# ResamplingAlgorithmData

ResamplingAlgorithmData[rs,"prop"]

gives the specified property "prop" for the resampling rs.

# Details • Various resampling methods can be used in a spatial transformation when applied to an image or an array of data. For a list of supported resampling methods, see the reference page for Resampling.
• Use ResamplingAlgorithmData to obtain properties of resampling methods such as kernel shape, kernel support, and regularity.
• returns supported resampling methods with default parameter settings.
• ResamplingAlgorithmData[rs,"Properties"] gives the list of all applicable properties for a given resampling rs. If a property does not apply, Missing["reason"] is returned.
• The following properties can be obtained, if applicable:
•  "Kernel" interpolation kernel "Basis" basis function for interpolation "FourierKernel" Fourier transform of the interpolation kernel "FourierBasis" Fourier transform of the basis function "Regularity" regularity of the interpolation kernel "KernelSupport" kernel radius "BasisSupport" radius of basis function support "ErrorKernel" error kernel
• Resampling properties "Kernel" and "Basis" are different if a method consists of a linear convolution in combination with a recursive filter, such as "Spline" and "OMOMS".

# Examples

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## Basic Examples(3)

Extract some properties of a resampling method:

List available properties for a given resampling method:

Some properties may not be applicable for all resampling methods:

## Scope(5)

All resampling methods up to order with default parameter settings:

Properties of "Nearest" resampling:

Properties of "CatmullRom":

The convolution kernel:

Plot of the kernel:

The Fourier kernel and the plot of it:

The kernel regularity is and the radius is 2:

The error kernel and its series expansion:

Properties of the {"Schaum",2} resampling method:

Interpolation kernel:

Regularity of the interpolating kernel is (discontinuous):

Support interval of the kernel:

Fourier transform of the kernel:

Attenuation plot:

Approximation kernel:

Approximation order and expansion coefficient:

Properties of the {"Spline",5} resampling method:

Resampling basis function:

Effective interpolation kernel:

Regularity of the interpolating kernel:

Support interval of the basis function:

Fourier transform of the kernel:

Attenuation plot:

Approximation kernel:

Approximation order and expansion coefficient:

## Properties & Relations(4)

Some resampling methods are not symmetric:

Some resampling methods are not continuous:

Some resampling methods are equivalent. For example"CatmullRom" and {"Meijering",3}:

Signal to noise ratio for signals with a Markov power spectrum of :