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Theory of Optimal View Interpolation with Depth Inaccuracy

  • Keita Takahashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Depth inaccuracy greatly affects the quality of free-viewpoint image synthesis. A theoretical framework for a simplified view interpolation setup to quantitatively analyze the effect of depth inaccuracy and provide a principled optimization scheme based on the mean squared error metric is proposed. The theory clarifies that if the probabilistic distribution of disparity errors is available, optimal view interpolation that outperforms conventional linear interpolation can be achieved. It is also revealed that under specific conditions, the optimal interpolation converges to linear interpolation. Furthermore, appropriate band-limitation combined with linear interpolation is also discussed, leading to an easy algorithm that achieves near-optimal quality. Experimental results using real scenes are also presented to confirm this theory.

Keywords

Linear Interpolation Target Image Optimal Interpolation Real Scene Gain Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Keita Takahashi
    • 1
  1. 1.IRT Research InitiativeThe University of TokyoTokyoJapan

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