Multi-Channel Similarity Based Compression

  •  Sergey Voronin    


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.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2020): 0.439

Impact Factor 2020 (by WJCI): 0.247

Google Scholar Citations (March 2022): 6907

Google-based Impact Factor (2021): 0.68

h-index (December 2021): 37

i10-index (December 2021): 172

(Click Here to Learn More)