Quantization Error - Quantization Error Models

Quantization Error Models

In the typical case, the original signal is much larger than one LSB. When this is the case, the quantization error is not significantly correlated with the signal, and has an approximately uniform distribution. In the rounding case, the quantization error has a mean of zero and the RMS value is the standard deviation of this distribution, given by . In the truncation case the error has a non-zero mean of and the RMS value is . In the eight-bit ADC example, the RMS rounding error represents 0.113% of the full signal range.

At lower amplitudes the quantization error becomes dependent on the input signal, resulting in distortion. This distortion is created after the anti-aliasing filter, and if these distortions are above 1/2 the sample rate they will alias back into the audio band. In order to make the quantization error independent of the input signal, noise with an amplitude of 2 least significant bits is added to the signal. This slightly reduces signal to noise ratio, but, ideally, completely eliminates the distortion. It is known as dither.