What is Quantize error? : about Dithering

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What is Quantize error?

Quantize error

Quantization error occurs when an analog signal is converted into a digital form in a digital system. Analog signals have continuous values, but in a digital system, they are sampled at regular intervals to be approximated. This process results in the loss of the analog signal’s continuity, leaving it with discrete values.

Quantization error represents the difference between the original analog signal and its digital approximation. This error arises due to the loss of precision that occurs during the digitalization process. In other words, it’s the error introduced because an analog signal cannot be infinitely finely represented, leading to an approximation error.

The size of the quantization error generally correlates with the size of the quantization steps. The smaller the steps (or the finer the quantization), the smaller the quantization error. Generally, the human auditory system hardly detects quantization errors when playing back audio signals with 16-bit depth or more.

This quantization error often manifests as a form of noise, usually noticeable in the high-frequency range. This happens because the step-like quantization noise is perceived as high-frequency components. To minimize quantization error, one can use higher bit depths or apply signal processing techniques like dithering.

Dithering?

Quantize error

Dithering effectively disperses quantization error. It adds a small random noise signal, known as a dither signal, to the original signal. This helps to preserve the details of the signal and makes the quantization error less perceptible to human hearing or vision. Dithering is commonly used to alter the sonic characteristics of quantization error, generally resulting in more natural-sounding audio or visual presentations.

Dithering is typically applied when reducing the bit depth (e.g., 24-bit to 16-bit) and can be utilized in fields like music production, audio mixing, and video editing to improve quality. It requires precise calculations and algorithms, making the selection and application of appropriate dithering techniques crucial.