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2017 | OriginalPaper | Buchkapitel

What Is the Best Depth-Map Compression for Depth Image Based Rendering?

verfasst von : Jens Ogniewski, Per-Erik Forssén

Erschienen in: Computer Analysis of Images and Patterns

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Labeling Color 2D Digital Images in Theoretical Near Logarithmic Time
Nächstes Kapitel Nonlinear Mapping Based on Spectral Angle Preserving Principle for Hyperspectral Image Analysis
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Metadaten
Titel
What Is the Best Depth-Map Compression for Depth Image Based Rendering?
verfasst von
Jens Ogniewski
Per-Erik Forssén
Copyright-Jahr
2017
Buch
Computer Analysis of Images and Patterns

Print ISBN: 978-3-319-64697-8

Electronic ISBN: 978-3-319-64698-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-64698-5_34