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Lossless compression of time‐series data based on increasing average of neighboring signals
Author(s) -
Takezawa Tetsuya,
Asakura Koichi,
Watanabe Toyohide
Publication year - 2010
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10093
Subject(s) - lossless compression , golomb coding , signal (programming language) , pulse code modulation , computer science , algorithm , amplitude , data compression , differential coding , pulse compression , series (stratigraphy) , signal transfer function , code (set theory) , mathematics , analog signal , decoding methods , transmission (telecommunications) , telecommunications , physics , artificial intelligence , image compression , optics , radar , paleontology , set (abstract data type) , image (mathematics) , biology , programming language , image processing
Golomb‐Rice encoding is a well‐known compression algorithm for sensor data. When time‐series data change drastically with large amplitudes, as found in a pulse signal, the code length based on Golomb‐Rice coding becomes long. In order to shorten the code length, the amplitude of the signal is decreased by calculating the differential signal between a raw signal and a similar signal. In this paper, we develop a lossless compression method for time‐series data such as sensor data. In traditional methods, finding the past signal from which a differential signal with low amplitude can be generated is the main topic. However, if there are no past signals that can be used to sufficiently reduce the amplitude of the differential signal, the data compression procedure has little effect. In our approach, a signal that decreases the energy of a pulse signal or increases the energy of the neighboring signal of a pulse signal is used to generate differential signals. In order to select an effective signal, we propose a method for detecting reference signals based on the cumulative distribution features of the time‐series data. Experiments confirm that the proposed method can generate codes whose length is shortened. The code length was decreased to 97% on average and to as little as 81% in comparison with the traditional method. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 47–56, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecj.10093