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Bioinspired Compression Schemas

16/07/2009

Khaled MASMOUDIPierre KORNPROBST INRIAMarc ANTONINI I3S

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Neuromath kickoff meeting

How to use a retina model for efficient Video coding-decoding ?

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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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What we see Vs what we get

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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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Quality as a functional of spikes number

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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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From bit per pixel to bit per spike

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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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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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Epilogue

• New retinal model :

With video coding efficiency as a design principle

• Get all of that on GPU

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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)