Lossless image compression and reconstruction by cellular neural networks
Mamoru Tanaka, Yuichi Tanji, Masaru Onishi, Toshiya Nakaguchi · 2002
It is clear that the characteristic of cellular neural networks (CNN) is the use of A-templates by which many kind of dynamical interpolative nonlinear effects can be generated without dependency of image scanning. This paper describes nonlinear quantization methods in a discrete-time cellular neural network (DT-CNN) which generates a high quality lossy or lossless reconstructed image. It is very important that the DT-CNN state variable image which is determined dynamically based on the minimization of the DT-CNN Lyapunov energy function to generate an optimized interpolative prediction function is a lossless interpolative DPCM image between the original input and the interpolation prediction functions. The small compression ratio for the reconstructed lossless image can be changed by the multivalue quantization and the A-template. By the DT-CNN nondependency of image scanning, the lossless image points can be extracted even in an lossy image by checking the existence of local errors.