基于提升方案的高动态范围图像无损编码,利用离散时间细胞神经网络的非线性插值效应

H. Aomori, K. Kawakami, T. Otake, N. Takahashi, M. Yamauchi, M. Tanaka
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引用次数: 0

摘要

提升方案是一种构造线性和非线性小波变换的灵活方法。本文提出了一种新的基于提升方案的高动态范围(HDR)图像无损编码方法,该方法采用离散时间细胞神经网络(dt - cnn)。在我们提出的方法中,利用DT-CNN的非线性插值动力学对图像进行插值。由于DT-CNN的输出函数是一个多级量化函数,因此我们的方法适合于HDR图像的预测,并构成整数提升方案进行无损编码。此外,与仅使用b模板的传统CNN图像编码方法相比,我们的方法利用了a模板的非线性插值动力学。实验结果表明,与传统的线性滤波提升方法相比,该方法具有更好的编码性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lossless high dynamic range image coding based on lifting scheme using nonlinear interpolative effect of discrete-time cellular neural networks
The lifting scheme is a flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, we propose a novel lossless high dynamic range (HDR) image coding method based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs). In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN. Because the output function of DT-CNN works as a multi-level quantization function, our method adapts for the prediction of HDR image, and composes the integer lifting scheme for lossless 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 method using linear filters.
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