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In this paper we explore the use of the discrete wavelet transform
analysis of an arbitrary signal in order to improve the data compression
capability of data coders. Wavelet analysis is widespread used in image
codifiers, for example in JPEG2000. The wavelet compression methods are
adequate for representing transients, such as percussion sounds in audio, or
high-frequency components in two-dimensional images, for example an image
of stars on a night sky. The wavelet analysis provides a subband decomposition
of any arbitrary signal, and this enables a lossless or a lossy implementation
with the same architecture. The signals could range from speech to sounds or
music, but the approach is more orientated to other natural signals like arbitrary
discrete series, EEG or ECG. Experimental results based on coefficients
quantification, show a loss less compression of 2: I in all kind of signals, and
lossy results preserving most of the signal waveform of about 5: I to 3: I.
CitationReig, R. [et al.]. Data compression of natural signals based on discrete wavelet transform analysis. A: International conference on non-linear speech processing. "An ISCA tutorial and research workshop on non-linear speech processing : NOLISP 09 : Non-Linear Speech Processing 2009, programme and abstracts, Vic, june 25-27, 2009". Vic: 2009.
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