Hybrid lifting scheme using discrete-time cellular neural networks for lossless image coding
Hisashi Aomori, Tsuyoshi Otake, Norikazu Takahashi, Mamoru Tanaka · 2006
The lifting scheme is a 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. The hybrid use of the linear filter and the nonlinear filter is an efficient method for obtaining an optimal filter pair. In this paper, we propose a novel hybrid lifting scheme using discrete-time cellular neural networks (DT-CNNs) for lossless image coding. In our method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNNs, and the update process of lifting is designed by using the linear 5-tap filter to avoid the aliasing. Since the output function of DT-CNNs works as a multilevel 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 those of the conventional lifting method using linear filters.