Quantization error in the context of "Lossy compression"

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⭐ Core Definition: Quantization error

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 error in the context of Digital recording

In digital recording, an audio or video signal is converted into a stream of discrete numbers representing the changes over time in air pressure for audio, or chroma and luminance values for video. This number stream is saved to a storage device. To play back a digital recording, the numbers are retrieved and converted back into their original analog audio or video forms so that they can be heard or seen.

In a properly matched analog-to-digital converter (ADC) and digital-to-analog converter (DAC) pair, the analog signal is accurately reconstructed, within the constraints of the Nyquist–Shannon sampling theorem, which dictates the sampling rate and quantization error dependent on the audio or video bit depth. Because the signal is stored digitally, assuming proper error detection and correction, the recording is not degraded by copying, storage or interference.

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Quantization error in the context of Audio bit depth

In digital audio using pulse-code modulation (PCM), bit depth is the number of bits of information in each sample, and it directly corresponds to the resolution of each sample. Examples of bit depth include Compact Disc Digital Audio, which uses 16 bits per sample, and DVD-Audio and Blu-ray Disc, which can support up to 24 bits per sample.

In basic implementations, variations in bit depth primarily affect the noise level from quantization error—thus the signal-to-noise ratio (SNR) and dynamic range. However, techniques such as dithering, noise shaping, and oversampling can mitigate these effects without changing the bit depth. Bit depth also affects bit rate and file size.

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