Decorrelation

Source: Wikipedia, the free encyclopedia.

Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal.[1] A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible. Since the minimum possible autocorrelation for a given signal energy is achieved by equalising the power spectrum of the signal to be similar to that of a white noise signal, this is often referred to as signal whitening.

Process

Most decorrelation algorithms are

linear, but there are also non-linear
decorrelation algorithms.

Many data compression algorithms incorporate a decorrelation stage.

Karhunen–Loève transform, or a simplified approximation such as the discrete cosine transform
.

By comparison,

sub-band coders
do not generally have an explicit decorrelation step, but instead exploit the already-existing reduced correlation within each of the sub-bands of the signal, due to the relative flatness of each sub-band of the power spectrum in many classes of signals.

Linear predictive coders
can be modelled as an attempt to decorrelate signals by subtracting the best possible linear prediction from the input signal, leaving a whitened residual signal.

Decorrelation techniques can also be used for many other purposes, such as reducing

echo cancellers
.

In

colour differences found in each pixel
of an image. This is generally termed as 'decorrelation stretching'.

The concept of decorrelation can be applied in many other fields. In neuroscience, decorrelation is used in the analysis of the neural networks in the human visual system. In cryptography, it is used in cipher design (see Decorrelation theory) and in the design of hardware random number generators.

See also

References

  1. ^ "Decorrelation - an overview | ScienceDirect Topics". www.sciencedirect.com. Retrieved 2020-09-25.
  2. ^ "Data Compression - an overview | ScienceDirect Topics". www.sciencedirect.com. Retrieved 2020-09-25.

External links