Method of picture compression by auto-organisation of a neural network

Claim 1

Method of picture compression by autoorganisation of a neural network, characterised in that it comprises: a preprocessing during which pictures of identical type are partitioned into pixel blocks with dimensions smaller than those of the pictures, a phase of initialising a neural network comprising N neurons disposed in a single plane and having a number of inputs equal to the dimension of the blocks extracted from the pictures, during which values of coefficients, or weights, are randomly allocated to each connection linking a neuron j to an input ei, and an initial neighbourhood radius Vo determining the neighbour neurons of the neuron j is chosen a learning phase during which the neural network is fed successively with pixel blocks extracted randomly from the various pictures and the weights of the neural connections evolve by successive approximations following an algorithm simulating the behaviour of the visual cortex, through a computation of potentials which aims to elect one neuron with updating of its neighbours, until their values are as close as possible to most probable input values, a compression phase during which the source picture is partitioned into pixel blocks, each block being presented one after the other to the inputs of the neural network and coded as a function of the neuron of which the values of the weights are closest to the inputs.