Stochastic Vector QuantisersThis paper was submitted to a Special Issue of IEEE Trans. Information Theory on Information-Theoretic Imaging on 30 July 1999. It was not accepted for publication, but it underpins several subsequently published papers

In this paper a stochastic generalisation of the standard Linde-BuzoGray (LBG) approach to vector quantiser (VQ) design is presented, in which the encoder is implemented as the sampling of a vector of code indices from a probability distribution derived from the input vector, and the decoder is implemented as a superposition of reconstruction vectors, and the stochastic VQ is optimised using a minimum mean Euclidean reconstruction distortion criterion, as in the LBG case. Numerical simulations are used to demonstrate how this leads to self-organisation of the stochastic VQ, where different stochastically sampled code indices become associated with different input subspaces. This property may be used to automate the process of splitting high-dimensional input vectors into low-dimensional blocks before encoding them.