2003
DOI: 10.1111/1467-8659.00681
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Out‐of‐core compression and decompression of large n‐dimensional scalar fields

Abstract: We present a simple method for compressing very large and regularly sampled scalar fields. Our method is particularlyattractive when the entire data set does not fit in memory and when the sampling rate is high relative to thefeature size of the scalar field in all dimensions. Although we report results for and data sets, the proposedapproach may be applied to higher dimensions. The method is based on the new Lorenzo predictor, introducedhere, which estimates the value of the scalar field at each sample from t… Show more

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Cited by 110 publications
(78 citation statements)
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“…To make the best possible use of available neighbors, we employ spectral prediction [16] by precomputing "optimal" rational weights for all 2 7 = 128 possible configurations (Figure 3). Our derivation shows parallelogram [4] and Lorenzo prediction [6,17] to be special cases (0x70 respectively 0x7f in Figure 3) of spectral prediction.…”
Section: Geometry Compressionmentioning
confidence: 82%
“…To make the best possible use of available neighbors, we employ spectral prediction [16] by precomputing "optimal" rational weights for all 2 7 = 128 possible configurations (Figure 3). Our derivation shows parallelogram [4] and Lorenzo prediction [6,17] to be special cases (0x70 respectively 0x7f in Figure 3) of spectral prediction.…”
Section: Geometry Compressionmentioning
confidence: 82%
“…Both compressors rely on polynomial interpolation for prediction, with fixed integer polynomial coefficients. APAX uses univariate Lagrange polynomials of degree 0 and 1, allowing linear polynomials (or any function with fpzip exploits correlations in more than one dimension using the Lorenzo predictor [17], which over a 3D domain reproduces trivariate quadratic polynomials (or any function for which…”
Section: The Fpzip and Apax Compressorsmentioning
confidence: 99%
“…al. presented an arithmetic-coding-based method for compressing 3D geometric data [5]- [7]. In their method, we first predict coordinates of a point in a 3D mesh model, each of which is expressed in FP numbers, from coordinates of the adjacent points by some techniques, typically by parallelogram predictors.…”
Section: Preliminariesmentioning
confidence: 99%
“…al. presented an arithmetic-coding-based method for compressing 3D geometric data [5]- [7]. Harada et.…”
Section: Introductionmentioning
confidence: 99%