• Kenneth Lange
Part of the Statistics and Computing book series (SCO)


Wavelets are just beginning to enter statistical theory and practice [2, 5, 7, 10, 12]. The pace of discovery is still swift, and except for orthogonal wavelets, the theory has yet to mature. However, the advantages of wavelets are already obvious in application areas such as image compression. Wavelets are more localized in space than the competing sines and cosines of Fourier series. They also use fewer coefficients in representing images, and they pick up edge effects better. The secret behind these successes is the capacity of wavelets to account for image variation on many different scales.


Image Compression Scaling Function Mother Wavelet Wavelet Shrinkage Haar Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer New York 2010

Authors and Affiliations

  1. 1.Departments of Biomathematics, Human Genetics, and Statistics David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA

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