Data compression for multidimensional time series data
AssigneeProtein Metrics Inc.
InventorDoron Kletter
Life of this patent
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Abstract
Described herein are computer-implemented methods for compressing sparse multidimensional ordered series data. In particular, these methods and apparatuses for performing them (including software) may be particularly well suited to efficiently compressing spectrographic data.
Claims20
A computer-implemented method for compressing sparse multidimensional ordered series data, the method comprising:
- dividing a multidimensional ordered series data into a plurality of local regions, wherein the data in each local region comprise one or more indexed data sets, each indexed data set comprising an index (n) within a given local region of the sparse multidimensional ordered series data and one or more variables that are indexed by the index (n);
- identifying a predictor that calculates each of the one or more variables as a function of the index (n), a current local region data, and at least one previous local region data;
- adjusting the current local region data by subtracting a scaled predicted correlated data based on the at least one previous local region data when a level of correlation exceeding a threshold exists between the current local region data and the previous local region data;
- and
- encoding the adjusted current local region data, including an optimum scale factor, into a compressed stream.
A computer-implemented method for compressing sparse multidimensional ordered series data, the method comprising:
- dividing a multidimensional ordered series data comprising histopathological imaging data into a plurality of local regions, wherein the data in each local region comprise one or more indexed data sets, each indexed data set comprising a spatial index (n) within a given local region of the histopathological imaging data and one or more variables that are indexed by the spatial index (n);
- and
- processing the plurality of local regions in an order by:
- identifying a predictor that calculates each of the one or more variables as a function of the spatial index (n), a current local region data, and at least one previous local region data;
- adjusting the current local region data by subtracting a scaled predicted correlated data based on the at least one previous local region data when a level of correlation exceeding a threshold exists between the current local region data and the previous local region data;
- and
- encoding the adjusted current local region data, including an optimum scale factor, into a compressed stream.
A system for compressing sparse multidimensional ordered series data, the system comprising a non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor, cause the processor to:
- divide a multidimensional ordered series data into a plurality of local regions, wherein the data in each local region comprise one or more indexed data sets, each indexed data set comprising an index (n) within a given local region of the sparse multidimensional ordered series data and one or more variables that are indexed by the index (n);
- identify a predictor that calculates each of the one or more variables as a function of the index (n), a current local region data, and at least one previous local region data;
- adjust the current local region data by subtracting a scaled predicted correlated data based on the at least one previous local region data when a level of correlation exceeding a threshold exists between the current local region data and the previous local region data;
- and
- encode the adjusted current local region data, including an optimum scale factor, into a compressed stream.
Dependencies are inferred from the claim text.