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Derive the expression for signal to quantization noise ratio in PCM.
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  • Signal-to-Quantization-Noise Ratio (SQNR or SNqR) is widely used quality measure in analysing digitizing schemes such as PCM (pulse code modulation) and multimedia codecs.
  • 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.

  • The SQNR formula is derived from the general SNR (Signal-to-Noise Ratio) formula for the binary pulse-code modulated communication channel:

    $\mathrm{SNR}=\frac{3 \times 2^{2n}}{1+4P_e \times (2^{2n} - 1)} \frac{m_m(t)^2}{m_p(t)^2}$

    where

    $ P_{e}$ is the probability of received bit error

    $m_{p}(t)$ is the peak message signal level

    $m_{m}(t) $ is the mean message signal level

  • As SQNR applies to quantized signals, the formulae for SQNR refer to discrete-time digital signals.

  • Instead of $m_{t}$, we will use the digitized signal $ x{_n}$. For $N$ quantization steps, each sample, $ X $requires $\nu =\log _{2}N$ bits. The probability distribution function (pdf) representing the distribution of values in $ X$ and can be denoted as $f(x)$. The maximum magnitude value of any {\displaystyle x} 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: $\overline{x^2} = E[x^2] = P_{x^\nu}=\int_{}^{}x^2f(x)dx$ - The quantization noise power can be expressed as: $E[\tilde{x}^2] = \frac{x_{max}^2}{3\times4^\nu}$ 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: $\mathrm{SQNR}|_{dB}=P_{x^\nu}+6\nu+4.8$ 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 6dB $ (20\times log_{{10}}(2))$.
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