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Data Compression and Wavelet Transforms

Published online by Cambridge University Press:  26 July 2016

G.M. Richter ,
M. Capaccioli ,
G. Longo  and
H. Lorenz
G.M. Richter
Affiliation:
Astrophysikalisches Institut Potsdam, An der Sternwarte 16, D-14482 Potsdam, Germany
M. Capaccioli
Affiliation:
Osservatorio Astronomico Capodimonte, via Moiariello 16, I-80131 Napoli, Italy
G. Longo
Affiliation:
Osservatorio Astronomico Capodimonte, via Moiariello 16, I-80131 Napoli, Italy
H. Lorenz
Affiliation:
Astrophysikalisches Institut Potsdam, An der Sternwarte 16, D-14482 Potsdam, Germany

Abstract

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Efficient data compression needs analyzing functions to recognise the local resolution of the signal. They are provided by the wavelet concept. The optimal wavelet (best information concentration) is defined by the image model of the application. For the most common images in astronomy the H-transform is optimal (in the sense of Karhunen-Loeve transform). The role of the H-transform in 2-dimensional processing is the same as the Haar-transform in 1-dimension, but it is not the ‘2-dimensional Haar-transform’ found in text books.

Type
Part Five: Image Detection, Cataloguing and Classification
Information
Symposium - International Astronomical Union , Volume 161: Astronomy from Wide-Field Imaging , 1994 , pp. 219 - 223
Copyright
Copyright © Kluwer 1994 

References

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