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Time-Universal Data Compression

by 1,2
Institute of Computational Technologies of the Siberian Branch of the Russian Academy of Science, 630090 Novosibirsk, Russia
Department of Information Technologies, Novosibirsk State University, 630090 Novosibirsk, Russia
The preliminary version of this paper is accepted for ISIT 2019, Paris.
Algorithms 2019, 12(6), 116;
Received: 26 April 2019 / Revised: 25 May 2019 / Accepted: 27 May 2019 / Published: 29 May 2019
(This article belongs to the Special Issue Data Compression Algorithms and their Applications)
Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We suggest a method of data compression whose performance is close to optimal, but for which the extra time needed is relatively small: the ratio of this extra time and the total time of calculation can be limited, in an asymptotic manner, by an arbitrary positive constant. In short, the main idea of the suggested approach is as follows: in order to find the best, try all the data compressors, but, when doing so, use for compression only a small part of the file. Then apply the best data compressors to the whole file. Note that there are many situations where it may be necessary to find the best data compressor out of a given set. In such a case, it is often done by comparing compressors empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it. View Full-Text
Keywords: data compression; universal coding; time-series forecasting data compression; universal coding; time-series forecasting
MDPI and ACS Style

Ryabko, B. Time-Universal Data Compression. Algorithms 2019, 12, 116.

AMA Style

Ryabko B. Time-Universal Data Compression. Algorithms. 2019; 12(6):116.

Chicago/Turabian Style

Ryabko, Boris. 2019. "Time-Universal Data Compression" Algorithms 12, no. 6: 116.

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