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Published byOctavia Parsons Modified over 9 years ago
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1 Bioinspired Compression Schemas 16/07/2009 Khaled MASMOUDI Pierre KORNPROBST INRIA Marc ANTONINII3S
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2 Neuromath kickoff meeting How to use a retina model for efficient Video coding-decoding ?
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3 The Thorpe retinal model Implement a simplistic model to generate spike trains. What data structures to use to represent them. Estimate the « quality » and « cost » of such a signal. Finally: Are those spike trains suitable for static image compression?
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4 What we see Vs what we get
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5 Quality metrics Is What we get at the end of the decoding similar to what we see before coding Different possibilities experimented for similarity measures: Peak SNR Weightened SNR Mean SSIM
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6 Quality as a functional of spikes number
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7 Cost metrics Use Information theory metrics as Shannon Entropy : Get a theorical Threshold Real encoded image file size What we do really get after Image decomposition Representation transform Lossless Coding
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8 From bit per pixel to bit per spike
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9 Towards video Classical video coding :Video is a series of frames Code first frame than use differential coding Difference is the Schalwijk distance between two possible rank ordered series
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10 Next step Still some things to do with Thorpe Some technical improvement : Use parallelism as in the actual neural circuitry Consider continuity in Spike train generation: Use 2D+t filters Use « Virtual Retina » model to integrate more capabilities in the coder (Gain Control)
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11 Epilogue New retinal model : With video coding efficiency as a design principle Get all of that on GPU
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12 For the « Journal club » Brief summary of W. Bialek Work: ‘’Efficient representation as a design principle for neural coding and computation’’ (2007) Presentation of E. Simoncelli’s : ‘’Spike-triggered neural characterization’’(2006)
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