[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Error Concealment by Means of Motion Refinement and Regularized Bregman Divergence

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2012 (IDEAL 2012)

Abstract

This work addresses the problem of error concealment in video transmission systems over noisy channels employing Bregman divergences along with regularization. Error concealment intends to improve the effects of disturbances at the reception due to bit-errors or cell loss in packet networks. Bregman regularization gives accurate answers after just some iterations with fast convergence, better accuracy and stability. This technique has an adaptive nature: the regularization functional is updated according to Bregman functions that change from iteration to iteration according to the nature of the neighborhood under study at iteration n. Numerical experiments show that high-quality regularization parameter estimates can be obtained. The convergence is sped up while turning the regularization parameter estimation less empiric, and more automatic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bregman, M.: The Relaxation Method of Finding the Common Point of Convex Sets and its Application to the Solution of Problems in Convex Programming. USSR Comt. Math and Math. Ph. 7(3), 200–217 (1967)

    Article  Google Scholar 

  2. Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems. J. Willey & Sons (1977)

    Google Scholar 

  3. Stutz, D.: Restauração de Imagens em Escala Nanométrica com Funcional de Regularização de Tikhonov e Computação Paralela. M.Sc. thesis, IPRJ/UERJ, N. Friburgo, RJ, Brazil (2004)

    Google Scholar 

  4. Stutz, D., Silva Neto, A.J., Farias, R.C.: Information Weighted Mean Square Error (IWMSE): Uma Medida de Comparação de Imagens Baseada na Percepção, X EMC, N. Friburgo, RJ, Brazil (2007)

    Google Scholar 

  5. Galatsanos, N.P., Katsaggelos, A.K.: Methods for Choosing the Regularization Parameter and Estimating the Noise Variance in Image Restoration and their Relation. IEEE Trans. on Im. Proc., 322–336 (1992)

    Google Scholar 

  6. Coelho, A.M., Estrela, V.V.: EM-Based Mixture Models Applied to Video Event Detection. In: Principal Component Analysis - Engineering Applications, pp. 102–124. Intech (2012) ISBN 9788563337214, http://www.intechopen.com/books/principal-component-analysis-engineering-applications/em-based-mixture-models-applied-to-video-event-detection

  7. Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An Iterative Regularization Method for Total Variation Based Image Restoration. Multiscale Modeling Sim. 4, 460–489 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.: Information Geometry of U-Boost and Bregman Divergence. Neural Comput. 16, 1437–1481 (2004)

    Article  MATH  Google Scholar 

  9. Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S.: An Information Theoretic Analysis of Maximum Likelihood Mixture Estimation for Exponential Families. In: Proc. 21st ICML (2004)

    Google Scholar 

  10. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, 2nd edn. Springer, New York (2006)

    MATH  Google Scholar 

  11. Hinterberger, W., Scherzer, O.: Models for Image Interpolation Based on the Optical Flow. Computing 66, 231–247 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Grossauer, H.: Inpainting of Movies Using Optical Flow, Math. Models for Registration and Applications to Med. Imaging, Math. Ind., vol. 10, pp. 151–162. Springer, Berlin (2006)

    Book  Google Scholar 

  13. Slesareva, N., Bruhn, A., Weickert, J.: Optic Flow Goes Stereo: A Variational Method for Estimating Discontinuity-Preserving Dense Disparity Maps. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 33–40. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Li, X., Jackson, J.R., Katsaggelos, A.K., Mersereau, R.M.: Multiple Global Affine Motion Model for H.264 Video Coding with Low Bit Rate. In: Proc. SPIE VCIP, San Jose, CA (2005)

    Google Scholar 

  15. Coelho, A.M., Estrela, V.V., de Assis, J.T.: Error Concealment by Means of Clustered Blockwise PCA. In: IEEE Picture Coding Symposium, Chicago, IL, USA (2009)

    Google Scholar 

  16. do Carmo, F.P., Estrela, V.V., de Assis, J.T.: Estimating Motion with Principal Component Regression Strategies. In: Proc. of IEEE MMSP 2009, Rio de Janeiro, RJ, Brazil (2009)

    Google Scholar 

  17. Coelho, A.M., Estrela, V.V.: Data-Driven Motion Estimation with Spatial Adaptation. Intl. J. of Image Proc (IJIP) 6(1), 53–67 (2012), http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume6/Issue1/IJIP-513.pdf

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Coelho, A.M., Estrela, V.V., do Carmo, F.P., Fernandes, S.R. (2012). Error Concealment by Means of Motion Refinement and Regularized Bregman Divergence. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_78

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32639-4_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32638-7

  • Online ISBN: 978-3-642-32639-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics