Skip to main content

Spatial Temporal Prediction for Video Data Compression

  • Chapter
  • 125 Accesses

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 378))

Abstract

A spatial-temporal correlation technique for video data prediction is described in this paper. This technique is based on the analysis of the local correlation of video images. The mathematical formulation extends the current existing spatial correlation model to both spatial and temporal domain. Our proposed technique can be described mathematically as the minimization of an objective function under the least mean square error criterion. With the statistical analysis of video signal, the solution of the prediction problem becomes the problem of designing a suitable spatial-temporal kernel. This can be dealt with by solving a set of linear algebraic equations to determine the kernel coefficients. In our study, we designed a 2x2x2 (x-y-t) three-dimensional kernel. The experiments using the commercial TV program and video tapes were conducted and the results were encouraging.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. Besag, Spatial Interaction and the Statistical Analysis of Lattice Systems, Journal of Royal Statistical Society, Vol. 35, No. 2, pp. 193–236, 1973.

    Google Scholar 

  2. B. Chitprasert and K.R. Rao, Human Visual Weighted Progressive Image Transmission, IEEE Transactions on Communications, Vol. 38, No. 7, pp. 1040–1044, July 1990.

    Article  Google Scholar 

  3. H. Derin and H. Elliott, Model and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Trans. on Pattern Analysis, Machine Intelligence, Vol. PAMI-9, No. 1, pp. 39–55, January 1987.

    Article  Google Scholar 

  4. H. Derin and P.A. Kelly, Discrete-index Markov-type Random Processes, Proceedings of IEEE, Vol. 77, No. 10, pp. 1485–1509, October 1989.

    Article  Google Scholar 

  5. S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. PAMI-6, No.6, pp. 721–741, Nov. 1984.

    Article  Google Scholar 

  6. B.G. Haskell, F.W. Mounts, and J.C. Candy, Interframe Coding of Videotelephone Picture, Proceedings of IEEE, Vol. 60, No. 7, pp. 792–800, July 1972.

    Article  Google Scholar 

  7. P.G. Hoel, S.C. Port and C.J. Stone, Introduction to Stochastic Processes, Waveland Press, INC. Prospect Heights, Illinois, 1987.

    Google Scholar 

  8. A.K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliff, NJ, 1989.

    MATH  Google Scholar 

  9. J. Konard and E. Dubois, Bayesian Estimation of Motion Vector Field, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 9, pp. 910–927, September 1992.

    Article  Google Scholar 

  10. W. Qian and D.M. Titterington, “Multidimensional Markov Chain Models for Image Textures,” Journal of Royal Statistical Society (B), Vol. 53, No. 3, pp. 661–674, 1991.

    MathSciNet  MATH  Google Scholar 

  11. M. Rabbani, and P.W. Jones, Digital Image Compression Techniques, SPIE-The International Society for Optical Engineering, Bellingham, WA, 1991.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Li, H.H., Sun, S. (1997). Spatial Temporal Prediction for Video Data Compression. In: Li, H.H., Sun, S., Derin, H. (eds) Video Data Compression for Multimedia Computing. The Springer International Series in Engineering and Computer Science, vol 378. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6239-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-6239-9_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7862-4

  • Online ISBN: 978-1-4615-6239-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics