Geometric prediction structure for multiview video coding

Abstract

One of the critical issues to successful service of 3D video is how to compress huge amount of multi-view video data efficiently. In this paper, we described about geometric prediction structure for multi-view video coding. By exploiting the geometric relations between each camera pose, we can make prediction pair which maximizes the spatial correlation of each view. To analyze the relationship of each camera pose, we defined the mathematical view center and view distance in 3D space. We calculated virtual center pose by getting mean rotation matrix and mean translation vector. We proposed an algorithm for establishing the geometric prediction structure based on view center and view distance. Using this prediction structure, inter-view prediction is performed to camera pair of maximum spatial correlation. In our prediction structure, we also considered the scalability in coding and transmitting the multi-view videos. Experiments are done using JMVC (Joint Multiview Video Coding) software on MPEG-FTV test sequences. Overall performance of proposed prediction structure is measured in the PSNR and subjective image quality measure such as PSPNR.

Cite this paper

@article{lee2010geometric,
  title = {{Geometric prediction structure for multiview video coding}},
  author = {Lee, Seok and Wey, Ho-Cheon and Park, Du‐Sik},
  journal = {Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE},
  volume = {7524},
  pages = {75241A},
  year = {2010},
  doi = {10.1117/12.839845}
}

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