Artificial Neural Networks for Compression of Digital Images: a Review Venkata

Digital images require large amounts of memory for storage. Thus, the transmission of an image from one computer to another can be very time consuming. By using data compression techniques, it is possible to remove some of the redundant information contained in images, requiring less storage space and less time to transmit. Artificial Neural networks can be used for the purpose of image compression. Successful applications of neural networks to vector quantization have now become well established, and other aspects of neural networks for image compression are stepping up to play significant roles in assisting the traditional compression techniques. This paper discusses various neural network architectures for image compression. Among the architectures presented are the Kohonen self-organized maps (KSOM), Hierarchical Self-organized maps (HSOM), the Back-Propagation networks (BPN), Modular Neural networks (MNN), Wavelet neural Networks, Fractal Neural Networks, Predictive coding Neural Networks and Cellular Neural Networks (CNN). The architecture of these networks, and their performance issues are compared with those of the conventional compression techniques.