# Quantization Error Derivation

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Due to the sampling step of an ADC, these harmonics get folded to the Nyquist band, pushing the total noise power into the Nyquist band and with an approximately white spectrum The application of such compressors and expanders is also known as companding. Quantization error models[edit] In the typical case, the original signal is much larger than one least significant bit (LSB). In contrast, mid-tread quantizers do have a zero output level, and can reach arbitrarily low bit rates per sample for input distributions that are symmetric and taper off at higher magnitudes. http://vealcine.com/quantization-error/quantization-noise-model-quantization-error.php

doi:10.1109/JRPROC.1948.231941 ^ Seymour Stein and J. When the input data can be modeled as a random variable with a probability density function (pdf) that is smooth and symmetric around zero, mid-riser quantizers also always produce an output Examples of fields where this limitation applies include electronics (due to electrons), optics (due to photons), biology (due to DNA), physics (due to Planck limits) and chemistry (due to molecules). The potential signal-to-quantization-noise power ratio therefore changes by 4, or 10 ⋅ log 10 ( 4 ) = 6.02 {\displaystyle \scriptstyle 10\cdot \log _{10}(4)\ =\ 6.02} https://en.wikipedia.org/wiki/Quantization_(signal_processing)

## Quantization Noise Power Formula

Around the quantum limit, the distinction between analog and digital quantities vanishes.[citation needed] See also[edit] Analog-to-digital converter Beta encoder Data binning Discretization Discretization error Posterization Pulse code modulation Quantile Regression dilution The step size Δ = 2 X m a x M {\displaystyle \Delta ={\frac {2X_{max}}{M}}} and the signal to quantization noise ratio (SQNR) of the quantizer is S Q N R For example, for N {\displaystyle N} **=8 bits, M {\displaystyle M} =256** levels and SQNR = 8*6 = 48dB; and for N {\displaystyle N} =16 bits, M {\displaystyle M} =65536 and

- Gray, Vector Quantization and Signal Compression, Springer, ISBN 978-0-7923-9181-4, 1991. ^ Hodgson, Jay (2010).
- Satish Kashyap 18.745 προβολές 35:35 Vector Quantization Part-1 - Διάρκεια: 8:22.
- Rounding and truncation are typical examples of quantization processes.
- doi:10.1109/MCOM.1977.1089500 ^ Rabbani, Majid; Joshi, Rajan L.; Jones, Paul W. (2009). "Section 1.2.3: Quantization, in Chapter 1: JPEG 2000 Core Coding System (Part 1)".

Please try the request again. is since Vfs = 2n q, then which simplifies to N.B. The difference between the original signal and the reconstructed signal is the quantization error and, in this simple quantization scheme, is a deterministic function of the input signal. Quantization Error In Analog To Digital Conversion Berklee Press. ^ William Fleetwood Sheppard, "On the Calculation of the Most Probable Values of Frequency Constants for data arranged according to Equidistant Divisions of a Scale", Proceedings of the London

Madhan Mohan 27.677 προβολές 4:03 Signal-to-Noise Ratio - Διάρκεια: 13:17. Quantization Noise In Pcm doi:10.1109/TIT.1984.1056920 ^ Toby Berger, "Optimum Quantizers and Permutation Codes", IEEE Transactions on Information Theory, Vol. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply.

If it is assumed that distortion is measured by mean squared error, the distortion D, is given by: D = E [ ( x − Q ( x ) ) 2

For the example uniform quantizer described above, the forward quantization stage can be expressed as k = ⌊ x Δ + 1 2 ⌋ {\displaystyle k=\left\lfloor {\frac {x}{\Delta }}+{\frac {1}{2}}\right\rfloor } Uniform Quantization The distinguishing characteristic of a mid-riser quantizer is that it has a classification threshold value that is exactly zero, and the distinguishing characteristic of a mid-tread quantizer is that is it The difference between the blue and red signals in the upper graph is the quantization error, which is "added" to the quantized signal and is the source of noise. Although r k {\displaystyle r_{k}} may depend on k {\displaystyle k} in general, and can be chosen to fulfill the optimality condition described below, it is often simply set to a

## Quantization Noise In Pcm

For signals whose amplitude is less than the FSR the Signal - to - Noise Ratio will be reduced. Recording and Producing in the Home Studio, p.38-9. Quantization Noise Power Formula The Art of Digital Audio 3rd Edition. Quantization Error Example It has been shown to be a valid model in cases of high resolution quantization (small Δ {\displaystyle \Delta } relative to the signal strength) with smooth probability density functions.[4][15] However,

Principles of Digital Audio 2nd Edition. http://vealcine.com/quantization-error/quantization-of-signals-quantization-error.php IT-44, No. 6, pp. 2325–2383, Oct. 1998. Madhan Mohan 15.150 προβολές **4:08 Digital Audio 102** - PCM, Bit-Rate, Quantisation, Dithering, Nyquists Sampling Theorum - PB15 - Διάρκεια: 6:06. When the input signal has a high amplitude and a wide frequency spectrum this is the case.[16] In this case a 16-bit ADC has a maximum signal-to-noise ratio of 98.09dB. How To Reduce Quantization Error

Rich Radke 8.100 προβολές 1:03:51 Quantization and Coding in A/D Conversion - Διάρκεια: 8:31. By using this site, you agree to the Terms of Use and Privacy Policy. The input and output sets involved in quantization can be defined in a rather general way. http://vealcine.com/quantization-error/quantization-error-and-quantization-step-size.php Your cache administrator is webmaster.

Lloyd, "Least Squares Quantization in PCM", IEEE Transactions on Information Theory, Vol. What Is Quantization The additive noise model for quantization error[edit] A common assumption for the analysis of quantization error is that it affects a signal processing system in a similar manner to that of For a fixed-length code using N {\displaystyle N} bits, M = 2 N {\displaystyle M=2^{N}} , resulting in S Q N R = 20 log 10 2 N = N

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However, in some quantizer designs, the concepts of granular error and overload error may not apply (e.g., for a quantizer with a limited range of input data or with a countably For some probabilistic source models, the best performance may be achieved when M {\displaystyle M} approaches infinity. Gray and David L. Quantization Step Size Formula doi:10.1109/TIT.1972.1054906 ^ Toby Berger, "Minimum Entropy Quantizers and Permutation Codes", IEEE Transactions on Information Theory, Vol.

The value of the measured signal is Vm = Vs - e, where Vm is the measured value, Vs is the actual value, and e is the error. In more elaborate quantization designs, both the forward and inverse quantization stages may be substantially more complex. The quantization error creates harmonics in the signal that extend well above the Nyquist frequency. this content Your cache administrator is webmaster.

doi:10.1109/TIT.2005.846397 ^ Pohlman, Ken C. (1989). The set of possible output values may be finite or countably infinite. Quantization replaces each real number with an approximation from a finite set of discrete values (levels), which is necessary for storage and processing by numerical methods. After defining these two performance metrics for the quantizer, a typical Rate–Distortion formulation for a quantizer design problem can be expressed in one of two ways: Given a maximum distortion constraint