Open AccessTwo-dimensional data-block compression of information-measuring system algorithms
Author(s) -
A. V. Levenets,
D. I. Nefed’ev,
Руслан Иванович Баженов,
V. B. Masyagin,
Saida Beknazarova
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1546/1/012089
Subject(s) - data compression , algorithm , compression (physics) , block (permutation group theory) , compression ratio , computer science , frame (networking) , data compression ratio , data structure , lossless compression , image compression , mathematics , artificial intelligence , engineering , telecommunications , materials science , geometry , image (mathematics) , image processing , automotive engineering , composite material , programming language , internal combustion engine
The authors suggest a new approach to develop measurement data compression algorithms. According to that a data frame is considered as a bit sequence formed into a two-dimensional well-ordered linear structure. It is supposed that there is no explicit bind to the source data / specifications width within the collected structure. The researchers propose block compression algorithms. They take into account not only the relationship between one sensor readings in adjacent samples, but also the connection of the samples in one frame so that it is expected to contribute to higher compression efficiency. The studies of the proposed compression algorithms have shown that they can be effectively used for compressing data frames of information-measuring systems. The mid-compression ratio of the proposed block compression algorithms falls in the range 8.0 to 9.5, and a simple adaptive algorithm observed in the paper provides the maximum value of the average compression ratio.