Lossless image coding by cellular neural networks with minimum coding rate learning

In this paper, a novel lossless image coding scheme using the cellular neural network (CNN) is proposed. From the viewpoint of the optimal lossless coding, our method is optimized for not only mean squared error (MSE) but also a coding rate. The key idea of this work is that the local structure of an image is modeled by six types of CNN templates in order to achieve high prediction performance, and the CNN parameters that gives prediction characteristic are decided by the supervised minimum coding rate learning. Moreover, in the entropy coding layer, the prediction residuals are coded by an adaptive arithmetic encoder with context modeling for high coding efficiency. The effectiveness of proposed algorithm is confirmed by some computer simulations of various standard test images, and its performance is compared with that of conventional hierarchical coding schemes having scalability.