Adaptive image coding using multilayer neural networks

A data compression technique based on neural networks is presented. The schema consists of multiple multilayer perceptron networks, which produce a transformation of the original image with a reduced redundancy. A perceptron with a hidden layer is used; the input and output layers have the same number of nodes, while in the middle the number is reduced, thus producing a data compression of the original information. The transformation is carried out by the neural networks in an adaptive way. A split segmentation, based on spatial activities of regions, is applied to the original image in order to locate uniform blocks. A higher ratio between the input and the hidden nodes is used with large blocks and a lower one with smaller blocks; details are then retained in a good way. Major advantages of the proposed approach lie in its good performance, even with images outside the training set