A phantom axon setup for validating models of action potential recordings

Olivier Rossel 1 Fabien Soulier 2 Serge Bernard 2 David Guiraud 1 Guy Cathébras 2
1 CAMIN - Control of Artificial Movement and Intuitive Neuroprosthesis
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
2 SysMIC - Conception et Test de Systèmes MICroélectroniques
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we proposed a new geometric finite mixture model-based adaptive arithmetic coding (AAC) for lossless image compression. Applying AAC for image compression, large compression gains can be achieved only through the use of sophisticated models that provide more accurate probabilistic descriptions of the image. In this work, we proposed to divide the residual image into non-overlapping blocks, and then we model the statistics of each block by a mixture of geometric distributions of parameters estimated through the maximum likelihood estimation using the expectation–maximization algorithm. Moreover, a histogram tail truncation method within each predicted error block is used in order to reduce the number of symbols in the arithmetic coding and therefore to reduce the effect of the zero-occurrence symbols. Experimentally, we showed that using convenient block size and number of mixture components in conjunction with the prediction technique median edge detector, the proposed method outperforms the well known lossless image compressors.
Type de document :
Article dans une revue
Medical and Biological Engineering and Computing, Springer Verlag, 2016, 10 (4), pp.671-678. 〈10.1007/s11517-016-1463-3〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01347422
Contributeur : Isabelle Gouat <>
Soumis le : jeudi 21 juillet 2016 - 06:48:00
Dernière modification le : jeudi 28 juin 2018 - 17:53:15

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Olivier Rossel, Fabien Soulier, Serge Bernard, David Guiraud, Guy Cathébras. A phantom axon setup for validating models of action potential recordings. Medical and Biological Engineering and Computing, Springer Verlag, 2016, 10 (4), pp.671-678. 〈10.1007/s11517-016-1463-3〉. 〈lirmm-01347422〉

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