Publication: Tensor Decompositions for Integral Histogram Compression and Look-Up
Tensor Decompositions for Integral Histogram Compression and Look-Up
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Ballester-Ripoll, R., & Pajarola, R. (2019). Tensor Decompositions for Integral Histogram Compression and Look-Up. IEEE Transactions on Visualization and Computer Graphics, 25(2), 1435–1446. https://doi.org/10.1109/TVCG.2018.2802521
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Histograms are a fundamental tool for multidimensional data analysis and processing, and many applications in graphics and visualization rely on computing histograms over large regions of interest (ROI). Integral histograms (IH) greatly accelerate the calculation in the case of rectangular regions, but come at a large extra storage cost. Based on the tensor train decomposition model, we propose a new compression and approximate retrieval algorithm to reduce the overall IH memory usage by several orders of magnitude at a user-defined a
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- Ballester-Ripoll, Rafael
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Ballester-Ripoll, R., & Pajarola, R. (2019). Tensor Decompositions for Integral Histogram Compression and Look-Up. IEEE Transactions on Visualization and Computer Graphics, 25(2), 1435–1446. https://doi.org/10.1109/TVCG.2018.2802521
