TTHRESH: Tensor Compression for Multidimensional Visual Data. Ballester-Ripoll, R., Lindstrom, P., & Pajarola, R. IEEE Transactions on Visualization and Computer Graphics, 4, 2019. ![TTHRESH: Tensor Compression for Multidimensional Visual Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 8 downloads 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 N-dimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to 3 and more dimensions, together with adaptive quantization, run-length and arithmetic coding to store the HOSVD transform coefficients' relative positions as sorted by their absolute magnitude. Our scheme degrades the data particularly smoothly and outperforms other state-of-the-art volume compressors at low-to-medium bit rates, as required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include extremely fine bit rate selection granularity, bounded resulting l^2 error, and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct subsampled or filtered-resampled versions of all (or selected parts) of the data set.
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abstract = {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 N-dimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to 3 and more dimensions, together with adaptive quantization, run-length and arithmetic coding to store the HOSVD transform coefficients' relative positions as sorted by their absolute magnitude. Our scheme degrades the data particularly smoothly and outperforms other state-of-the-art volume compressors at low-to-medium bit rates, as required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include extremely fine bit rate selection granularity, bounded resulting l^2 error, and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct subsampled or filtered-resampled versions of all (or selected parts) of the data set.},
bibtype = {article},
author = {Ballester-Ripoll, Rafael and Lindstrom, Peter and Pajarola, Renato},
doi = {10.1109/TVCG.2019.2904063},
journal = {IEEE Transactions on Visualization and Computer Graphics}
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