Discrete Cosine Transform: Difference between revisions
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m (Nobody in there right mind know's what the hell "orthogonal transform" is unless you are into linear algebra. Break it down more ;-D. On a burden of theoretical proof it's scary.) |
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The Discrete Cosine Transform (DCT) is similar to the [[DFT|Discrete Fourier Transform]]: It transforms a signal from the [[time domain]] into the [[frequency domain]]. And just as the fourier transform uses sine and cosine waves to represent a signal, the DCT only uses cosine waves. The DCT I and II, is mostly used in image compression, while [[MDCT]] (DCT-IV) is used in audio encoding. | The Discrete Cosine Transform (DCT) is similar to the [[DFT|Discrete Fourier Transform]]: It transforms a signal from the [[time domain]] into the [[frequency domain]]. And just as the fourier transform uses sine and cosine waves to represent a signal, the DCT only uses cosine waves. The DCT I and II, is mostly used in image compression, while [[MDCT]] (DCT-IV) is used in audio encoding. | ||
The DCT is an invertible, discrete orthogonal transformation. | The DCT is an invertible, discrete orthogonal transformation. An orthogonal transformation consists of multiply the inner products of corresponding vectors or matrixes (including measures for angles and lengths). | ||
Revision as of 06:27, 27 July 2006
The Discrete Cosine Transform (DCT) is similar to the Discrete Fourier Transform: It transforms a signal from the time domain into the frequency domain. And just as the fourier transform uses sine and cosine waves to represent a signal, the DCT only uses cosine waves. The DCT I and II, is mostly used in image compression, while MDCT (DCT-IV) is used in audio encoding.
The DCT is an invertible, discrete orthogonal transformation. An orthogonal transformation consists of multiply the inner products of corresponding vectors or matrixes (including measures for angles and lengths).