Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps
In this work, we study the compression of multichannel signals with irregular sampling rates and data gaps. We consider state-of-the-art algorithms, which were originally designed to compress gapless signals with regular sampling, adapt them to operate with signals with irregular sampling rates and...
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IEEE
2024-01-01
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| author | Pablo Cervenansky Alvaro Martin Gadiel Seroussi |
| author_facet | Pablo Cervenansky Alvaro Martin Gadiel Seroussi |
| author_sort | Pablo Cervenansky |
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| description | In this work, we study the compression of multichannel signals with irregular sampling rates and data gaps. We consider state-of-the-art algorithms, which were originally designed to compress gapless signals with regular sampling, adapt them to operate with signals with irregular sampling rates and data gaps, and then evaluate their performance experimentally, through the compression of signals obtained from real-world datasets. Both the original algorithms and our schemes compress signals by exploiting their temporal, and, in some cases, spatial correlation. They work in a near-lossless fashion, guaranteeing a bounded absolute error between each decompressed sample and its original value. This includes the important lossless compression case, which corresponds to an error bound of zero. Our schemes first encode the position of the gaps, using arithmetic coding combined with a Krichevsky-Trofimov probability assignment on a Markov model, and then encode the data values separately. Our experimental analysis consists of comparing the compression performance of our schemes with each other, and with representative special-purpose and general-purpose lossless compression algorithms. We also measure and compare the schemes’ running times, to assess their practicality. From the results we extract some general conclusions: in the lossless case, TS2Diff and LZMA attain the best compression performance, whereas our adaptation of algorithm APCA is preferred for positive error bounds. At the same time, our adaptation of APCA, and TS2Diff, attain some of the best running times. |
| format | Article |
| id | doaj-art-bddb17cab8b74036a48835f37b576dbc |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-bddb17cab8b74036a48835f37b576dbc2025-08-20T02:35:16ZengIEEEIEEE Access2169-35362024-01-011219319519321110.1109/ACCESS.2024.351849510804126Compression of Multichannel Signals With Irregular Sampling Rates and Data GapsPablo Cervenansky0https://orcid.org/0009-0001-3696-6750Alvaro Martin1https://orcid.org/0000-0001-8601-1242Gadiel Seroussi2https://orcid.org/0000-0002-2893-189XPEDECIBA Informática, Montevideo, UruguayPEDECIBA Informática, Montevideo, UruguayInstituto de Computación, Facultad de Ingeniería, Universidad de la República, Montevideo, UruguayIn this work, we study the compression of multichannel signals with irregular sampling rates and data gaps. We consider state-of-the-art algorithms, which were originally designed to compress gapless signals with regular sampling, adapt them to operate with signals with irregular sampling rates and data gaps, and then evaluate their performance experimentally, through the compression of signals obtained from real-world datasets. Both the original algorithms and our schemes compress signals by exploiting their temporal, and, in some cases, spatial correlation. They work in a near-lossless fashion, guaranteeing a bounded absolute error between each decompressed sample and its original value. This includes the important lossless compression case, which corresponds to an error bound of zero. Our schemes first encode the position of the gaps, using arithmetic coding combined with a Krichevsky-Trofimov probability assignment on a Markov model, and then encode the data values separately. Our experimental analysis consists of comparing the compression performance of our schemes with each other, and with representative special-purpose and general-purpose lossless compression algorithms. We also measure and compare the schemes’ running times, to assess their practicality. From the results we extract some general conclusions: in the lossless case, TS2Diff and LZMA attain the best compression performance, whereas our adaptation of algorithm APCA is preferred for positive error bounds. At the same time, our adaptation of APCA, and TS2Diff, attain some of the best running times.https://ieeexplore.ieee.org/document/10804126/Multichannel signal compressiontime series compressionnear-lossless compressionirregular sampling ratedata gaps |
| spellingShingle | Pablo Cervenansky Alvaro Martin Gadiel Seroussi Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps IEEE Access Multichannel signal compression time series compression near-lossless compression irregular sampling rate data gaps |
| title | Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps |
| title_full | Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps |
| title_fullStr | Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps |
| title_full_unstemmed | Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps |
| title_short | Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps |
| title_sort | compression of multichannel signals with irregular sampling rates and data gaps |
| topic | Multichannel signal compression time series compression near-lossless compression irregular sampling rate data gaps |
| url | https://ieeexplore.ieee.org/document/10804126/ |
| work_keys_str_mv | AT pablocervenansky compressionofmultichannelsignalswithirregularsamplingratesanddatagaps AT alvaromartin compressionofmultichannelsignalswithirregularsamplingratesanddatagaps AT gadielseroussi compressionofmultichannelsignalswithirregularsamplingratesanddatagaps |