Abstract
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.
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References
Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Transactions on Image Processing (1992)
Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 31(4), 532–540 (1983)
Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: An overview. IEEE Transactions on Consumer Electronics 16(4), 1103–1127 (2000)
Clark, A., et al.: Electrical picture-transmitting system. US Patent assigned to AT& T (1928)
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)
Field, D.: What is the goal of sensory coding? Neural Computation 6(4), 559–601 (1994)
Gollisch, T., Meister, M.: Eye smarter than scientists believed: Neural computations in circuits of the retina. Neuron. 65(2), 150–164 (2010)
Graham, D., Field, D.: Efficient coding of natural images. New Encyclopedia of Neuroscience (2007)
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)
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)
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
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)
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)
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)
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)
Rodieck, R.: Quantitative analysis of the cat retinal ganglion cells response to visual stimuli. Vision Research 5(11), 583–601 (1965)
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)
Taubman, D.: High performance scalable image compression with ebcot. IEEE Transactions on Image Processing 9(7), 1158–1170 (2000)
Thorpe, S., Gautrais, J.: Rank order coding. Computational Neuroscience: Trends in Research 13, 113–119 (1998)
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)
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/
Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
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)
Wohrer, A., Kornprobst, P., Antonini, M.: Retinal filtering and image reconstruction. Research Report RR-6960, INRIA (2009), http://hal.inria.fr/inria-00394547/en/
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)
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Masmoudi, K., Antonini, M., Kornprobst, P. (2011). A Bio-Inspired Image Coder with Temporal Scalability. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_41
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DOI: https://doi.org/10.1007/978-3-642-23687-7_41
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