Abstract
Data compression algorithms typically rely on identifying repeated sequences of symbols from the original data to provide a compact representation of the same information, while maintaining the ability to recover the original data from the compressed sequence. Using data transformations prior to the compression process has the potential to enhance the compression capabilities, being lossless as long as the transformation is invertible. Floating point data presents unique challenges to generate invertible transformations with high compression potential. This paper identifies key conditions for basic operations of floating point data that guarantee lossless transformations. Then, we show four methods that make use of these observations to deliver lossless compression of real datasets, where we improve compression rates up to 40%.
This work was supported by the IoTalentum Project within the Framework of Marie Skłodowska-Curie Actions Innovative Training Networks (ITN)-European Training Networks (ETN), which is funded by the European Union Horizon 2020 Research and Innovation Program under Grant 953442.
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Taurone, F., Lucani, D.E., Fehér, M., Zhang, Q. (2023). Lossless Preprocessing of Floating Point Data to Enhance Compression. In: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-031-38318-2_45
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DOI: https://doi.org/10.1007/978-3-031-38318-2_45
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