An end-to-end spike-based image compression architecture

In this paper, we discover the special properties of neurons in terms of compression. Neurons are able to transform a visual stimulus into a sequence of discrete biphasic events, called spikes trains, forming the neural code. The neural spike generation properties are beneficial to image processing community as the neural code is very compact, yet informative enough, to be used in the input stimulus recovery. We show that the spike-based compression enables to improve the reconstruction quality in time which is a completely novel feature compared to compression standards. In addition, we mathematically prove that the proposed neuro-inspired mechanism behave either as a uniform or a non-uniform quantizer depending on its parameter. Last but not least, we build an end-to-end spike-based coding/decoding architecture that first transforms an image with a DCT filter and then, it generates spikes to compress the transformed coefficients. Based on these spike trains we reconstruct the input image and we estimate the rate-distortion performance of the whole system. Last but not least, we provide numerical results that confirm that our proposed architecture is much more efficient compared to the JPEG standards.