Image compression using a self-organized neural network

In the research described by this paper, we implemented and evaluated a linear self-organized feedforward neural network for image compression. Based on the Generalized Hebbian Learning Algorithm (GHA), the neural network extracts the principle components from the auto-correlation matrix of the input images. To do so, an image is first divided into mutually exclusive square blocks of size m x m. Each block represents a feature vector ofm2 dimension in the feature space. The input dimension of the neural net is therefore m2 and the output dimension is m. Training based on GHA for each block then yields a weight matrix with dimension of m x m2, rows ofwhich are the eigenvectors ofthe auto-correlation matrix of the input image block. Projection of each image block onto the extracted eigenvectors yields m coefficients for each block. Image compression is then accomplished by quantizing and coding the coefficients for each block. To evaluate the performance ofthe neural network, two experiments were conducted using standard IEEE images. First, the neural net was implemented to compress images at different bit rates using different block sizes. Second, to test the neural network's generalization capability, the sets ofprinciple components extracted from one image was used for compressing different but statistically similar images. The evaluation, based on both visual inspection and statistical measures (NMSE and SNR) of the reconstructed images, demonstrates that the network can yield satisfactoiy image compression performance and possesses a good generalization capability.