Quantization Error In Fft
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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 the reconstructed signal (red). The difference between the original signal
Quantization Noise Power Formula
and the reconstructed signal is the quantization error and, in this simple quantization scheme, quantization error formula is a deterministic function of the input signal. Quantization, in mathematics and digital signal processing, is the process of mapping a quantization error in pcm large set 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
Round Off Noise In Iir Digital Filters
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 a quantizer. An analog-to-digital converter is an example of a quantizer. Contents 1 Basic properties of quantization 2 Basic types
Round Off Noise In Dsp
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 infinite. The input and output sets involved in quantization can be defined in a rather general way. For example, vector quantization is the application of quantization to multi-dimensional (vector-valued) input data.[1] Basic types of quantization[edit] 2-bit resolution with four levels of qu
Help Rules Groups Blogs What's New? Teardown Videos Datasheets Advanced Search Forum Digital Design and Embedded Programming Digital Signal Processing [SOLVED] SNR of a Quantized Signal using FFT + Post New Thread Results 1 trade off between round off and overflow noise to 10 of 10 SNR of a Quantized Signal using FFT LinkBack LinkBack URL
Quantization Of Filter Coefficients
About LinkBacks Thread Tools Show Printable Version Download This Thread Subscribe to this Thread… Search Thread Advanced Search 2nd September 2010,20:21 finite word length effects in digital filters #1 iffe Newbie level 4 Join Date Sep 2010 Posts 6 Helped 0 / 0 Points 436 Level 4 SNR of a Quantized Signal using FFT Hi all, I have a problem in the calculation https://en.wikipedia.org/wiki/Quantization_(signal_processing) of SNR. Sine wave is quantized and then I have calculated its SNR using FFT. The coherence condition with windowing as well is implemented to avoid leakage in the adjacent frequency bins. I am unable to calculate the SNR accurately with different resolution of quantizer (ADC). If I use a standard formula for quantization noise power (Q^2 / 12), the SNR corresponds with the theoretical formula of SNR (SNR = http://www.edaboard.com/thread186844.html 6.02*NoOfBits + 1.76) but when I try to use my own calculated noise power, the SNR does not corresponds with the theoretical formula with different resolution. I think, I am not calculating the noise power in the correct way. I have considered the noise spectrum starting from 2nd bin to input bandwidth. By the way I am using the formula of SNR = 10*log10(RMS_of_Signal/Mean_of_RSS_of_Noise) Please help. /Iffe 2nd September 2010,21:49 #2 JoannesPaulus Advanced Member level 3 Join Date Mar 2008 Location USA Posts 773 Helped 226 / 226 Points 7,235 Level 20 Re: SNR of a Quantized Signal using FFT The signal power should be calculated as: Code: sig_pwr=sum(X(bin_start:bin_end).*conj(X(bin_start:bin_end))); where X is the DFT of your signal, bin_start is the first bin of your DFT containing signal power, bin_end is the last bin containing signal power (for example, if you use a window that spreads the signal power across 7 bins and the center frequency is at, say, bin 191, bin_start=188; bin_end=194;). The noise power is calculated the same way but skipping the signal bins. Moreover, if you have a non-linear system, you should also skip the bins containing harmonic power (unless you are interested in SNDR). 2 members found this post helpful. 2nd September 2010,21:49 3rd September
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