Image Processing Reference
In-Depth Information
For more details on Non-scalable and Scalable 3DHV coding, the reader is
referred to the Chapter “3D Holoscopic Video Representation and Coding
Technology.”
Conclusions
This chapter has provided an overview of the current and upcoming formats
for 3D video coding, from stereo compatible formats to holoscopic, through
multiview video plus depth format. The research on 3D video is progressing
at a very rapid pace, driven by academic research as well as standardization
efforts and the industrial development of new products for 3D video coding
and display.
After presenting the main video representation formats, the main charac-
teristics of the standard coding algorithms have been introduced. This
includes the coding tools for stereo images and multiview, as well as for
the multiview video-plus-depth. In the latter, some approaches encode the
texture and depth information independently, by exploiting the signals
'
inherent characteristics. Other tools exploit the correlations between the
texture video and its associated depth-map, to improve the compression
performance. Also distortion models have been described, which evaluate
the final distortion on the synthesized views distortion rather than on the
depth map distortion.
With the demand for a more immersive experience, and the availability of
more advanced displays, which use a very large number of views, the
holoscopic format has emerged as a possible solution. However, due to the
increased amount of data required, a non-scalable 3DHV coding algorithm is
described, for exploiting the cross-correlation that exists between neighbor-
ing micro-images. In general, this chapter covers several topics about 3D
video representation and coding, providing a set of valuable references,
which enables someone interested to get involved in this topic.
Acknowledgements The authors would like to thank the Interactive Visual Media Group of
Microsoft Research and National Institute of Information and Communications Technology
(NICT), for providing the
Ballet
and
Breakdancers
and
Shark
data set, respectively
,
for research
purposes.
References
1. CISCO (2014) Cisco visual networking index: forecast and methodology, 2013-2018. White
paper
2. Vetro A, Tourapis A, M¨ ller K, Chen T (2011) 3D-TV content storage and transmission. IEEE
Trans Broadcast 57(2):384-394
3. Zilly F, Kluger J, Kauff P (2011) Production rules for stereo acquisition. Proc IEEE
99(4):590-606
