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author Chris Cannam <cannam@all-day-breakfast.com>
date Wed, 20 Mar 2013 15:35:50 +0000
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cannam@95 3 <title>The Discrete Hartley Transform - FFTW 3.3.3</title>
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cannam@95 48 <a name="The-Discrete-Hartley-Transform"></a>
cannam@95 49 <p>
cannam@95 50 Previous:&nbsp;<a rel="previous" accesskey="p" href="Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029.html#Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029">Real even/odd DFTs (cosine/sine transforms)</a>,
cannam@95 51 Up:&nbsp;<a rel="up" accesskey="u" href="More-DFTs-of-Real-Data.html#More-DFTs-of-Real-Data">More DFTs of Real Data</a>
cannam@95 52 <hr>
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cannam@95 54
cannam@95 55 <h4 class="subsection">2.5.3 The Discrete Hartley Transform</h4>
cannam@95 56
cannam@95 57 <p>If you are planning to use the DHT because you've heard that it is
cannam@95 58 &ldquo;faster&rdquo; than the DFT (FFT), <strong>stop here</strong>. The DHT is not
cannam@95 59 faster than the DFT. That story is an old but enduring misconception
cannam@95 60 that was debunked in 1987.
cannam@95 61
cannam@95 62 <p>The discrete Hartley transform (DHT) is an invertible linear transform
cannam@95 63 closely related to the DFT. In the DFT, one multiplies each input by
cannam@95 64 cos - i * sin (a complex exponential), whereas in the DHT each
cannam@95 65 input is multiplied by simply cos + sin. Thus, the DHT
cannam@95 66 transforms <code>n</code> real numbers to <code>n</code> real numbers, and has the
cannam@95 67 convenient property of being its own inverse. In FFTW, a DHT (of any
cannam@95 68 positive <code>n</code>) can be specified by an r2r kind of <code>FFTW_DHT</code>.
cannam@95 69 <a name="index-FFTW_005fDHT-98"></a><a name="index-discrete-Hartley-transform-99"></a><a name="index-DHT-100"></a>
cannam@95 70 Like the DFT, in FFTW the DHT is unnormalized, so computing a DHT of
cannam@95 71 size <code>n</code> followed by another DHT of the same size will result in
cannam@95 72 the original array multiplied by <code>n</code>.
cannam@95 73 <a name="index-normalization-101"></a>
cannam@95 74 The DHT was originally proposed as a more efficient alternative to the
cannam@95 75 DFT for real data, but it was subsequently shown that a specialized DFT
cannam@95 76 (such as FFTW's r2hc or r2c transforms) could be just as fast. In FFTW,
cannam@95 77 the DHT is actually computed by post-processing an r2hc transform, so
cannam@95 78 there is ordinarily no reason to prefer it from a performance
cannam@95 79 perspective.<a rel="footnote" href="#fn-1" name="fnd-1"><sup>1</sup></a>
cannam@95 80 However, we have heard rumors that the DHT might be the most appropriate
cannam@95 81 transform in its own right for certain applications, and we would be
cannam@95 82 very interested to hear from anyone who finds it useful.
cannam@95 83
cannam@95 84 <p>If <code>FFTW_DHT</code> is specified for multiple dimensions of a
cannam@95 85 multi-dimensional transform, FFTW computes the separable product of 1d
cannam@95 86 DHTs along each dimension. Unfortunately, this is not quite the same
cannam@95 87 thing as a true multi-dimensional DHT; you can compute the latter, if
cannam@95 88 necessary, with at most <code>rank-1</code> post-processing passes
cannam@95 89 [see e.g. H. Hao and R. N. Bracewell, <i>Proc. IEEE</i> <b>75</b>, 264&ndash;266 (1987)].
cannam@95 90
cannam@95 91 <p>For the precise mathematical definition of the DHT as used by FFTW, see
cannam@95 92 <a href="What-FFTW-Really-Computes.html#What-FFTW-Really-Computes">What FFTW Really Computes</a>.
cannam@95 93
cannam@95 94 <div class="footnote">
cannam@95 95 <hr>
cannam@95 96 <h4>Footnotes</h4><p class="footnote"><small>[<a name="fn-1" href="#fnd-1">1</a>]</small> We provide the DHT mainly as a byproduct of some
cannam@95 97 internal algorithms. FFTW computes a real input/output DFT of
cannam@95 98 <em>prime</em> size by re-expressing it as a DHT plus post/pre-processing
cannam@95 99 and then using Rader's prime-DFT algorithm adapted to the DHT.</p>
cannam@95 100
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