A novel gray image representation using overlapping rectangular NAM and extended shading approach
Journal of Visual Communication and Image Representation
Predicting protective bacterial antigens using random forest classifiers
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A difference engine for image reconstruction
EGGH'93 Proceedings of the Eighth Eurographics conference on Graphics Hardware
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A novel theory is introduced for analyzing image compression methods that are based on compression of wavelet decompositions. This theory precisely relates (a) the rate of decay in the error between the original image and the compressed image as the size of the compressed image representation increases (i.e., as the amount of compression decreases) to (b) the smoothness of the image in certain smoothness classes called Besov spaces. Within this theory, the error incurred by the quantization of wavelet transform coefficients is explained. Several compression algorithms based on piecewise constant approximations are analyzed in some detail. It is shown that, if pictures can be characterized by their membership in the smoothness classes considered, then wavelet-based methods are near-optimal within a larger class of stable transform-based, nonlinear methods of image compression. Based on previous experimental research it is argued that in most instances the error incurred in image compression should be measured in the integral sense instead of the mean-square sense