Multi-Channel Similarity Based Compression
Sergey Voronin
Computer and Information Science, 2020, vol. 13, issue 1, 80
Abstract:
Many situations arise where data is collected continuously across multiple channels or over multiple similar subjects. In many cases, transmission of the data across all channels is necessary, but the process can be made more efficient by making use of present similarity between data across different channels. We present here a combined compression approach which exploits approximate linear dependence and high correlation coefficient values between pairs of transformed and sorted channel data vectors. By exploiting this similarity, substantial compression gains can be achieved compared to compression of data per each individual channel.
Date: 2020
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.ccsenet.org/journal/index.php/cis/article/download/0/0/41859/43495 (application/pdf)
http://www.ccsenet.org/journal/index.php/cis/article/view/0/41859 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ibn:cisjnl:v:13:y:2020:i:1:p:80
Access Statistics for this article
More articles in Computer and Information Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().