High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests
Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to m...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9081983/ |
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| author | Manuel Martinez Torres Miguel Hernandez-Cabronero Ian Blanes Joan Serra-Sagrista |
| author_facet | Manuel Martinez Torres Miguel Hernandez-Cabronero Ian Blanes Joan Serra-Sagrista |
| author_sort | Manuel Martinez Torres |
| collection | DOAJ |
| description | Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto <italic>et al.</italic>’s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds. |
| format | Article |
| id | doaj-art-9c96f9385cee4c64be5c82a39005a07d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9c96f9385cee4c64be5c82a39005a07d2025-01-16T00:01:03ZengIEEEIEEE Access2169-35362020-01-018812838129710.1109/ACCESS.2020.29913149081983High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code ForestsManuel Martinez Torres0https://orcid.org/0000-0001-6020-7618Miguel Hernandez-Cabronero1https://orcid.org/0000-0001-9301-4337Ian Blanes2https://orcid.org/0000-0001-8939-1666Joan Serra-Sagrista3https://orcid.org/0000-0003-4729-9292Karlsruhe Institute of Technology, Karlsruhe, GermanyInformation and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, SpainInformation and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, SpainKarlsruhe Institute of Technology, Karlsruhe, GermanyEfficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto <italic>et al.</italic>’s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds.https://ieeexplore.ieee.org/document/9081983/Data compressionhigh-throughput entropy codingvariable-to-fixed codes |
| spellingShingle | Manuel Martinez Torres Miguel Hernandez-Cabronero Ian Blanes Joan Serra-Sagrista High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests IEEE Access Data compression high-throughput entropy coding variable-to-fixed codes |
| title | High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests |
| title_full | High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests |
| title_fullStr | High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests |
| title_full_unstemmed | High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests |
| title_short | High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests |
| title_sort | high throughput variable to fixed entropy codec using selective stochastic code forests |
| topic | Data compression high-throughput entropy coding variable-to-fixed codes |
| url | https://ieeexplore.ieee.org/document/9081983/ |
| work_keys_str_mv | AT manuelmartineztorres highthroughputvariabletofixedentropycodecusingselectivestochasticcodeforests AT miguelhernandezcabronero highthroughputvariabletofixedentropycodecusingselectivestochasticcodeforests AT ianblanes highthroughputvariabletofixedentropycodecusingselectivestochasticcodeforests AT joanserrasagrista highthroughputvariabletofixedentropycodecusingselectivestochasticcodeforests |