A Bio-Inspired Image Coder with Temporal Scalability

  • Khaled Masmoudi
  • Marc Antonini
  • Pierre Kornprobst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)


We present a novel bio-inspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with time-delays. The second stage is a time-dependent analog-to-digital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.


Static image compression bio-inspired signal coding retina 


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  1. 1.
    Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Transactions on Image Processing (1992)Google Scholar
  2. 2.
    Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 31(4), 532–540 (1983)CrossRefGoogle Scholar
  3. 3.
    Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: An overview. IEEE Transactions on Consumer Electronics 16(4), 1103–1127 (2000)CrossRefGoogle Scholar
  4. 4.
    Clark, A., et al.: Electrical picture-transmitting system. US Patent assigned to AT& T (1928)Google Scholar
  5. 5.
    Crowley, J., Stern, R.: Fast computation of the difference of low-pass transform. IEEE Transactions on Pattern Analysis and Machine Intelligence (2), 212–222 (2009)Google Scholar
  6. 6.
    Field, D.: What is the goal of sensory coding? Neural Computation 6(4), 559–601 (1994)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Gollisch, T., Meister, M.: Eye smarter than scientists believed: Neural computations in circuits of the retina. Neuron. 65(2), 150–164 (2010)CrossRefGoogle Scholar
  8. 8.
    Graham, D., Field, D.: Efficient coding of natural images. New Encyclopedia of Neuroscience (2007)Google Scholar
  9. 9.
    Linares-Barranco, A., Gomez-Rodriguez, F., Jimenez-Fernandez, A., Delbruck, T., Lichtensteiner, P.: Using FPGA for visuo-motor control with a silicon retina and a humanoid robot. In: Proceedings of ISCAS 2007, pp. 1192–1195. IEEE, Los Alamitos (2007)Google Scholar
  10. 10.
    Masmoudi, K., Antonini, M., Kornprobst, P.: Another look at the retina as an image scalar quantizer. In: Proceedings of ISCAS 2010, pp. 3076–3079. IEEE, Los Alamitos (2010)Google Scholar
  11. 11.
    Masmoudi, K., Antonini, M., Kornprobst, P.: Exact reconstruction of the rank order coding using frames theory. ArXiv e-prints (2011), http://arxiv.org/abs/1106.1975v1
  12. 12.
    Masmoudi, K., Antonini, M., Kornprobst, P., Perrinet, L.: A novel bio-inspired static image compression scheme for noisy data transmission over low-bandwidth channels. In: Proceedings of ICASSP, pp. 3506–3509. IEEE, Los Alamitos (2010)Google Scholar
  13. 13.
    Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.: Adaptive color image compression based on visual attention. In: Proceedings of IEEE ICIAP, pp. 416–421. IEEE, Los Alamitos (2002)Google Scholar
  14. 14.
    Perrinet, L.: Sparse Spike Coding: applications of Neuroscience to the processing of natural images. In: Proceedings of SPIE, the International Society for Optical Engineering, number ISSN (2008)Google Scholar
  15. 15.
    Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E., Simoncelli, E.: Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454(7207), 995–999 (2008)CrossRefGoogle Scholar
  16. 16.
    Rodieck, R.: Quantitative analysis of the cat retinal ganglion cells response to visual stimuli. Vision Research 5(11), 583–601 (1965)CrossRefGoogle Scholar
  17. 17.
    Sterling, P., Cohen, E., Smith, R., Tsukamoto, Y.: Retinal circuits for daylight: why ballplayers don’t wear shades. Analysis and Modeling of Neural Systems, 143–162 (1992)Google Scholar
  18. 18.
    Taubman, D.: High performance scalable image compression with ebcot. IEEE Transactions on Image Processing 9(7), 1158–1170 (2000)CrossRefGoogle Scholar
  19. 19.
    Thorpe, S., Gautrais, J.: Rank order coding. Computational Neuroscience: Trends in Research 13, 113–119 (1998)CrossRefGoogle Scholar
  20. 20.
    Van Rullen, R., Thorpe, S.: Rate coding versus temporal order coding: What the retinal ganglion cells tell the visual cortex. Neural Computation 13, 1255–1283 (2001)CrossRefMATHGoogle Scholar
  21. 21.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004), http://www.cns.nyu.edu/~zwang/ CrossRefGoogle Scholar
  22. 22.
    Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  23. 23.
    Wohrer, A., Kornprobst, P.: Virtual retina: A biological retina model and simulator, with contrast gain control. Journal of Computational Neuroscience 26(2), 219–249 (2009)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Wohrer, A., Kornprobst, P., Antonini, M.: Retinal filtering and image reconstruction. Research Report RR-6960, INRIA (2009), http://hal.inria.fr/inria-00394547/en/
  25. 25.
    Zhang, Y., Ghodrati, A., Brooks, D.: An analytical comparison of three spatio-temporal regularization methods for dynamic linear inverse problems in a common statistical framework. Inverse Problems 21, 357 (2005)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Khaled Masmoudi
    • 1
  • Marc Antonini
    • 1
  • Pierre Kornprobst
    • 2
  1. 1.I3S laboratoryUNS–CNRSSophia-AntipolisFrance
  2. 2.NeuroMathComp Team ProjectINRIASophia-AntipolisFrance

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