Theory of Optimal View Interpolation with Depth Inaccuracy

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


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.


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.


  1. 1.
    Buehler, C., Bosse, M., McMillan, L., Gortler, S., Cohen, M.: Unstructured lumigraph rendering. In: ACM SIGGRAPH Papers pp. 425–432 (2001)Google Scholar
  2. 2.
    Chai, J., Tong, X., Chany, S.C., Shum, H.Y.: Plenoptic sampling. In: ACM Trans. Graphics (Proc. ACM SIGGRAPH), pp. 307–318 (2000)Google Scholar
  3. 3.
    Chen, S.E., Williams, L.: View interpolation for image synthesis. In: Proc. ACM SIGGRAPH, pp. 279–288 (1993)Google Scholar
  4. 4.
    Girod, B.: The efficiency of motion-compensating prediction for hybrid coding of video sequences. IEEE Journal SAC SAC 5(7), 1140–1154 (1987)Google Scholar
  5. 5.
    Gortler, S.-J., Crzeszczuk, R., Szeliski, R., Cohen, M.-F.: The lumigraph. In: Proc. ACM SIGGRAPH, pp. 43–54 (1996)Google Scholar
  6. 6.
    Kubota, A., Smolic, A., Magnor, M., Tanimoto, M., Chen, T., Zhang, C.: Special issue on multi-view imaging and 3dtv. IEEE Signal Processing Magazine 24(6), 10–111 (2007)CrossRefGoogle Scholar
  7. 7.
    Levoy, M., Hanrahan, P.: Light field rendering. In: Proc. ACM SIGGRAPH, pp. 31–42 (1996)Google Scholar
  8. 8.
    Lin, Z., Shum, H.Y.: A geometric analysis of light field rendering. Intl. Journal of Computer Vision 58(2), 121–138 (2004)CrossRefGoogle Scholar
  9. 9.
    Ramanathan, P., Girod, B.: Rate-distortion analysis for light field coding and streaming. EURASIP SP:IC 21(6), 462–475 (2006)Google Scholar
  10. 10.
    Shade, J.W., Gortler, S.J., He, L.W., Szeliski, R.: Layered depth images. In: Proc. ACM SIGGRAPH, pp. 231–242 (1998)Google Scholar
  11. 11.
    Shum, H.Y., Kang, S.B., Chan, S.C.: Survey of Image-Based Representations and Compression Techniques. IEEE Trans. CSVT 13(11), 1020–1037 (2003)Google Scholar
  12. 12.
    Taguchi, Y., Takahashi, K., Naemura, T.: Real-time all-in-focus video-based rendering using a network camera array. In: Proc. 3DTV-Conference, pp. 241–244 (2008)Google Scholar
  13. 13.
    Takahashi, K., Naemura, T.: Theoretical model and optimal prefilter for view interpolation. In: Proc. IEEE ICIP, pp. 1528–1531 (2008)Google Scholar
  14. 14.
    Tong, X., Chai, J., Shum, H.Y.: Layered lumigraph with lod control. The Journal of Visualization and Computer Animation 13(4), 249–261 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Zhang, C., Chen, T.: Spectral analysis for sampling image-based rendering data. IEEE Trans. CSVT 13(11), 1038–1050 (2003)Google Scholar
  16. 16.
    Zhang, C., Chen, T.: A survey on image-based rendering - representation, sampling and compression. EURASIP SP:IC 19(1), 1–28 (2004)zbMATHGoogle Scholar
  17. 17.
    Zitnick, C., Kang, S.B., Uyttendaele, M., Winder, S., Szeliski, R.: High quality video interpolation using a layered representation. In: ACM SIGGRAPH Papers, pp. 600–608 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations