Main Content

Estimate Standard Deviation of Quantization Noise of Real-Valued Signal

Quantizing a real signal to p bits of precision can be modeled as a linear system that adds normally distributed noise with a standard deviation of ϛnoise=2-p12 [1,2].

Compute the theoretical quantization noise standard deviation with p bits of precision using the fixed.realQuantizationNoiseStandardDeviation function.

p = 14;
theoreticalQuantizationNoiseStandardDeviation = fixed.realQuantizationNoiseStandardDeviation(p);

The returned value is ϛnoise=2-p12.

Create a real signal with n samples.

n = 1e6;
x = rand(1,n);

Quantize the signal with p bits of precision.

wordLength = 16;
x_quantized = quantizenumeric(x,1,wordLength,p);

Compute the quantization noise by taking the difference between the quantized signal and the original signal.

quantizationNoise = x_quantized - x;

Compute the measured quantization noise standard deviation.

measuredQuantizationNoiseStandardDeviation = std(quantizationNoise)
measuredQuantizationNoiseStandardDeviation = 1.7607e-05

Compare the actual quantization noise standard deviation to the theoretical and see that they are close for large values of n.

theoreticalQuantizationNoiseStandardDeviation = 1.7619e-05


  1. Bernard Widrow. “A Study of Rough Amplitude Quantization by Means of Nyquist Sampling Theory”. In: IRE Transactions on Circuit Theory 3.4 (Dec. 1956), pp. 266–276.

  2. Bernard Widrow and István Kollár. Quantization Noise – Roundoff Error in Digital Computation, Signal Processing, Control, and Communications. Cambridge, UK: Cambridge University Press, 2008.

See Also