Skip to main content
SpringerLink
Log in

Video Compression Algorithm Based on Neural Network Structures

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

Included in the following conference series:

Abstract

The presented here paper describes a new approach to the video compression problem. Our method uses the neural network image compression algorithm which is based on the predictive vector quantization (PVQ). In this method of image compression two different neural network structures are exploited in the following elements of the proposed system: a competitive neural networks quantizer and a neuronal predictor. For the image compression based on this approach it is important to correctly detect scene changes in order to improve performance of the algorithm. We describe the image correlation method and discuss its effectiveness.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • 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. Generic coding of moving pictures and associated audio information–Part 1: Systems. Int. Telecommun. Union-Telecommun. (ITU-T), Recommendation H.222.0, MPEG-2 Systems (1994)

    Google Scholar 

  2. Narrow-band visual telephone systems and terminal equipment. Int. Telecommun. Union-Telecommun. (ITU-T), Recommendation H.320 (1999)

    Google Scholar 

  3. Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston, A., Peterson, J., Sparks, R., Handley, M., Schooler, E.: SIP: Session initiation protocol. Internet Eng. Task Force (IETF). Request for Comments (RFC), vol. 3261 (2002)

    Google Scholar 

  4. Schulzrinne, H., Casner, S., Frederick, R., Jacobson, V.: RTP: A transport protocol for real-time applications. Internet Eng. Task Force (IETF). Request for Comments (RFC), vol. 1889 (1996)

    Google Scholar 

  5. Sullivan, G.J., Wiegand, T.: Video compression – from concepts to the H.264/AVC standard. Proceedings of the IEEE 93, 18–31 (2005)

    Article  Google Scholar 

  6. Clarke, R.J.: Digital compression of still images and video. Academic Press, London (1995)

    Google Scholar 

  7. Gray, R.: Vector quantization. IEEE ASSP Magazine 1, 4–29 (1984)

    Article  Google Scholar 

  8. Gersho, A., Gray, R.M.: Vector quantization and signal compression. Kluwer Academic Publishers (1992)

    Google Scholar 

  9. Rutkowski, L., Cierniak, R.: Image compression by competitive learning neural networks and predictive vector quantization. International Journal of Applied Mathematics and Computer Science 18, 147–157 (1996)

    Google Scholar 

  10. Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Signal Processing: Image Communication 15, 559–565 (2000)

    Google Scholar 

  11. Cierniak, R.: An image compression algorithm based on neural networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 706–711. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Fowler, J.E., Carbonara, M.R., Ahalt, S.C.: Image coding using differential vector quantization. IEEE Transactions on Circuits and Systems for Video Technology 3, 350–367 (1993)

    Article  Google Scholar 

  13. Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Transactions Circuits Systems CAS-33, 812–818 (1986)

    Article  Google Scholar 

  14. Rutkowski, L.: Identification of MISO nonlinear regressions in the presence of a wide class of disturbances. IEEE Transactions on Information Theory IT-37, 214–216 (1991)

    Article  MathSciNet  Google Scholar 

  15. Greblicki, W., Rutkowska, D., Rutkowski, L.: An orthogonal series estimate of time-varying regression. Annals of the Institute of Statistical Mathematics 35, 215–228 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  16. Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Rutkowski, L., Przybyl, A., Cpalka, K.: Novel on-line speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Transactions on Industrial Electronics 59, 1238–1247 (2012)

    Article  Google Scholar 

  18. Korytkowski, M., Rutkowski, L., Scherer, R.: On combining backpropagation with boosting. In: IEEE International Joint Conference on Neural Network (IJCNN) Proceedings, Vancouver, July 16-21, vols. 1-10, pp. 1274–1277 (2006)

    Google Scholar 

  19. Greenfield, S., Chiclana, F.: Type-reduction of the discretized interval type-2 fuzzy set: approaching the continuous case through progressively finer discretization. Journal of Artificial Intelligence and Soft Computing Research 1, 193 (2011)

    Google Scholar 

  20. Pietruczuk, L., Duda, P., Jaworski, M.: A new fuzzy classifier for data streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS (LNAI), vol. 7267, pp. 318–324. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  21. Jaworski, M., Duda, P., Pietruczuk, L.: On fuzzy clustering of data streams with concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS (LNAI), vol. 7268, pp. 82–91. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  22. Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Transactions on Knowledge and Data Engineering 25, 1272–1279 (2013)

    Article  Google Scholar 

  23. Pietruczuk, L., Duda, P., Jaworski, M.: Adaptation of decision trees for handling concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS (LNAI), vol. 7894, pp. 459–473. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  24. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Transactions on Knowledge and Data Engineering 26, 108–119 (2014)

    Article  Google Scholar 

  25. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Information Sciences 266, 1–15 (2014)

    Google Scholar 

  26. Chen, L., Feris, R., Turk, M.: Efficient partial shape matching using Smith-Waterman algorithm. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW, pp. 1–6 (2008)

    Google Scholar 

  27. Adhikari, P., Gargote, N., Digge, J., Hogade, B.G.: Abrupt scene change detection. World Academy of Science, Engineering and Technology 18, 687–692 (2008)

    Google Scholar 

  28. Seeling, P.: Scene change detection for uncompressed video. In: Technological Developments in Education and Automation, pp. 11–14 (2010)

    Google Scholar 

  29. Li, Z., Liu, G.: A novel scene change detection algorithm based on the 3D wavelet transform. In: IEEE International Conference on Image Processing, ICIP, pp. 1536–1539 (2008)

    Google Scholar 

  30. Radwan, N.I., Salem, N.M., El Adawy, M.I.: Histogram correlation for video scene change detection. In: Wyld, D.C., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications. AISC, vol. 166, pp. 765–773. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Armii Krajowej 36, 42-200, Czestochowa, Poland

    Michal Knop & Robert Cierniak

  2. Department of Electrical Engineering, M. S. University of Baroda, Vadodara, India

    Nimit Shah

Authors
  1. Michal Knop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Cierniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nimit Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Knop, M., Cierniak, R., Shah, N. (2014). Video Compression Algorithm Based on Neural Network Structures. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07173-2_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation