Grayscale Images and RGB Video: Compression by Morphological Neural Network

  • Osvaldo de Souza
  • Paulo César Cortez
  • Francisco A. T. F. da Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)


This paper investigates image and RGB video compression by a supervised morphological neural network. This network was originally designed to compress grayscale image and was then extended to RGB video. It supports two kinds of thresholds: a pixel-component threshold and pixel-error counting threshold. The activation function is based on an adaptive morphological neuron, which produces suitable compression rates even when working with three color channels simultaneously. Both intra-frame and inter-frame compression approaches are implemented. The PSNR level indicates that the compressed video is compliant with the desired quality levels. Our results are compared to those obtained with commonly used image and video compression methods. Network application results are presented for grayscale images and RGB video with a 352 × 288 pixel size.


Supervised Morphological Neural Network RGB Video Compression Image Compression 


  1. 1.
    Winkler, S., van den Branden Lambrecht, C.J., Kunt, M.: Vision Models and Applications to Image and Video Processing, p. 209. Springer (2001)Google Scholar
  2. 2.
    Reddy, et al.: Image Compression and Reconstruction Using a New Approach by Artificial Neural Network. International Journal of Image Processing (IJIP) 6(2), 68–85 (2012)Google Scholar
  3. 3.
    Cramer, C., Gelenbe, E., Bakircloglu, H.: Low Bit-rate Video Compression with Neural Networks and Temporal Subsampling. Proceedings of the IEEE 84(10), 1529–1543 (1996)CrossRefGoogle Scholar
  4. 4.
    Vaddella, R.P.V., Rama, K.: Artificial Neural Networks for Compression of Digital images: A Review. International Journal of Reviews in Computing, 75–82 (2010)Google Scholar
  5. 5.
    Singh, M.P., Arya, K.V., Sharma, K.: Video Compression Using Self-Organizing Map and Pattern Storage Using Hopfield Neural Network. In: International Conference on Industrial and Information Systems (ICIIS), December 28-31, pp. 272–278 (2009)Google Scholar
  6. 6.
    García-Rodríguez, J., Domínguez, E., Angelopoulou, A., Psarrou, A., Mora-Gimeno, F.J., Orts, S., García-Chamizo, J.M.: Video and Image Processing with Self-Organizing Neural Networks. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part II. LNCS, vol. 6692, pp. 98–104. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Khashman, A.: Neural Networks Arbitration for Optimum DCT Image Compression. In: IEEE Eurocon (2007) Google Scholar
  8. 8.
    Banon, G.J.F.: Characterization of Translation Invariant Elementary Morphological Operators Between Gray-level Images. INPE, São José dos Campos, SP, Brasil (1995) Google Scholar
  9. 9.
    Banon, G.J.F., Faria, S.D.: Morphological Approach for Template Matching. In: Brazilian Symposium on Computer Graphics and Image Processing Proceedings. IEEE Computer Society (1997)Google Scholar
  10. 10.
    Faria, S.D.: Uma abordagem morfológica para casamento de padrões, Master Tesis, National Institute for Space Research, INPE-6346-RDI/597 (1997)Google Scholar
  11. 11.
    Silva, F.A.F.S., Banon, G.J.F.: Rede morfológica não supervisionada (RMNS). In: IV Brazilian Conference on Neural Networks, pp. 400–405 (1999)Google Scholar
  12. 12.
    Banon, G.J.F., Barrera, J.: Decomposition of Mappings Between Complete Lattices by Mathematical Morphology – Part I: General Lattices. Signal Processing 30(3), 299–327 (1993)zbMATHCrossRefGoogle Scholar
  13. 13.
    Foreman, Video stream for tests,
  14. 14.
    Heo, J., Ho, Y.-S.: Efficient Differential Pixel Value Coding in CABAC for H.264/AVC Lossless Video Compression. Circuits, Systems and Signal Processing 31(2), 813–825 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kaminsky, E., Grois, D., Hadar, O.: Dynamic Computational Complexity and Bit Allocation for Optimizing H.264/AVC Video Compression. Journal of Visual Communication and Image Representation 19(1), 56–74 (2008)CrossRefGoogle Scholar
  16. 16.
    Saha, A., Mukherjee, J., Sural, S.: A Neighborhood Elimination Approach for Block Matching in Motion Estimation. Signal Processing, Image Communication 26(8), 438–454 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Osvaldo de Souza
    • 1
  • Paulo César Cortez
    • 1
  • Francisco A. T. F. da Silva
    • 2
  1. 1.DETIFederal University of CearáFortalezaBrazil
  2. 2.National Institute For Space ResearchROENEusébioBrazil

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