// Workers AI · dad joke modeWhat did signal-to-quantization-noise ratio say? "I'm ratio-nal.

Signal-to-quantization-noise ratio (SQNR or SN_qR) is widely used quality measure in analysing digitizing schemes such as pulse-code modulation (PCM). The SQNR reflects the relationship between the maximum nominal signal strength and the quantization error (also known as quantization noise) introduced in the analog-to-digital conversion.

As SQNR applies to quantized signals, the formulae for SQNR refer to discrete-time digital signals. Instead of the value- and time-continuous message signal $m(t)$, the digitized signal $x(n)$ will be used. For $N$ quantization steps, each sample, $x$ requires $\nu =\log _{2}N$ bits. The probability distribution function (PDF) represents the distribution of values in $x$ and can be denoted as $f(x)$. The maximum magnitude value of any $x$ is denoted by $x_{max}$.

As SQNR, like SNR, is a ratio of signal power to some noise power, it can be calculated as:

$\mathrm {SQNR} ={\frac {P_{signal}}{P_{noise}}}={\frac {E[x^{2}]}{E[{\tilde {x}}^{2}]}}$

The signal power is:

${\overline {x^{2}}}=E[x^{2}]=P_{x^{\nu }}=\int _{}^{}x^{2}f(x)dx$

The quantization noise power can be expressed as:

$E[{\tilde {x}}^{2}]={\frac {x_{max}^{2}}{3\times 4^{\nu }}}$

Giving:

$\mathrm {SQNR} ={\frac {3\times 4^{\nu }\times {\overline {x^{2}}}}{x_{max}^{2}}}$

When the SQNR is desired in terms of decibels (dB), a useful approximation to SQNR is:

$\mathrm {SQNR} |_{dB}=P_{x^{\nu }}+6.02\nu +4.77$

where $\nu$ is the number of bits in a quantized sample, and $P_{x^{\nu }}$ is the signal power calculated above. Note that for each bit added to a sample, the SQNR goes up by approximately 6 dB ($20\times log_{10}(2)$).

References

• B. P. Lathi, Modern Digital and Analog Communication Systems (3rd edition), Oxford University Press, 1998

External links

• Signal to quantization noise in quantized sinusoidal - Analysis of quantization error on a sine wave