Image coding and decoding by discrete‐time cellular neural networks

When the original signal to be encoded has a strong correlation in the neighborhood, as in the case of the natural image, it can be expected that the original signal is represented by a smaller number of bits. This paper proposes the following low-bit coding system. the analog image is converted into the digital image by A-D conversion in the transmitter based on the dynamics of the locally interconnected discrete-time cellular neural network (DTCNN). the digital image is passed through a lowpass filter in the receiver so that the original analog image is restored. In this method, the coding of the static image is reduced to the optimization problem where the error between the original image and the decoded image is minimized. the distortion function is defined so that the noise generated by encoding is suppressed from the viewpoint of the whole image. A high-quality image is reconstructed by the dynamics of the multivalued neuron, which is an extension of the output of the neuron to multiple values. In this method, a parallel processing dynamics is employed, where a complex function can be realized from simple devices, and it is expected to realize the digital conversion in real-time of the analog information which is input in parallel, as in the case of the light input to the retina.