The discrete wavelet transform is less computationally complex, taking O("N") time as compared to O("N" log "N") for the fast Fourier transform. This computational advantage is not inherent to the transform, but reflects the choice of a logarithmic division of frequency, in contrast to the equally spaced frequency divisions of the FFT (Fast Fourier Transform) which uses the same basis functions as DFT (Discrete Fourier Transform). It is also important to note that this complexity only applies when the filter size has no relation to the signal size. A wavelet without compact support such as the Shannon wavelet would require O("N"2). (For instance, a logarithmic Fourier Transform also exists with O("N") complexity, but the original signal must be sampled logarithmically in time, which is only useful for certain types of signals.)