We asked whether rank order coding could be used to define an efficient compression scheme for still images. The main hypothesis underlying this work is that the mammalians retina generates a compressed neural code for the visual stimuli. The main novelty of our approach is to show how this neural code can be exploited in the context of image compression. Our coding scheme is a combination of a simplified spiking retina model and well known data compression techniques and consists in three main stages. The first stage is the bio-inspired retina model proposed by Thorpe et al. This model transforms of a stimulus into a wave of electrical impulses called spikes. The major property of this retina model is that spikes are ordered in time as a function of the cells activation: this yields the so-called rank order code (ROC). ROC states that the first wave of spikes give a good estimate of the input signal. In the second stage, we show how this wave of spikes can be expressed using a 4-ary dictionary alphabet: the stack run coding. The third stage consists in applying, to the stack run code, a arithmetic coder of the first order. We then compare our results to the JPEG standards and we show that our model offers similar rate/quality trade-off until 0.07 bpp, for a lower computational cost. In addition, our model offers interesting properties of scalability and of robustness to noise
