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By L R. Ford

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2) We further take into account the fact that the statistics in different quadrants of a wavelettransformed image are different, and compute the weighted mean of the entropies in each quadrant of the transformed image. For example, in the two-scale decomposition shown in Figure 6, the weighted entropy (in bits/pixel) is computed as 1 1 (H(C2 ) + H(D2,v ) + H(D2,h ) + H(D2,d )) + (H(D1,v ) + H(D1,h ) + H(D1,d )) . 16 4 C2 D 2,h D 1,h D 2,v D 2,d D D 1,d 1,v Figure 6. A two-scale wavelet decomposition.

Adaptation of prediction methods is made based on local statistics of the pixels. In the wavelet transform approach to decorrelating an image, we could also use an adaptive scheme in deciding the use of wavelet filters on a pixel by pixel basis. As we have seen, there is not a wavelet filter that performs uniformly better than the others. Thus, the activities in some small neighborhood should be used in determining the type of filters to use. The use of adaptive wavelet filters in lossless and multiresolution representation of images warrants further investigation.

Proc. SPIE 2825, 1996. [19] T. A. C. M. Kalker and I. Shah. Ladder Structures for multidimensional linear phase perfect reconstruction filter banks and wavelets. In Proceedings of the SPIE Conference on Visual Communications and Image Processing (Boston), pages 12–20, 1992. [20] C. W. Kim, R. Ansari, and A. E. Cetin. A class of linear-phase regular biorthogonal wavelets. In Proc. IEEE Int. Conference on Acoustics, Speech, Signal Processing, pages 673–676, 1992. [21] R. Laroia, S. A. Tretter, and N.

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Automorphic Functions 1ST Edition by L R. Ford

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