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1. The best compression of signal is achieved by using its eigen-transform (karhunen-Loeve transform). But it depends on signal, so inconvenient to use.
2. Many of signals in real world can be approximated by first order Markov process. So that is good idea to use eigen-transform of first order Markov process. ( By the way it has parameter 'beta', so let choose some value )
3. DCTII is eigentransform of processes which correlation matrix is very similar to first order Markov processes, when 'beta' close to 1.
Vetterli M., Kovacevic J.(1995) Wavelets and Subband Coding(488s).djvu
see page 375
N. Ahmed, T. Natarajan. and K. R Rao. Discrete cosine transform. IEEE Trans. on Computers, 23:88-93. January 1974.
If you want to see method of computing DCT through DFT and vice cersa - see h**p://vadkudr.boom.ru/Algorithms/vadkudr_Algorithms_eng.html#DTT
when using dft you have to keep complex numbers also you have to perform complex computation however by using dct you all deal with real numbers you perform your computation with real numbers and you keep real coefficients. So, you gain memory locations and your computational cost decrease. ( if your signal is real )
the DC term of the 8-point DCT represents a 1/8 scale of the input sequence and each following AC term adds another 1/8 as well...so the frequency info is concentrated in the first terms...and thus lossy compression can be done with minimal distortion in DCT...by simply discarding the higher frequency components...
also...the number of multiplications needed for DCT is less than DFT...since DCT multiplications are real...
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