Sparse Spectral Data Compression Using Predictive Difference Coding
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Solution Overview
Problem
Conventional compression methods are ineffective in compressing large datasets of spectroscopic data, particularly those containing double precision floating point numbers, as they fail to recognize and exploit the sparse and predictable patterns inherent in such data, often resulting in increased data size or low compression ratios.
Innovation Solution
The method involves defining a predictor to model the behavior of the data, assigning amplitude and hop code words based on value and frequency, and generating a compressed output that includes a decoder legend and code word dictionaries, specifically tailored for sparse multidimensional ordered series data like mass spectrometry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression methods (GZIP, LZW, BZIP2) are applied to spectroscopic data, then the data can be compressed, but the compression ratio is low and data size is not significantly reduced
Solution Approach 1:
The patent transforms the data representation from standard floating-point format to a custom encoding scheme where values are represented as differences from predicted values. This parameter transformation enables significantly better compression by exploiting the predictable nature of spectroscopic data, achieving compression ratios orders of magnitude better than conventional methods.
Solution Approach 2:
The patent segments the spectroscopic data into discrete value levels and represents them using hierarchical coding. By dividing the continuous data range into discrete bins and encoding the bin indices rather than full precision values, the method achieves compact representation while maintaining measurement precision, resolving the contradiction between data size reduction and information preservation.
2Productivity
If lossy compression is applied to reduce data size, then compression ratio improves, but measurement precision and data accuracy deteriorate
Solution Approach 1:
The patent changes the parameter representation by storing differences from predicted values rather than absolute values. This transformation allows lossless compression where the original precision is fully preserved, eliminating the trade-off between compression ratio and measurement precision that plagues conventional lossy methods.
Solution Approach 2:
The patent creates a compressed representation that is mathematically equivalent to the original data through reversible transformations. The encoded data can be perfectly decoded back to the original values, providing a lossless copy that maintains full measurement precision while achieving high compression ratios.
3Measurement precision
If high precision floating point data is stored, then measurement accuracy is maintained, but data size increases
Solution Approach 1:
The patent transforms high-precision floating-point data into a difference encoding format where each value is represented as a small integer offset from a predicted value. This parameter transformation dramatically reduces the number of bits required to represent each data point while maintaining full precision through reversible decoding, directly resolving the contradiction between precision and data size.
Solution Approach 2:
The patent moves from representing data in the absolute value dimension to representing it in the difference-from-prediction dimension. This dimensional transformation exploits the fact that differences are typically much smaller and more compressible than absolute values, achieving compact storage while preserving full measurement precision through the reversible encoding scheme.
Data Source
AI summary
Disclosed are methods and systems for significantly compressing sparse multidimensional ordered series data comprised of indexed data sets, wherein each data set comprises an index, a first variable and a second variable. The methods and systems are particularly suited for compression of data recorded in double precision floating point format.