Image compression using wavelet transform and self-development neural network

In this paper, we propose a novel method of encoding an image without blocky effects. The method incorporates the wavelet transform and a self-development neural network-the Vitality Conservation (VC) network to achieve significant improvement in image compression performance. The implementation consists of three steps. The image is first decomposed at different scales using wavelet transform to obtain an orthogonal wavelet representation of the image. Each band can be subsequently processed in parallel. In the second step, the discrete Karhunen-Loeve transform is used to extract the principal components of the wavelet coefficients. Thus, the processing speed can be much faster than otherwise. Finally, results of the second step are used as input to the VC network for vector quantization. Our simulation results show that such an implementation can, in much less time, achieve superior reconstructed images to other methods.