We present a VQ based technique for coding image data that, like closed loop VXC, adopts a analysis by synthesis approach. We define a new type of spatial interaction model for image data, called prediction pattern, which we use along with a quantized excitation (residuals) vector, to generate an approximation of an input block of pixels. A prediction pattern is simply a k × k array with each element representing a prediction scheme from a given set of predictors. A prediction pattern captures the spatial dependencies present in an image block. Given an image, a set of prediction schemes and a codebook of prediction patterns, we encode an image by partitioning it into blocks and for each block identifying the prediction pattern from within the codebook that best models the spatial dependencies that are present in the block. Having identified this prediction pattern we then search the residual codebook for a code vector that in combination with the already chosen prediction pattern results in the synthesis of the closest approximation to the current image block. The problem is to design an optimal set of prediction schemes and an optimal codebook of prediction patterns, given an image (or class of images). We present algorithms for codebook design and give implementation results on a few standard images. Preliminary results give substantial (between 2 or 3 db) improvements over a simple implementation of full search VQ.