Analog Digital Quantization Error
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the original analog signal (green), the quantized signal (black dots), the signal reconstructed from the quantized signal (yellow) and quantization error in analog to digital conversion the difference between the original signal and the reconstructed signal (red). quantization noise introduced by analog to digital conversion The difference between the original signal and the reconstructed signal is the quantization error and, in quantization error in digital communication this simple quantization scheme, is a deterministic function of the input signal. Quantization, in mathematics and digital signal processing, is the process of mapping a large set
Quantization Error Formula
of input values to a (countable) smaller set. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms. quantization error adc The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error. A device or algorithmic function that performs quantization is called a quantizer. An analog-to-digital converter is an example of a quantizer. Contents 1 Basic properties of quantization 2 Basic types of quantization 2.1 Analog-to-digital converter (ADC) 2.2 Rate–distortion optimization 3 Rounding example 4 Mid-riser and mid-tread uniform quantizers 5 Dead-zone quantizers 6 Granular distortion and overload distortion 7 The additive noise model for quantization error 8 Quantization error models 9 Quantization noise model 10 Rate–distortion quantizer design 11 Neglecting the entropy constraint: Lloyd–Max quantization 12 Uniform quantization and the 6 dB/bit approximation 13 Other fields 14 See also 15 Notes 16 References 17 External links Basic properties of quantization[edit] Because quantization is a many-to-few mapping, it is an inherently non-linear and irreversible process (i.e., because the same output value is shared by multiple input values, it is impossible in
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tour help Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring http://electronics.stackexchange.com/questions/61596/quantization-noise-and-quantization-error developers or posting ads with us Electrical Engineering Questions Tags Users Badges Unanswered Ask Question _ Electrical Engineering Stack Exchange is a question and answer site for electronics and electrical engineering professionals, students, and enthusiasts. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Quantization noise and Quantization error up vote 6 down vote favorite quantization error 1 What is the difference between the quantization noise and quantization error in ADC? I understood that the quantization error you get when you convert analog to digital and quantization noise when you convert from digital to analog. adc conversion share|improve this question asked Mar 20 '13 at 10:08 Sam 13314 add a comment| 4 Answers 4 active oldest votes up vote 4 down vote accepted The quantization noise is an abstraction, meant to represent the quantization error as quantization error in a signal (so it can be compared to other forms of noise. You consider the quantization noise as the difference between the (real) quantized signal and the (ideal) sampled one. Because of the loss of information due to quantization, a signal that is A/D and then D/A converted will show an additional noise due to quantization. A situation in which using quantization noise is useful is when determining the quantization depth (number of levels/bits) of a signal. By comparing the quantization noise to the other noise sources, it's possible to determine the maximum reasonable number of levels for the quantization, because additional bits would be absorbed by noise. This of course happens if the sampling rule is respected. share|improve this answer edited Mar 20 '13 at 10:24 answered Mar 20 '13 at 10:17 clabacchio♦ 11k42061 grazie per la risposta. –Sam Mar 20 '13 at 10:27 (English mode OFF) Prego :) (English mode ON) –clabacchio♦ Mar 20 '13 at 10:33 add a comment| up vote 3 down vote An very important aspect of quantization noise that has not yet been mentioned is that unlike some types of noise, it cannot in general be removed by filtering, but adding the right sort of noise to a signal before it is sampled can cause change the character of the quantization noise in such a way that much of it can be removed. Fo
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