In order to explore visual coding strategies, we use a wavelet-like transform which output is sparse, as is observed in the primary visual areas [ 6]. This transform is defined in the context of a feed-forward spiking neural network, and the output is the list of its neurons’ spikes: it is recursively constructed using a greedy matching pursuit scheme which first selects best matches and then laterally interacts with its correlated neighbors. We study the quality of this algorithm and its enhancement by the prior knowledge of the statistics of its input, namely natural images. An application to image compression is shown which is comparable to other techniques such as JPEG at low bit compression.
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