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The Wikipedia article on Wavelet Transform states that:

Wavelet compression is not good for all kinds of data: transient signal characteristics mean good wavelet compression, while smooth, periodic signals are better compressed by other methods, particularly traditional harmonic compression (frequency domain, as by Fourier transforms and related).

What is the precise quantification of this statement? For the smooth periodic signals we have for example: if the signal is of class $C^d$ of $d$-times continuously differentiable periodic functions, then the partial Fourier sum of order $N$ gives uniform approximation error at most $N^{-d-1}$. What would be the corresponding characterization for wavelet series?

To make the question more concrete: if the signal is piecewise $C^d$-periodic, with, say, one discontinuity, what approximation error do I get with $N$ wavelet coefficients (the Fourier approximation gives only $N^{-1}$ away from the discontinuity)? Conversely, how many coefficients do I need to get an accuracy $\epsilon$?

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The Wikipedia article on Wavelet Transform states that:

Wavelet compression is not good for all kinds of data: transient signal characteristics mean good wavelet compression, while smooth, periodic signals are better compressed by other methods, particularly traditional harmonic compression (frequency domain, as by Fourier transforms and related).

What is the precise quantification of this statement? For the smooth periodic signals we have for example: if the signal is of class $C^d$ of $d$-times continuously differentiable periodic functions, then the partial Fourier sum of order $N$ gives uniform approximation error at most $N^{-d-1}$. What would be the corresponding characterization for wavelet series?

To make the question more concrete: if the signal is piecewise $C^d$-periodic, with, say, one discontinuity, what approximation error do I get with $N$ wavelet coefficients (the Fourier approximation gives only $N^{-1}$ away from the discontinuity)?

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