An Efficient Data Compression Approach based on Entropic Codingfor Network Devices with Limited Resources

Elie Fute Tagne, Hugues Marie Kamdjou, Alain Bertrand Bomgni and Armand Nzeukou

European Journal of Electrical Engineering and Computer Science, 2019, vol. 3, issue 5

Abstract: The expansion of sensitive dataderiving from a variety of applications has requiredthe need to transmit and/or archivethem with increased performance in terms of quality, transmission delay or storage volume. However, lossless compression techniques are almost unacceptable in the application fields where data does not allow alterations because of the fact that loss of crucial information can distort the analysis. This paper introduces MediCompress, a lightweight lossless data compression approach for irretrievable data like those from the medical or astronomy fields. The proposed approachis based on entropic Arithmetic coding, Run-length encoding, Burrows-wheeler transform and Move-to-front encoding. The results obtained on medical images have an interesting Compression Ratio (CR) in comparison with the lossless compressor SPIHT and a better Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) than SPIHT and JPEG2000.

Keywords: Big Data; Entropic Coding; Image Compression; Lossless Compression; Telemedicine (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:3:y:2019:i:5:id:19121

DOI: 10.24018/ejece.2019.3.5.121

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