Enhancement of Image Compression in Neural Network Using Wavelets

Abstract

Image compression is the technique of reducing the size of image file without degrading the quality of the image Bandwidth conservation is an important issue in case of multimedia communication. Uncompressed multimedia (graphical, audio and video) data requires considerable storage capacity and transmission bandwidth. It demands for data storage capacity and data-transmission bandwidth continuously to outstrip the capabilities of available technologies. So to solve this problem an efficient multimedia communication scheme is proposed which is based on Wavelet. This paper shows the idea of image compression based on hierarchical back propagation neural network and results are analyzed The further analysis is conducted in the network model and tested training algorithm. This concludes that a high compression ratio is achieved with Bi-orthogonal Wavelet functions. The results are obtained with a Bi-orthogonal 6.8 Reconstruction Wavelet function and proved the best. Then Neural Network is implemented to prove the best result and hence achieved. Experimental results suggest that the proposed system can be efficiently used to compress while maintaining high image compression.