Quantization noise in the context of "Analog-to-digital converter"

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⭐ Core Definition: Quantization noise

In mathematics and digital signal processing, quantization is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. 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.

The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error, noise or distortion. A device or algorithmic function that performs quantization is called a quantizer. An analog-to-digital converter is an example of a quantizer.

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Quantization noise in the context of Analog signals

An analog signal (American English) or analogue signal (British and Commonwealth English) is any signal, typically a continuous-time signal, representing some other quantity, i.e., analogous to another quantity. For example, in an analog audio signal, the instantaneous signal voltage varies in a manner analogous to the pressure of the sound waves.

In contrast, a digital signal represents the original time-varying quantity as a sampled sequence of quantized numeric values, typically but not necessarily in the form of a binary value. Digital sampling imposes some bandwidth and dynamic range constraints on the representation and adds quantization noise.

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