This paper introduces a novel adaptive cascade architecture for image compression. The idea is an extension of parallel neural network (NN) architectures which have been previously used for image compression. It is shown that the proposed technique results in higher image quality for a given compression ratio than existing NN image compression schemes. It is also shown that training of the proposed architecture is significantly faster than that of other NN-based
techniques and that the number of learning parameters is small. This allows the coding process to include adaptation of the learning parameters, thus, compression does not depend on the selection of the training set as in previous single and parallel structure NN.