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We present frequency and spatial adaptive packet wavelet coder implemented with cellular neural network architecture. 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. Traditional packet decomposition adapts to a global frequency distribution, this technique finds the best joint of spatial segmentation and local frequency basis. The simulation results indicate that the quality of the reconstructed image is superior by using frequency and spatial adaptive packet wavelet coding scheme.