Lifting-Based Lossless Image Coding by Discrete-Time Cellular Neural Networks

The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In the nonlinear lifting scheme, it is difficult to design the optimal update filter corresponding to the nonlinear prediction filter. It is well-known that the combination use of linear filter and nonlinear filter is an efficient filter pair. In this paper, we propose a novel lifting-based lossless image coding method using discrete-time cellular neural networks (DT-CNNs). In our method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNNs, and the linear 5-tap filter is used for avoiding the aliasing. Since the output function of DT-CNNs works as a multi-level quantizing function, our method composes the integer lifting scheme for lossless image coding. Moreover, our method makes good use of the nonlinear interpolative dynamics by A-template compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods.