Vector quantization and signal compression pdf
Bücher portofrei bestellen bei büheavenlybells.orgIn 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. 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.
Vector Quantization and Signal Compression
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Compression of medical images has always been viewed with skepticism, since the loss of information involved is thought to affect diagnostic information. The use of higher compression with high fidelity of the reconstructed images allows fast transmission of images over the Internet for prompt inspection by radiologists at remote locations in an emergency situation, while higher quality images follow in a progressive manner if desired. Such fast and progressive transmission can also be used for downloading large data sets such as the Visible Human at a quality desired by the users for research or education. This new adaptive vector quantization uses a neural networks-based clustering technique for efficient quantization of the wavelet-decomposed subimages, yielding minimal distortion in the reconstructed images undergoing high compression. National Center for Biotechnology Information , U. Journal List J Digit Imaging v.
Skip to search form Skip to main content. Wavelet coefficients, obtained from EMG signal samples, are arranged to form tree vectors TVs , where each vector has a hierarchical tree structure. Vector quantization is then applied for encoding to TVs, which uses a pre-calculated codebook. View PDF. Save to Library. Create Alert.