Discrete sine transform

The discrete sine transform (DST) is a frequency transform similar to the discrete Fourier transform (DFT), but using only real numbers. It is equivalent to the imaginary parts of a DFT of roughly twice the length, operating on real data with odd symmetry (since the Fourier transform of a real and odd function is real and imaginary), where in some variants the input and/or output data are shifted by half a sample.

A related transform is the discrete cosine transform (DCT), which is equivalent to a DFT of real and even functions.

Table of contents 1 Definition 1.1 DST-I 1.2 DST-II 1.3 DST-III 1.4 DST-IV 1.5 DST V-VIII 1.6 Inverse Transforms 1.7 Computation 2 Applications

Definition

Formally, the discrete sine transform is a linear, invertible function F : Rⁿ -> Rⁿ (where R denotes the set of real numbers). There are several variants of the DST with slightly modified definitions. The n real numbers x₀, ...., x_n-1 are transformed into the n real numbers f₀, ..., f_n-1 according to one of the formulas:

DST-I

<math>f_j = \sum_{k=0}^{n-1} x_k \sin \left[\frac{\pi}{n+1} (j+1) (k+1) \right]</math>

A DST-I of n=3 real numbers abc is exactly equivalent to a DFT of eight real numbers 0abc0(-c)(-b)(-a) (odd symmetry). (In contrast, DST types II-IV involve a half-sample shift in the equivalent DFT.)

Thus, the DST-I corresponds to the boundary conditions: x_k is odd around k=-1 and odd around k=n; similarly for f_j.

DST-II

<math>f_j =

   \sum_{k=0}^{n-1} x_k \sin \left[\frac{\pi}{n} (j+1) (k+1/2) \right]</math>

Some authors further multiply the f_n-1 term by 1/√2 (see below for the corresponding change in DST-III); this does not affect the basic properties, but breaks the direct correspondence with a real-odd DFT of half-shifted input.

The DST-II implies the boundary conditions: x_k is odd around k=-1/2 and odd around k=n-1/2; f_j is odd around j=-1 and even around j=n-1.

DST-III

<math>f_j = \frac{(-1)^j}{2} x_{n-1} +

   \sum_{k=0}^{n-2} x_k \sin \left[\frac{\pi}{n} (j+1/2) (k+1) \right]</math>

Some authors further multiply the x_n-1 term by √2 (see above for the corresponding change in DST-II); this does not affect the basic properties, but breaks the direct correspondence with a real-odd DFT of half-shifted output.

The DST-III implies the boundary conditions: x_k is odd around k=-1 and even around k=n-1; f_j is odd around j=-1/2 and odd around j=n-1/2.

DST-IV

<math>f_j =

   \sum_{k=0}^{n-1} x_k \sin \left[\frac{\pi}{n} (j+1/2) (k+1/2) \right]</math>

The DST-IV implies the boundary conditions: x_k is odd around k=-1/2 and even around k=n-1/2; similarly for f_j.

DST V-VIII

In principle, there are actually four additional types of discrete sine transform (Martucci, 1994), corresponding to real-odd DFTs of logically odd order, which have factors of n+1/2 in the denominators of the sine arguments. However, these variants seem to be rarely used in practice.

Inverse Transforms

The inverse of DST-I is DST-I multiplied by 2/(n+1). The inverse of DST-IV is DST-IV multiplied by 2/n. The inverse of DST-II is DST-III multiplied by 2/n (and vice versa).

Like for the DFT, the normalization factor in front of these transform definitions is merely a convention and differs between treatments. For example, some authors multiply the transforms by <math>\sqrt{2/n}</math> so that the inverse does not require any additional multiplicative factor.

Computation

Although the direct application of these formulas would require O(n²) operations, as in the fast Fourier transform (FFT) it is possible to compute the same thing with only O(n log n) complexity by factorizing the computation. (One can also compute DSTs via FFTs combined with O(n) pre- and post-processing steps.)

Applications

DSTs are widely employed in solving partial differential equations by spectral methods, where the different variants of the DST correspond to slightly different odd/even boundary conditions at the two ends of the array.

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References:

• S. A. Martucci, "Symmetric convolution and the discrete sine and cosine transforms," IEEE Trans. Sig. Processing SP-42, 1038-1051 (1994).
• Matteo Frigo and Steven G. Johnson: FFTW, http://www.fftw.org/. A free (GPL) C library that can compute fast DSTs (types I-IV) in one or more dimensions, of arbitrary size.