Vector quantization and signal compression pdf

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vector quantization and signal compression pdf

Vector Quantization Data Compression

Vector quantization VQ is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points vectors into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensional data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression.
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Published 07.01.2019

Vector Quantization Part-1

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.

Wavelet based vector quantization with treecode vectors for EMG Signal compression

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Skip to main content Skip to table of contents. Advertisement Hide. Vector Quantization and Signal Compression. Front Matter Pages i-xxii. Front Matter Pages Pages




  1. Crescencio M. says:

    Compression of medical images has always been viewed with skepticism, since the loss of information involved is thought to affect diagnostic information.

  2. Ibi C. says:

    In this article, we make a comparative study for a new approach compression between discrete cosine transform DCT and discrete wavelet transform DWT.

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