Data compression for multidimensional time series data
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Publication Number
US-11790559-B2
Publication Date
2023-10-17
Expiration Date
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.
Core Innovation
The invention relates to computer-implemented compression/decompression of sparse multidimensional ordered series data from a compressed data file or stream. It uses local regions to decompose the data into regions and operates on a target local region to be restored during decompression. The decoded adjusted local region data for the target local region is combined with predicted local region peaks to generate restored local region data for output.
For a target local region, an optimum scale factor is decoded by comparing the decoded adjusted local region data to one or more prior local regions decoded from the compressed data file or stream. Based on the comparison, predicted local region peaks are generated and scaled by the decoded optimum scale factor. The scaled predicted local region peaks are then added to the decoded adjusted local region data to provide the restored local region data for the target local region.
Correlation is used to condition the addition of predicted local region peaks, including a correlation threshold that enables or disables prediction so that restored local region data is conditionally generated. This conditional adding supports high-fidelity restoration while avoiding unstable behavior, and it can avoid adding extra per-region signaling bits. During decompression, restored local regions can be produced for partial restoration, enabling on-the-fly/streaming and direct/partial access by restoring only selected local regions, while full restoration can be performed iteratively for the compressed data file.
Claims Coverage
The independent claims are clm-00001, clm-00010, and clm-00012. Across these independent claims, the inventive features focus on decoding adjusted local region data for a target local region, decoding an optimum scale factor by comparing to prior local regions, generating predicted local region peaks, scaling the predicted peaks, adding them to the decoded adjusted local region data, and outputting restored local region data.
Decoding an adjusted local region data and restoring a target local region
decoding an adjusted local region data from the compressed data file or stream, wherein the decoded adjusted local region data corresponds to a target local region to be restored; adding the predicted local region peaks and the decoded adjusted local region data together to provide a restored local region data; and outputting the restored local region data
Decoding an optimum scale factor from prior decoded local regions to generate scaled predicted local region peaks
decoding an optimum scale factor from the compressed data file or stream by comparing the decoded adjusted local region data to one or more prior local regions decoded from the compressed data file or stream, and generating predicted local region peaks that are scaled by the decoded optimum scale factor
Offsetting prior local region data to identify a subset of peaks and scaling the subset
decoding an optimum scale factor from the compressed data file or stream by comparing the decoded adjusted local region data to one or more prior local region data that are offset from the decoded adjusted local region data to identify a subset of peaks from the decoded adjusted local region data and scaling the subset of peaks by the decoded optimum scale factor to generate a predicted local region peaks
System configured to perform decompression using a decoded adjusted local region and scaled predicted local region peaks
a system comprising a non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor, cause the processor to receive the compressed data file or stream; decode an adjusted local region data; decode an optimum scale factor by comparing the decoded adjusted local region data to one or more prior local regions; generate predicted local region peaks scaled by the decoded optimum scale factor; add the predicted local region peaks and the decoded adjusted local region data together to provide a restored local region data; and output the restored local region data
The independent claims cover decompression of sparse multidimensional ordered series data by decoding adjusted local region data for a target local region, decoding an optimum scale factor by comparing to prior decoded local regions, generating predicted local region peaks scaled by the optimum scale factor, adding the predicted peaks to the decoded adjusted local region data, and outputting the restored local region data.
Stated Advantages
Claims indicate very high compression ratios while providing high-fidelity restoration.
Claims indicate high fidelity restoration metrics including indistinguishable results at 40× magnification.
Claims indicate PSNR of about 56 dB and low PMSE.
Conditional adding using a correlation threshold enables prediction to be enabled or disabled to avoid unstable behavior and to avoid adding extra per-region signaling bits.
Enables on-the-fly/streaming and direct/partial access by restoring only selected local regions.
Documented Applications
Sparse multidimensional ordered series data decompression for spectrographic data, including mass spectrometry with m/z and ion fragment intensity/count, retention time index (n), and ion mobility (z_n).
Microscopy, including bright-field microscopy, Z-stacks, and multiplexed fluorescence microscopy with fluorescence dye layers (k).
Partial or selective restoration enabling direct/partial access to compressed stream data by restoring selected local regions.
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