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# Adaptive sparse spike coding : applications of neuroscience to the compression of natural images

• for a modern implementation, see this review on sparse models for computer vision

• get the preprint (also on arXiV or locally)

• program of the SPIE meeting in Strasbourg

Session 4:  Compression Technologies
Date: Tuesday 8 April
Time: 08:30 - 10:30
Session Chair: Peter Schelkens, Vrije Univ. Brussel (Belgium)
Paper 7000-15
Time: 08:30 - 09:10
• see the presentation

 Figure 2: The Golden Laplacian Pyramid. To represent the edges of the image at different levels, we may use a simple recursive approach constructing progressively a set of images of decreasing sizes, from a base to the summit of a pyramid. Using simple down-scaling and up-scaling operators we may approximate well a Laplacian operator. This is represented here by stacking images on a Golden Rectangle, that is where the aspect ratio is the golden section $\phi \eqdef \frac{1+\sqrt{5}}{2}$. We present here the base image on the left and the successive levels of the pyramid in a clockwise fashion (for clarity, we stopped at level $8$). Note that here we also use $\phi^2$ (that is $\phi+1$) as the down-scaling factor so that the resolution of the pyramid images correspond across scales. Note at last that coefficient are very kurtotic: most are near zero, the distribution of coefficients has long tails.

## reference

• Laurent Perrinet. Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images, URL . In Optical and Digital Image Processing Conference 7000 - Proceedings of SPIE Volume 7000, 7 - 11 April 2008, pages 15 - S4. 2008 abstract.

 This work was supported by European integrated project FP6-015879, "FACETS".

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