Calculate The Maximum Quantization Error For The Signal
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Quantization Error Definition
“Maximum Quantization Error”? up vote 2 down vote favorite 1 I have an formula for this "Maximum Quantization Error" but i dont know what it is based in. Its just thrown in my study material without further explanation. It is defined as: $$Q = \dfrac {\Delta x}{2^{N+1}}$$ where $N$ is the number of bits used for quantization in a analog to digital conversion, and $\Delta x$ is, in portuguese "Faixa de Excursão do Sinal", I don't quantization error percentage know what would be the correct translation, but I bet on something like "Signal Excursion Band". I know, its a strange name. Can someone help me with this? What is this $\Delta x$? Sorry for my bad english, it isnt my native language. adc quantization share|improve this question edited Apr 29 '14 at 17:07 jojek♦ 6,70341444 asked Apr 29 '14 at 15:19 Diedre 20115 Evidently you are learning the basics. Speaking as a retired EE; real designs are a lot more complicated. The answer below is idealized for discussion. While not wrong, there are large confounding terms in physical implementation. –rrogers Dec 30 '15 at 14:42 add a comment| 1 Answer 1 active oldest votes up vote 4 down vote accepted When you quantize a signal, you introduce and error which can be defined as $$q[n] = x_q[n]-x[n]$$ where $q[n]$ is the quantization error, $x[n]$ the original signal, and $x_q[n]$ of the quantized signal. The maximum quantization error is simply $max(\left | q \right |)$, the absolute maximum of this error function. Dx in this definition seems to be the range of the input signal so we could rewrite this as $$Q = \frac{max(x)-min(x)}{2^{N+1}}$$ Let's look at a quick example. Let's assume you have a signal that's uniformly distributed between -1 and +1 and you want to quantize this with 3 bits. You hav
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How To Calculate Quantization Step Size
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Quantization Error Ppt
0 Don't like this video? Sign in to make your opinion count. Sign in 1 Loading... Loading... Transcript The interactive transcript could not be loaded. Loading... Loading... Rating is available when http://dsp.stackexchange.com/questions/15925/what-is-maximum-quantization-error the video has been rented. This feature is not available right now. Please try again later. Published on May 10, 2014 Category People & Blogs License Standard YouTube License Loading... Advertisement Autoplay When autoplay is enabled, a suggested video will automatically play next. Up next Analysis of Quantization Error - Duration: 15:04. Barry Van Veen 8,849 views 15:04 Quantization and Coding in A/D https://www.youtube.com/watch?v=cH9U89uQWog Conversion - Duration: 8:31. Barry Van Veen 10,242 views 8:31 GATE 2003 ECE Tolerance of 4 bit Weighted Resistor Analog to Digital Converter (ADC) - Duration: 6:35. GATE paper 1,676 views 6:35 DSP introduction - quantisation error (#008) - Duration: 8:45. Digital Signal Processing 3,153 views 8:45 242 videos Play all BSK - Digital CircuitsGATE paper 4 Methods to solve Aptitude Questions in smart way || Banking Careers - Duration: 14:58. Banking Careers 1,484,468 views 14:58 GATE 1994 ECE Match the following, ADCs with their conversion times - Duration: 5:49. GATE paper 1,422 views 5:49 DSP Lecture 23: Introduction to quantization - Duration: 1:03:51. Rich Radke 7,727 views 1:03:51 GATE 2014 ECE Six transistor SRAM memory cell - Duration: 9:21. GATE paper 4,655 views 9:21 Quantization Part 1: What is quantization - Duration: 4:03. Madhan Mohan 26,956 views 4:03 signal to quantization noise ratio derivation - Duration: 18:44. Signals Systems 454 views 18:44 Electronics 201: Analog/Digital Conversion - Duration: 19:53. humanHardDrive 34,912 views 19:53 GATE 2014 ECE Essential prime implicants of boolean function - Duration: 6:10. GATE paper 4,264 views 6:10 GATE 1995 ECE Match the
the original analog signal (green), the quantized signal (black dots), the signal reconstructed from the quantized signal (yellow) and the difference between the original signal and the reconstructed signal https://en.wikipedia.org/wiki/Quantization_(signal_processing) (red). 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, in mathematics and digital signal processing, is the process of mapping a large set of input values to a (countable) smaller set. Rounding and truncation are typical examples of quantization processes. Quantization is quantization error 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. 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 quantization error in 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 general to recover the exact input value when given only the output value). The set of possible input values may be infinitely large, and may possibly be continuous and therefore uncountable (such as the set of all real numbers, or all real numbers within some limited range). The set of possible output values may be finite or countably infin
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