Why Does Quantization Matter?

A scale equally balancing one large ball against six smaller ones
erhui1979/Getty Images

If you've ever listened to digital music–especially any kind of lossy audio format–then you've been exposed to mathematical quantization This behind-the-scenes digital signal processing is quite commonplace and most often an integral function of modern audio software or hardware (e.g. digital-to-analog converters). But quantization isn't limited to just audio. The term and its uses also apply to other fields, such as physics or digital imaging.

In a recording studio, microphones pick up analog music sound waves, which are then processed into a digital format. The signal can be sampled at 44,100 Hz and quantized with 8-, 16-, or 24-bit depth (and so forth). Higher bit depths provide more data, which enables more accurate conversion and reproduction of the original waveform.

Fundamentally, quantization is a complex process of rounding that involves some level of imprecision. Computers operate on ones and zeros, which is why analog-to-digital conversion is considered a close approximation and not an exact copy. When it comes to music, not only must the quantized signal maintain correct succession and amplitude of values, but the timing has to be accurate too. The process has to ensure that musical rhythm is maintained, with notes evenly distributed and set on the same beats (or fractions thereof). Otherwise, the audio may end up sounding off or strange to listening ears.

This concept of quantization can be visually observed with an image-editing program, such as Photoshop. When a large image is reduced in size, there is a loss of pixel information due to the mathematical process handling the task. The software performs the calculations and rounding to discard unwanted pixels while preserving the overall integrity, ratio, and context of the image – constrained proportions is as critical to photos as rhythm is to music. When zooming in and comparing the re-sized version of the photo to the original, edges and objects tend to appear somewhat coarse or jagged. This visual aspect of lossy compression similarly relates to types of digital audio files. More data and/or less compression results in higher overall quality.