5.4 Uniform quantization and the 6 dB/bit approximationįor example, rounding a real number x.5.3 Neglecting the entropy constraint: Lloyd–Max quantization.5.1 Granular distortion and overload distortion.3.3 Mid-riser and mid-tread uniform quantizers.An analog-to-digital converter is an example of a quantizer. A device or algorithmic function that performs quantization is called a quantizer. The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error. Quantization also forms the core of essentially all lossy compression algorithms. 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. Rounding and truncation are typical examples of quantization processes. Quantization, in mathematics and digital signal processing, 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. 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. This example shows 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 simplest way to quantize a signal is to choose the digital amplitude value closest to the original analog amplitude.