In thesis
1. Interpolation Techniques with Applications in Video Coding
Open this publication in new window or tab >>Interpolation Techniques with Applications in Video Coding
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Recent years have seen the advent of RGB+D video (color+depth video), which enables new applications like free-viewpoint video, 3D and virtual reality. This is however achieved by adding additional data, thus increasing the bitrate. On the other hand, the added geometrical data can be used for more accurate frame prediction, thus decreasing bitrate. Modern encoders use previously decoded frames to predict other ones, meaning they only need to encode the difference. When geometrical data is available, previous frames can instead be projected to the frame that is currently predicted, thus reaching a higher accuracy and a higher compression.

In this thesis, different techniques are described and evaluated enabling such a prediction scheme based on projecting from depth-images, so called depth-image based rendering (DIBR). A DIBR method is found that maximizes image quality, in terms of minimizing the differences of the projected frame to the groundtruth of the frame it was projected to, i.e. the frame that is to be predicted. This was achieved by evaluating combinations of both state-of-the-art methods for DIBR as well as own extensions, meant to solve artifacts that were discovered during this work. Furthermore, a real-time version of this DIBR method is derived and, since the deph-maps will be compressed as well, the impact of depth-map compression on the achieved projection quality is evaluated, for different compression methods including novel extensions of existing methods. Finally, spline methods are derived for both geometrical and color interpolation.

Although all this was done with a focus on video compression, many of the presented methods are useful for other applications as well, like free-viewpoint video or animation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 38
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1858
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-162116 (URN)9789179299514 (ISBN)
Presentation
2019-12-09, Ada Lovelace, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-12-10Bibliographically approved

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Ogniewski, JensForssén, Per-Erik

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