Frames for Exact Inversion of the Rank Order Coder

Khaled Masmoudi 1 Marc Antonini 1 Pierre Kornprobst 2
2 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Our goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Thorpe . who stated that the order in which the retina cells are activated encodes for the visual stimulus. Based on this idea, the authors proposed in a rank order coder/decoder associated to a retinal model. Though, it appeared that the decoding procedure employed yields reconstruction errors that limit the model bit-cost/quality performances when used as an image codec. The attempts made in the literature to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. Our contribution is twofold. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we design a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with over than 265 dB of increase in the peak signal-to-noise ratio quality compared to . Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations.
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Submitted on : Wednesday, July 17, 2013 - 2:13:46 PM
Last modification on : Monday, November 5, 2018 - 3:52:01 PM

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Khaled Masmoudi, Marc Antonini, Pierre Kornprobst. Frames for Exact Inversion of the Rank Order Coder. IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2012, 23 (2), pp.353--359. ⟨10.1109/TNNLS.2011.2179557⟩. ⟨hal-00845591⟩

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