TTHRESH: Tensor Compression for Multidimensional Visual Data
TTHRESH: Tensor Compression for Multidimensional Visual Data
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DOI：
10.1109/tvcg.2019.2904063
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发表时间：
2020-09-01
影响因子：
5.2
通讯作者：
Pajarola, Renato
中科院分区：
文献类型：
--
作者：
Ballester-Ripoll, Rafael;Lindstrom, Peter;Pajarola, Renato
在可视化应用中处理高分辨率多维数据集时，内存和网络带宽是决定性的瓶颈，它们越来越需要合适的数据压缩策略。提出了一种新的规则网格上多维数据的有损压缩算法。它利用高阶奇异值分解（HOSVD），SVD的推广到三维和更高，连同位平面，游程长度和算术编码压缩HOSVD变换系数。我们的方案降低了数据特别顺利，并实现较低的均方误差比其他国家的最先进的算法在低到中等的比特率，因为它需要在数据归档和管理的可视化目的。所提出的算法的其他优点包括非常精细的比特率选择粒度和在压缩域中以非常小的成本操纵数据的能力，例如以重构数据集的所有（或所选部分）的滤波和/或子采样版本。
Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. We introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.