Institutionen för systemteknik (ISY)

What is the best depth-map compression for Depth Image Based Rendering?

Jens Ogniewski, Per-Erik Forssén
CAIP17, Ystad, Sweden
International Conference on Computer Analysis of Images and Patterns
Volume LNCS 10425, Pages 403-415
August 2017

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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Bibtex entry

@InProceedings{ogniewski17b,
  author = 	 {Jens Ogniewski and Per-Erik Forss\'en},
  title = 	 {What is the best depth-map compression for Depth Image Based Rendering?},
  booktitle = {International Conference on Computer Analysis of Images and Patterns},
  year = 	 {2017},
  volume = 	 {LNCS 10425},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  pages = 	 {403-415},
  month = 	 {August},
  address = 	 {Ystad, Sweden},
  publisher =    {Springer Verlag},
  note = 	 {VR Project: Learnable Camera Motion Models, 2014-5928}
}