The wavelet memory types are used to represent the result of a wavelet transform applied to some data. The data can be a signal, in this case the operation is called a one-dimensional wavelet tranform, or it can be an image. In that case, the operation is called a two-dimensional wavelet transform. Operations on data of higher dimension are not supported at this time.

A wavelet transform is a time-scale operator: it adds therefore one dimension
to the data (the scale).
The meaning of the wavelet coefficients recorded into a wavelet-type variable depends to the choice of the discretization.
The finest one is known as the *continuous wavelet transform*: several
voices per octave can be computed for the scale.
The *orthogonal (or biorthogonal) wavelet transform*
allows to decompose
the data into an orthogonal (or biorthogonal) basis: a wavelet coefficient
corresponds to a scalar product. In this case, only one voice per octave
is computed and a decimation is achieved on the time (or space) domain.
The *dyadic wavelet transform* computes also only one voice per octave, but
without decimation along the time axis. It corresponds to a decomposition into wavelets
which generate a *frame*. It is often used to obtain a translation-invariant representation,
from which the *wavelet maxima representation* can be deduced.