Quantization Error In Adc
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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 quantization error in pcm the reconstructed signal (red). The difference between the original signal and the
Quantization Error Example
reconstructed signal is the quantization error and, in this simple quantization scheme, is a deterministic function of the how to reduce quantization error 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 uniform quantization 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. The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error.
What Is Quantization
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 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 numb
In electronics, an analog-to-digital converter (ADC, A/D, A–D, or A-to-D) is a system that converts an analog signal, such as a sound picked up by a microphone or light entering a digital camera, into a digital signal. An ADC may also provide an isolated quantization step size formula measurement such as an electronic device that converts an input analog voltage or current quantization example to a digital number proportional to the magnitude of the voltage or current. Typically the digital output is a two's complement
Adc Converter
binary number that is proportional to the input, but there are other possibilities. There are several ADC architectures. Due to the complexity and the need for precisely matched components, all but the most specialized ADCs are https://en.wikipedia.org/wiki/Quantization_(signal_processing) implemented as integrated circuits (ICs). A digital-to-analog converter (DAC) performs the reverse function; it converts a digital signal into an analog signal. Contents 1 Explanation 1.1 Resolution 1.1.1 Quantization error 1.1.2 Dither 1.1.3 Non-linearity 1.2 Jitter 1.3 Sampling rate 1.3.1 Aliasing 1.3.2 Oversampling 1.4 Relative speed and precision 1.5 Sliding scale principle 2 Types 2.1 Direct-conversion 2.2 Successive approximation 2.3 Ramp-compare 2.4 Wilkinson 2.5 Integrating 2.6 Delta-encoded 2.7 Pipeline 2.8 Sigma-delta 2.9 https://en.wikipedia.org/wiki/Analog-to-digital_converter Time-interleaved 2.10 Intermediate FM stage 2.11 Other types 3 Commercial 4 Applications 4.1 Music recording 4.2 Digital signal processing 4.3 Scientific instruments 4.4 Rotary encoder 5 Electrical symbol 6 Testing 7 See also 8 Notes 9 References 10 Further reading 11 External links Explanation[edit] The conversion involves quantization of the input, so it necessarily introduces a small amount of error. Furthermore, instead of continuously performing the conversion, an ADC does the conversion periodically, sampling the input. The result is a sequence of digital values that have been converted from a continuous-time and continuous-amplitude analog signal to a discrete-time and discrete-amplitude digital signal. An ADC is defined by its bandwidth and its signal-to-noise ratio. The bandwidth of an ADC is characterized primarily by its sampling rate. The dynamic range of an ADC is influenced by many factors, including the resolution, linearity and accuracy (how well the quantization levels match the true analog signal), aliasing and jitter. The dynamic range of an ADC is often summarized in terms of its effective number of bits (ENOB), the number of bits of each measure it returns that are on average not noise. An ideal ADC has an ENOB equal to its resolution. ADCs are chosen to match the bandwidth and required signal-to-noise ratio of the signal to
Help Rules Groups Blogs What's New? Teardown Videos Datasheets Advanced Search Forum EDA Theory Elementary Electronic Questions How do I solve quantization errors in ADC system? + Post New Thread http://www.edaboard.com/thread40731.html Results 1 to 8 of 8 How do I solve quantization errors in ADC system? LinkBack LinkBack URL About LinkBacks Thread Tools Show Printable Version Download This Thread Subscribe to this Thread… Search Thread Advanced Search 22nd June 2005,16:25 #1 KrisUK Newbie level 4 Join Date May 2005 Posts 7 Helped 0 / 0 Points 1,398 Level 8 How do I solve quantization errors in ADC quantization error system? How do I work out quantization error in a ADC system? I looked around on different sites from a recommendation from another user and came to the conclusion it is the max voltage divided by the number of bits. Is this correct? Thank you. 22nd June 2005,16:25 22nd June 2005,16:52 #2 Kral Advanced Member level 4 Join Date Mar 2005 Location USA Posts quantization error in 1,326 Helped 278 / 278 Points 11,626 Level 25 Re: Quantization Error The weighting of the LSB is equal to the (Reference Voltage)/2^n, where n is the number of bits. The Quantization error = 1/2 LSB. If the ADC is bipolar (can represent both positive and negative values, then the LSB weighting is 2X the above value. The quantization error is still 1/2 LSB. The total error includes the quantization error plus scale factor (gain) error, non-linearity errors. Regards, Jon 22nd June 2005,17:22 #3 banh Advanced Member level 1 Join Date Dec 2004 Posts 458 Helped 17 / 17 Points 3,856 Level 14 Quantization Error quantization error/noise is the difference between the actual sampled value and the quantized value. 2 cases: if the the actual sampled value is between 2 quantized levels -> it will either be rounded off or truncated. rounding -> take the nearest quantized level. truncated -> take the level below it. hence: the error is - rounding off: - truncated where Q is the resolution. Last edited by BlackMamba; 27th August 2010 at 12:44. 22nd June 2005,17:22 22nd June 2005,18:42 #4 KrisUK Newbie level 4 Join Date May 2005 Posts 7 Help