Perhaps the most well known implementation of wavelet compression, today, is JPEG 2000. By representing data in terms of a series of scaling coefficients and time shifts of a wavelet basis, we can store data efficiently with minimal loss. This type of compression can be used for signals which exist in any dimension. The problem is, choosing a wavelet basis which suits your data. And while there is a proof of existence of an optimal wavelet basis, there is no common procedure to find it. Through experimention and analysis using IACS supercomputers, we hope to uncover this procedure. Wavelets have uses from data compression, to biomedical and astrophysical applications, and of course, music.
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