Advertisement

Neural Video Compression Based on PVQ Algorithm

  • Michał KnopEmail author
  • Tomasz Kapuściński
  • Rafał Angryk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10245)

Abstract

In this paper we present a video compression algorithm based on predictive vector quantization, which is a combination of vector quantization and differential pulse code modulation. We optimized the algorithm using chroma subsampling which reduces the amount of information that needs to be processed. This allowed us to combine two color channels into one and thereby reduce the number of predictors and codebooks. Furthermore, we introduced inter-frames which only store regions that changed compared to previous frames, further decreasing the size of compressed data.

Keywords

Video compression Image compression PVQ 

References

  1. 1.
    CCITT: Video codec for audio visual services at px 64 kbits/s (1993)Google Scholar
  2. 2.
    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, vol. 3070, pp. 706–711. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24844-6_108 CrossRefGoogle Scholar
  3. 3.
    Cierniak, R., Knop, M.: Video compression algorithm based on neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7894, pp. 524–531. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38658-9_47 CrossRefGoogle Scholar
  4. 4.
    Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Signal Process. Image Commun. 15(6), 559–565 (2000)CrossRefGoogle Scholar
  5. 5.
    Clarke, R.J.: Digital Compression of Still Images and Video. Academic Press Inc., London (1995)Google Scholar
  6. 6.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Norwell (1991)zbMATHGoogle Scholar
  7. 7.
    Gray, R.: Vector quantization. IEEE ASSP Magaz. 1(2), 4–29 (1984)CrossRefGoogle Scholar
  8. 8.
    Grycuk, R., Knop, M.: Neural video compression based on SURF scene change detection algorithm. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 7. AISC, vol. 389, pp. 105–112. Springer, Cham (2016). doi: 10.1007/978-3-319-23814-2_13 CrossRefGoogle Scholar
  9. 9.
    ITU-R BT.709–6: Parameter values for the HDTV standards for production and international programme exchange (2015)Google Scholar
  10. 10.
    Kerr, D.A.: Chrominance subsampling in digital images. The Pumpkin (3), January 2012Google Scholar
  11. 11.
    Knop, M., Cierniak, R., Shah, N.: Video compression algorithm based on neural network structures. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 715–724. Springer, Cham (2014). doi: 10.1007/978-3-319-07173-2_61 CrossRefGoogle Scholar
  12. 12.
    Knop, M., Dobosz, P.: Neural Video Compression Algorithm. In: Choraś, R.S. (ed.) ICAISC 2014. AISC, vol. 313, pp. 59–66. Springer, Cham (2015). doi: 10.1007/978-3-319-10662-5_8 Google Scholar
  13. 13.
    Knop, M., Kapuściński, T., Mleczko, W.K., Angryk, R.: Neural video compression based on RBM scene change detection algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 660–669. Springer, Cham (2016). doi: 10.1007/978-3-319-39384-1_58 Google Scholar
  14. 14.
    Setton, E., Girod, B.: Video streaming with SP and SI frames. In: Proceedings of Visual Communication and Image Processing (2005)Google Scholar
  15. 15.
    Winkler, S., Kunt, M., van den Branden Lambrecht, C.J.: Vision and video: models and applications. In: van den Branden Lambrecht, C.J. (ed.) Vision Models and Applications to Image and Video Processing, pp. 201–229. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Xiph.org: Video test media. https://media.xiph.org/video/derf/ Accessed 10 Mar 2016
  17. 17.
    Zhang, R., Regunathan, S.L., Rose, K.: Video coding with optimal inter/intra-mode switching for packet loss resilience. IEEE J. Sel. Areas Commun. 18(6), 966–976 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michał Knop
    • 1
    • 2
    Email author
  • Tomasz Kapuściński
    • 1
    • 2
  • Rafał Angryk
    • 3
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzestochowaPoland
  2. 2.Institute of Information Technology, Radom Academy of EconomicsRadomPoland
  3. 3.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

Personalised recommendations