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Invertible update-then-predict integer lifting wavelet for lossless image compression

机译:可逆更新然后预测整数提升小波以实现无损图像压缩

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This paper presents a new wavelet family for lossless image compression by re-factoring the channel representation of the update-then-predict lifting wavelet, introduced by Claypoole, Davis, Sweldens and Baraniuk, into lifting steps. We name the new wavelet family as invertible update-then-predict integer lifting wavelets (IUPILWs for short). To build IUPILWs, we investigate some central issues such as normalization, invertibility, integer structure, and scaling lifting. The channel representation of the previous update-then-predict lifting wavelet with normalization is given and the invertibility is discussed firstly. To guarantee the invertibility, we re-factor the channel representation into lifting steps. Then the integer structure and scaling lifting of the invertible update-then-predict wavelet are given and the IUPILWs are built. Experiments show that comparing with the integer lifting structure of 5/3 wavelet, 9/7 wavelet, and iDTT, IUPILW results in the lower bit-rates for lossless image compression.
机译:本文通过将由Claypoole,Davis,Sweldens和Baraniuk引入的更新-预测-预测提升小波的通道表示重构为提升步骤,提出了一种用于无损图像压缩的新小波族。我们将新的小波族命名为可逆更新,然后预测整数提升小波(简称IUPILW)。为了构建IUPILW,我们研究了一些核心问题,例如规范化,可逆性,整数结构和缩放比例提升。给出了归一化的先更新后预测提升小波的信道表示,并讨论了可逆性。为了保证可逆性,我们将通道表示重构为提升步骤。然后给出了可逆更新-预测小波的整数结构和缩放提升,并建立了IUPILW。实验表明,与5/3小波,9/7小波和iDTT的整数提升结构相比,IUPILW的比特率更低,可实现无损图像压缩。

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