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Updated: Jul 12, 2025

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快速压缩细分卷用于科学可视化

Max Piochowiak, Carsten Dachsbacher

IEEE transactions on visualization and computer graphics

|October 23, 2023

概括

一种新的无损压缩技术有效地减少了基于voxel的细分体积的大小. 这种方法可以更快地处理数据,并改善大型科学数据集的交互可视化.

科学领域:

医疗成像医学成像
计算机图形计算机图形
数据压缩数据压缩

背景情况:

基于voxel的细分体积会产生大量的数据集,阻碍存储,传输和交互式可视化.
有效管理这些大型数据集对于科学研究和应用至关重要.

研究的目的:

为基于voxel的分段体积引入一种新的无损压缩技术.
为了应对数据大小在存储,传输和交互式可视化方面的挑战.

主要方法:

该技术将细分体积处理为小块,使用代改进方案编码标签和边界.
通过使用rANS-编码与全球频率表进行进一步压缩,以实现高效的并行解压.
该方法旨在适用于基于GPU的染.

主要成果:

实现高压缩比,将数据大小缩小到原始数据的1%~3%.
显示了压缩和解压缩 (高达每秒千兆字节) 的高吞吐量.
启用基于GPU的体积可视化,随时进行解压缩和细节级别染.

结论:

拟议的无损压缩技术显著减少了基于voxel的细分体积的数据大小.
它提高了存储,传输和交互式可视化的性能,特别是在GPU加速的环境中.
该方法支持高级可视化功能,如LOD染和缓存,以提高效率.

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