Multidimensional signal processing: Difference between revisions

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== Sampling ==

{{main|Multidimensional sampling}}

Multidimensional sampling requires different analysis than typical 1-D sampling. Single dimension sampling is executingexecuted by selecting points along a continuous line and storing the values of this data stream. In the case of multidimensional sampling, the data is selected utilizing a [[Lattice (order)|lattice]], which is a "pattern" based on the sampling [[vector (mathematics and physics)|vectors]] of the m-D data set.<ref name="mer83">Mersereau, R.; Speake, T., "The processing of periodically sampled multidimensional signals," Acoustics, IEEE Transactions on Speech and Signal Processing, vol.31, no.1, pp.188-194, Feb 1983.</ref> These vectors can be single dimensional or multidimensional depending on the data and the application.<ref name="mer83" />

Multidimensional sampling is similar to classical sampling as it must adhere to the [[Nyquist–Shannon sampling theorem]]. It is affected by [[aliasing]] and considerations must be made for eventual reconstruction.