Image Decomposition on the basis of an Inverse Pyramid with 3-layer Neural Networks

The contemporary information technologies and Internet impose high requirements on the image compression efficiency. Great number of methods for information redundancy reduction had already been developed, which are based on the image processing in the spatial or spectrum domain. Other methods for image compression use some kinds of neural networks. In spite of their potentialities, the methods from the last group do not offer high compression efficiency. New adaptive method for Image Decomposition on the basis of an Inverse Pyramid with Neural Networks is presented in this paper. The processed image is divided in blocks and then each is compressed in the space of the hidden layers of 3-layer BPNNs, which build the so-called Inverse Difference Pyramid. The results of the new method modeling are presented for sequence of static images in comparison with results for single images from the same group.