高效四维运动补偿无损压缩动态体医学图像数据

V. Sanchez, P. Nasiopoulos, R. Abugharbieh
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引用次数: 25

摘要

动态体积(四维- 4D)医学图像通常具有巨大的文件大小,并且需要大量的资源用于存储和传输。在本文中,我们提出了一种高效的四维医学图像无损压缩方法,该方法基于多帧运动补偿过程,采用四维搜索、可变块大小和双向预测。通过递归地在空间和时间维度上应用多帧运动补偿来减少数据冗余。该方法还使用了一种新的差分编码算法来减少运动向量的冗余,并使用了一种新的基于上下文的自适应二进制算法编码器(CABAC)来压缩残差数据。对不同模态的真实医学图像进行性能评估,得到高达16:1的无损压缩比。
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Efficient 4D motion compensated lossless compression of dynamic volumetric medical image data
Dynamic volumetric (four dimensional- 4D) medical images are typically huge in file size and require a vast amount of resources for storage and transmission purposes. In this paper, we propose an efficient lossless compression method for 4D medical images that is based on a multi-frame motion compensation process employing a 4D search, variable block- sizes and bi-directional prediction. Data redundancies are reduced by recursively applying multi-frame motion compensation in the spatial and temporal dimensions. The proposed method also uses a novel differential coding algorithm to reduce redundancies in motion vectors and a new context-based adaptive binary arithmetic coder (CABAC) for compression of the residual data. Performance evaluations on real medical images of varying modality resulted in lossless compression ratios of up to 16:1.
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