SciPy

scipy.fftpack.dct

scipy.fftpack.dct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False)[source]

Return the Discrete Cosine Transform of arbitrary type sequence x.

Parameters :

x : array_like

The input array.

type : {1, 2, 3}, optional

Type of the DCT (see Notes). Default type is 2.

n : int, optional

Length of the transform.

axis : int, optional

Axis over which to compute the transform.

norm : {None, ‘ortho’}, optional

Normalization mode (see Notes). Default is None.

overwrite_x : bool, optional

If True the contents of x can be destroyed. (default=False)

Returns :

y : ndarray of real

The transformed input array.

See also

idct
Inverse DCT

Notes

For a single dimension array x, dct(x, norm='ortho') is equal to MATLAB dct(x).

There are theoretically 8 types of the DCT, only the first 3 types are implemented in scipy. ‘The’ DCT generally refers to DCT type 2, and ‘the’ Inverse DCT generally refers to DCT type 3.

Type I

There are several definitions of the DCT-I; we use the following (for norm=None):

                                   N-2
y[k] = x[0] + (-1)**k x[N-1] + 2 * sum x[n]*cos(pi*k*n/(N-1))
                                   n=1

Only None is supported as normalization mode for DCT-I. Note also that the DCT-I is only supported for input size > 1

Type II

There are several definitions of the DCT-II; we use the following (for norm=None):

          N-1
y[k] = 2* sum x[n]*cos(pi*k*(2n+1)/(2*N)), 0 <= k < N.
          n=0

If norm='ortho', y[k] is multiplied by a scaling factor f:

f = sqrt(1/(4*N)) if k = 0,
f = sqrt(1/(2*N)) otherwise.

Which makes the corresponding matrix of coefficients orthonormal (OO' = Id).

Type III

There are several definitions, we use the following (for norm=None):

                  N-1
y[k] = x[0] + 2 * sum x[n]*cos(pi*(k+0.5)*n/N), 0 <= k < N.
                  n=1

or, for norm='ortho' and 0 <= k < N:

                                    N-1
y[k] = x[0] / sqrt(N) + sqrt(2/N) * sum x[n]*cos(pi*(k+0.5)*n/N)
                                    n=1

The (unnormalized) DCT-III is the inverse of the (unnormalized) DCT-II, up to a factor 2N. The orthonormalized DCT-III is exactly the inverse of the orthonormalized DCT-II.

References

[R29]‘A Fast Cosine Transform in One and Two Dimensions’, by J. Makhoul, IEEE Transactions on acoustics, speech and signal processing vol. 28(1), pp. 27-34, http://dx.doi.org/10.1109/TASSP.1980.1163351 (1980).
[R30]Wikipedia, “Discrete cosine transform”, http://en.wikipedia.org/wiki/Discrete_cosine_transform

Examples

The Type 1 DCT is equivalent to the FFT (though faster) for real, even-symmetrical inputs. The output is also real and even-symmetrical. Half of the FFT input is used to generate half of the FFT output:

>>> fft(array([4., 3., 5., 10., 5., 3.])).real
array([ 30.,  -8.,   6.,  -2.,   6.,  -8.])
>>> dct(array([4., 3., 5., 10.]), 1)
array([ 30.,  -8.,   6.,  -2.])

Previous topic

scipy.fftpack.irfft

Next topic

scipy.fftpack.idct