Multi Library Wavelet Neural Network for Lossless Image Compression

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as Wavelet Neural Networks (WNN) and genetic algorithms are being developed to explore the future of image coding. Successful applications of WNN in the case of function approximation become well established. This paper presents a direct solution method based on Multi Library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.