Node:The Discrete Hartley Transform, Previous:Real even/odd DFTs (cosine/sine transforms), Up:More DFTs of Real Data
The discrete Hartley transform (DHT) is an invertible linear transform
closely related to the DFT. In the DFT, one multiplies each input by
cos - i * sin (a complex exponential), whereas in the DHT each
input is multiplied by simply cos + sin. Thus, the DHT
transforms n real numbers to n real numbers, and has the
convenient property of being its own inverse. In FFTW, a DHT (of any
positive n) can be specified by an r2r kind of FFTW_DHT.
If you are planning to use the DHT because you've heard that it is "faster" than the DFT (FFT), stop here. That story is an old but enduring misconception that was debunked in 1987: a properly designed real-input FFT (such as FFTW's) has no more operations in general than an FHT. Moreover, in FFTW, the DHT is ordinarily slower than the DFT for composite sizes (see below).
Like the DFT, in FFTW the DHT is unnormalized, so computing a DHT of
size n followed by another DHT of the same size will result in
the original array multiplied by n.
The DHT was originally proposed as a more efficient alternative to the DFT for real data, but it was subsequently shown that a specialized DFT (such as FFTW's r2hc or r2c transforms) could be just as fast. In FFTW, the DHT is actually computed by post-processing an r2hc transform, so there is ordinarily no reason to prefer it from a performance perspective.1 However, we have heard rumors that the DHT might be the most appropriate transform in its own right for certain applications, and we would be very interested to hear from anyone who finds it useful.
If FFTW_DHT is specified for multiple dimensions of a
multi-dimensional transform, FFTW computes the separable product of 1d
DHTs along each dimension. Unfortunately, this is not quite the same
thing as a true multi-dimensional DHT; you can compute the latter, if
necessary, with at most rank-1 post-processing passes
[see e.g. H. Hao and R. N. Bracewell, Proc. IEEE 75, 264-266 (1987)].
For the precise mathematical definition of the DHT as used by FFTW, see What FFTW Really Computes.
We provide the DHT mainly as a byproduct of some internal algorithms. FFTW computes a real input/output DFT of prime size by re-expressing it as a DHT plus post/pre-processing and then using Rader's prime-DFT algorithm adapted to the DHT.