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Separable 2D Lifting Using Discrete-Time Cellular Neural Networks for Lossless Image Coding

机译:使用离散时间元胞神经网络进行无损图像编码的可分离 2D 提升

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摘要

The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively 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 using linear filters.
机译:提升方案是一种高效、灵活的线性和非线性小波变换构造方法。该文提出了一种基于离散时间元胞神经网络(DT-CNNs)提升方案的无损图像编码技术。在所提出的方法中,利用DT-CNN的非线性插值动力学对图像进行插值,并且由于DT-CNN的输出函数作为多级量化函数工作,因此我们的方法组成了用于无损图像编码的整数提升方案。此外,与仅使用B模板的传统CNN图像编码方法相比,有效地利用了A模板的非线性插值动力学。实验结果表明,与传统的线性滤波器提升方法相比,具有更好的喷码性能。

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