This paper describes the nse of analog Cellular Neural Networks (CNNs) for information coding and decoding - especially for the case of moving images. The dynamics of the coding (C-) and decoding (D-) CNNs are described by generalised CNN state eqnations. The C-CNN encodes the image by structural compression and halftoning. The D-CNN decodes the received data through a reconstruction process so as to almost recognise the original input to the C-CNN. The importance of our compression and quantisation technique lies in the ability to make the computations with only local connections. A dynamic quantitation is performed in the C-CNN to decide the binary value of each pixel from the neighboring values. In order to reduce the error between the original gray image and reconstructed halftone image, the template synthesis problem is addressed from the viewpoint of energy minimisation. The structural compression template synthesis problem is discussed from the viewpoints of topological and regnlarization theories. The structurally compressed image is regenerated in the D-CNN by a dynamic current distribution. The communication system in which the C- and D-CNNs are embedded consists of a differential transmitter with an internal receiver model in the feedback loop. Examples of the performance of the complete system are given.