利用Lanczos滤波器优化分解基础,对生物医学图像进行无损压缩

Jonathan Taquet, C. Labit
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引用次数: 10

摘要

本文提出将Lanczos插值滤波器作为小波原子引入生物医学图像嵌入有损压缩到无损压缩的优化分解中。为了考虑生物医学成像模态的多种内容,在通用包结构中对分解和Lanczos参数进行了联合优化。在大型数据库上给出了无损实验结果。他们表明,与使用5/3双正交小波和二进分解的已知基础相比,所提出的方法可以在低噪声图像上提高10%以上的压缩率,在3D-MRI上提高30%,同时在噪声数据集上提供类似的结果。
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Optimized decomposition basis using Lanczos filters for lossless compression of biomedical images
This paper proposes to introduce Lanczos interpolation filters as wavelet atoms in an optimized decomposition for embedded lossy to lossless compression of biomedical images. The decomposition and the Lanczos parameter are jointly optimized in a generic packet structure in order to take into account the various contents of biomedical imaging modalities. Lossless experimental results are given on a large scale database. They show that in comparison with a well known basis using 5/3 biorthogonal wavelets and a dyadic decomposition, the proposed approach allows to improve the compression by more than 10% on less noisy images and up to 30% on 3D-MRI while providing similar results on noisy datasets.
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