1 bioinspired compression schemas 16/07/2009 khaled masmoudi pierre kornprobst inria marc...
TRANSCRIPT
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Bioinspired Compression Schemas
16/07/2009
Khaled MASMOUDIPierre KORNPROBST INRIAMarc ANTONINI I3S
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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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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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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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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