Comparison of Pyramidal and Packet Wavelet Coder for Image Compression Using Cellular Neural Network (CNN) with Thresholding and Quantization

We present the packet wavelet coder implemented with cellular neural network architecture, and show its superiority over the pyramidal wavelet representation. This paper also demonstrates how the cellular neural universal machine (CNNUM) architecture can be extended to image compression. The packet wavelet coder performs the operation of image compression, aided by CNN architecture. It uses the highly parallel nature of the CNN structure and its speed outperforms traditional digital computers. In packet wavelet coder, an image signal can be analyzed by passing it through an analysis filter banks followed by a decimation process, according to the rules of packet wavelets. The Simulation results indicate that the quality of the reconstructed image is superior by using packet wavelet coding scheme. Finally, a quantization operation is performed in order to translate the coefficient values to discrete environment. Our results are compared with that of pyramidal wavelet representation