Artificial Neural Network Approach For Image Compression : A Survey

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.Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding.This paper discusses various neural network architectures for image compression. Among the architectures presented are the Back-Propagation networks (BPN),Kohonen self-organized maps (KSOM),Hierarchical Self-organized maps (HSOM), Modular Neural networks (MNN), Wavelet neural Networks, Fractal Neural Networks, and Cellular Neural Networks (CNN).