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Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding.

Ntsama EP, Colince W, Ele P - Springerplus (2016)

Bottom Line: To do this, we initially associated vector quantization and DCT, then vector quantization and DWT.The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding.Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio.

View Article: PubMed Central - PubMed

Affiliation: Physics Department, Faculty of Sciences, University of Ngaoundere, PO Box 454, Ngaoundere, Cameroon.

ABSTRACT
In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

No MeSH data available.


Compression scheme
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Fig3: Compression scheme

Mentions: The new compression approach is proposed through the Fig. 3.Fig. 3


Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding.

Ntsama EP, Colince W, Ele P - Springerplus (2016)

Compression scheme
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4829571&req=5

Fig3: Compression scheme
Mentions: The new compression approach is proposed through the Fig. 3.Fig. 3

Bottom Line: To do this, we initially associated vector quantization and DCT, then vector quantization and DWT.The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding.Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio.

View Article: PubMed Central - PubMed

Affiliation: Physics Department, Faculty of Sciences, University of Ngaoundere, PO Box 454, Ngaoundere, Cameroon.

ABSTRACT
In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

No MeSH data available.