Comparing/Contrasting Cosine and Fourier Transforms

What are the differences between a (discrete) cosine transform and a (discrete) Fourier transform? I know the former is used in JPEG encoding, while the latter plays a big part in signal and image processing. How related are they?

Cosine transforms are nothing more than shortcuts for computing the Fourier transform of a sequence with special symmetry (e.g. if the sequence represents samples from an even function).

To give a concrete example in Mathematica ($VersionNumber >= 6), consider the sequence

smp = {1., 2., 3., 4., 5., 4., 3., 2.};

The sequence has redundancy (e.g. smp[[2]] == smp[[8]], but note that in usual Fourier work, the indexing is taken to be from $0$ to $n-1$ instead of $1$ to $n$). A sequence like smp is termed an even sequence. The discrete Fourier transform of smp can be expected to have redundancy as well:

Fourier[smp] // Chop
{8.48528137423857, -2.414213562373095, 0, -0.4142135623730949, 0,
-0.4142135623730949, 0, -2.414213562373095}

and the discrete Fourier transform is itself even. One could hope to have a way to compute the discrete Fourier transform without redundancy, and this is where the type I discrete cosine transform (DCT-I) comes in:

FourierDCT[Take[smp, Length[smp]/2 + 1], 1] // Chop
{8.48528137423857, -2.414213562373095, 0., -0.4142135623730949, 0.}

The more usual type II discrete cosine transform (DCT-II) is the redundancy-free method for computing the Fourier transform of a so-called "quarter wave even" sequence (with an additional transformation to make the results entirely real for real inputs). A quarter wave even sequence looks like this:

smp = {1., 2., 3., 4., 4., 3., 2., 1.};

and the correspondence (e.g. smp[[2]] == smp[[7]]) is easily seen. DCT-II requires only half of the given sequence to do its job:

Exp[2 Pi I Range[0, 7]/16] Fourier[smp]/Sqrt[2] // Chop
{4.999999999999999, -1.5771610149494746, 0, -0.11208538229199128, 0, 
 0.11208538229199126, 0, 1.5771610149494748}

FourierDCT[Take[smp, Length[smp]/2], 2] // Chop
{5., -1.577161014949475, 0, -0.11208538229199139}

(We see in this example that the exploitation of symmetry in this case led to a slightly more accurate result.)

The other two types of discrete cosine transforms, as well as the four types of discrete sine transforms, are intended to be redundancy-free methods for computing discrete Fourier transforms. For DCT-I, one can deal with a sequence of length $\frac{N}{2}+1$ instead of a sequence of length $N$, while for DCT-II, only a length $\frac{N}{2}$ sequence is required. This represents a savings in computational time and effort. (I assume the case of even length here; a similar symmetry property can be established for the case of odd length.)

In any event, I wish to point out two good references on how FFT and the DCTs/DSTs are related: Van Loan's Computational Frameworks for the Fast Fourier Transform and Briggs/Henson's The DFT: an owner's manual for the discrete Fourier transform.