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High-throughput variable-to-fixed entropy codec using selective, stochastic code forests

Torres, M. M.; Hernandez-Cabronero, M.; Blanes, I.; Serra-Sagrista, J.

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 et al.'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. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000120391
Veröffentlicht am 30.06.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2169-3536
KITopen-ID: 1000120391
Erschienen in IEEE access
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 8
Seiten 81283-81297
Nachgewiesen in Dimensions
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