Another look at the retina as an image scalar quantizer

We investigate, in this paper, the processing of stimuli in the mammalians retina, and raise the analogy between the biological mechanisms involved and already existing analog-to-digital converters functioning. Besides, we propose a possible decoding procedure for the retina neural code under the restrictions of the model presented. The coder/decoder, we describe here, focuses on the temporal behavior of the three last retina layers. As time goes, our system gradually changes from a quasi-uniform quantizer to a highly non-linear one. Besides, high magnitude stimuli are well refined, while small magnitudes are coarsely approximated. This yields an original bioinspired quantization system, the behavior of which evolves dynamically during the time interval of stimuli observation. Here, we present a biologically realistic retina model adapted to a temporal signal. Then, we explore the input/output map of the system and its ability to recover the original signal. Further, we make the parallel between this bioinspired system and well known compandor/quantizer systems used for analog-to-digital converters. Finally, we compare the performance of our quantizer to the dead zone uniform scalar quantizer used in JPEG2000, and show a slightly better behavior for low rate transmissions.