Next Article in Journal
Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
Previous Article in Journal
Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling
Previous Article in Special Issue
Practical Grammar Compression Based on Maximal Repeats

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

Stream-Based Lossless Data Compression Applying Adaptive Entropy Coding for Hardware-Based Implementation

1
Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
2
Department of Computer Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(7), 159; https://doi.org/10.3390/a13070159
Received: 18 May 2020 / Revised: 26 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Lossless Data Compression)
Toward strong demand for very high-speed I/O for processors, physical performance growth of hardware I/O speed was drastically increased in this decade. However, the recent Big Data applications still demand the larger I/O bandwidth and the lower latency for the speed. Because the current I/O performance does not improve so drastically, it is the time to consider another way to increase it. To overcome this challenge, we focus on lossless data compression technology to decrease the amount of data itself in the data communication path. The recent Big Data applications treat data stream that flows continuously and never allow stalling processing due to the high speed. Therefore, an elegant hardware-based data compression technology is demanded. This paper proposes a novel lossless data compression, called ASE coding. It encodes streaming data by applying the entropy coding approach. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper describes the detailed mechanism of ASE coding. Furthermore, the paper demonstrates performance evaluations to promise that ASE coding adaptively shrinks streaming data and also works on a small amount of hardware resources without stalling or buffering any part of data stream.
Keywords: lossless data compression; data stream; entropy; adaptive; hardware lossless data compression; data stream; entropy; adaptive; hardware
MDPI and ACS Style

Yamagiwa, S.; Hayakawa, E.; Marumo, K. Stream-Based Lossless Data Compression Applying Adaptive Entropy Coding for Hardware-Based Implementation. Algorithms 2020, 13, 159.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop