This is affected by choosing suitable basis functions that allow for this. Based on the uncertainty principle of signal processing,. The higher the required resolution in time, the lower the resolution in frequency has to be. The transformed signal provides information about the time and the frequency. Therefore, wavelet-transformation contains information similar to the short-time-Fourier-transformation , but with additional special properties of the wavelets, which show up at the resolution in time at higher analysis frequencies of the basis function.
The difference in time resolution at ascending frequencies for the Fourier transform and the wavelet transform is shown below. This shows that wavelet transformation is good in time resolution of high frequencies, while for slowly varying functions, the frequency resolution is remarkable. Wavelet compression is a form of data compression well suited for image compression sometimes also video compression and audio compression. The goal is to store image data in as little space as possible in a file. Wavelet compression can be either lossless or lossy. Using a wavelet transform, 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.
This means that the transient elements of a data signal can be represented by a smaller amount of information than would be the case if some other transform, such as the more widespread discrete cosine transform , had been used. Discrete wavelet transform has been successfully applied for the compression of electrocardiograph ECG signals  In this work, the high correlation between the corresponding wavelet coefficients of signals of successive cardiac cycles is utilized employing linear prediction.
Wavelet compression is not good for all kinds of data: transient signal characteristics mean good wavelet compression, while smooth, periodic signals are better compressed by other methods, particularly traditional harmonic compression frequency domain, as by Fourier transforms and related. See Diary Of An x Developer: The problems with wavelets for discussion of practical issues of current methods using wavelets for video compression. First a wavelet transform is applied. This produces as many coefficients as there are pixels in the image i. Explore documentation for Wavelet Toolbox functions and features, including release notes and examples.
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Foundations of Signal Processing and Fourier and Wavelet Signal Processing :: Book Site
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Mathematical Analysis, Wavelets, and Signal Processing
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Time-Frequency Analysis Analyze signals jointly in time and frequency with the continuous wavelet transform. Learn more. Detection of singularities and feature extraction; Data compression; Numerical solutions of integral equations; Summary and Notes; References; Subject Index.
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