NDLI: Reliability-based generation and view synthesis in layered depth video

Content Provider	IEEE Xplore Digital Library
Author	Muller, K. Smolic, A. Dix, K. Kauff, P. Wiegand, T.
Copyright Year	2008
Description	Author affiliation: Image Process. Dept., Fraunhofer Inst. for Telecommun. - Heinrich-Hertz-Inst., Berlin (Muller, K.; Smolic, A.; Dix, K.; Kauff, P.; Wiegand, T.)
Abstract	In this paper, a system for video rendering on multiscopic 3D displays is considered where the data is represented as layered depth video (LDV). This representation consists of one full or central video with associated per-pixel depth and additional residual layers. Thus, only one full view with additional residual data needs to be transmitted. The LDV data is used at the receiver to generate all intermediate views for the display. The paper presents the LDV layer extraction as well as the view synthesis, using a scene reliability-driven approach. Here, unreliable image regions are detected and in contrast to previous approaches the residual data is enlarged to reduce artifacts in unreliable areas during rendering. To provide maximum data coverage, the residual data remains at its original positions and will not be projected towards the central view. The view synthesis process also uses this reliability analysis to provide higher quality intermediate views than previous approaches. As a final result, high quality intermediate views for an existing 9-view auto-stereoscopic display are presented, which prove the suitability of the LDV approach for advanced 3D video (3DV) systems.
Starting Page	34
Ending Page	39
File Size	1618289
Page Count	6
File Format	PDF
ISBN	9781424422944
DOI	10.1109/MMSP.2008.4665045
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Three dimensional displays Data mining Image generation Cameras Image edge detection Filtering
Content Type	Text
Resource Type	Article

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