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A Retina-inspired encoder: An innovative step on image coding using Leaky Integrate-and-Fire neurons

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Abstract

This paper aims to build an image coding system based on a model of the mammalian retina. The retina is the light-sensitive layer of tissue located on the inner coat of the eye and it is responsible for vision. Inspired by the way the retina handles and compresses visual information and based on previous studies we aim to build and analytically study a retinal-inspired image quantizer, based on the Leaky Integrate-and-Fire (LIF) model, a neural model approximating the behavior of the ganglion cells of the Ganglionic retinal layer that is responsible for visual data compression. In order to have a more concrete view of the encoder's behavior, in our experiments , we make use of the spatiotemporal decomposition layers provided by extensive studies on a previous retinal layer, the Outer Plexiform Layer (OPL). The decomposition layers produced by the OPL, are being encoded using our LIF image encoder and then, they are reconstructed to observe the encoder's efficiency.
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Dates and versions

hal-01901140 , version 1 (22-10-2018)

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Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. A Retina-inspired encoder: An innovative step on image coding using Leaky Integrate-and-Fire neurons. ICIP 2018, Oct 2018, Athens, Greece. pp.694-698, ⟨10.1109/ICIP.2018.8451599⟩. ⟨hal-01901140⟩
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