The periodic Bar-David (1974) sampling theorem provides an implicit representation of band limited signals using their crossings with a cosine function to form a multiplicative representation involving a Riesz product. The cosine crossings form a unique and stable representation of the signal. We incorporate the wavelet transform into the cosine crossing representation and show that they may be used to compactly represent overcomplete signal expansions.

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.>

A theory developed by DeVore, Jawerth and Popov of nonlinear approximation by both orthogonal and nonorthogonal wavelets has been applied to problems in surface and image compression. This theory relates precisely the norms in which the error is measured, the rate of decay in that error as the compression decreases, and the smoothness of the data. This overview of the previous results expands the argument, made earlier for image compression, that frequency-amplitude response curves that arise quite naturally in problems involving human visual and audio perception should be used to decide the quantization strategy for wavelet coefficients and the norm in which to measure the error in compressed data.>