A new entropy measure based on the wavelet transform and noise modeling [image compression]

We present in this brief paper a new way to measure the information in a signal, based on noise modeling. We show that the use of such an entropy-related measure leads to good results for signal restoration.

[1]  Fionn Murtagh,et al.  Image Processing and Data Analysis - The Multiscale Approach , 1998 .

[2]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[3]  Jean-Luc Starck,et al.  Deconvolution of astronomical images using the multiscale maximum entropy method , 1996 .

[4]  Fionn Murtagh,et al.  Multiresolution Support Applied to Image Filtering and Restoration , 1995, CVGIP Graph. Model. Image Process..

[5]  Ali Tabatabai,et al.  Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[6]  Jstor,et al.  Proceedings of the American Mathematical Society , 1950 .

[7]  E. Jaynes Information Theory and Statistical Mechanics , 1957 .

[8]  R. Narayan,et al.  Maximum Entropy Image Restoration in Astronomy , 1986 .

[9]  D. J. M. Kester,et al.  Pyramid maximum entropy images of IRAS survey data. , 1994 .

[10]  É. Thiébaut,et al.  Strict a priori constraints for maximum-likelihood blind deconvolution , 1995 .

[11]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[12]  J. Skilling Classic Maximum Entropy , 1989 .

[13]  Jean-Paul Donnay,et al.  Introduction au traitement numérique d’images , 1987 .

[14]  Ahmad Zandi,et al.  CREW: Compression with Reversible Embedded Wavelets , 1995, Proceedings DCC '95 Data Compression Conference.