quantisointia

Quantisointia is a Finnish-language term used to describe the process of quantization, the conversion of a continuous range of values into a finite set of representative values. It is commonly discussed in digital signal processing, communications, and data compression. The concept encompasses both the mathematical mapping and the practical consequences for signal quality and data size.

Quantization introduces distortion known as quantization error, which depends on the step size between representatives. A larger step reduces data size but increases error; a smaller step improves fidelity at the cost of larger code length. Quantizers are characterized by their step size, dynamic range, and whether the steps are uniform or nonuniform. Uniform quantizers use equal step sizes; nonuniform quantizers use varying steps, often via companding (such as μ-law or A-law) to allocate more levels to small magnitudes.

Key variants include scalar quantization, where each sample is quantized independently, and vector quantization, which quantizes blocks of samples. The Lloyd-Max algorithm can be used to design optimal scalar quantizers with respect to a given probability distribution.

Applications span audio and video encoding, sensor data compression, and digital communications. In computing and machine learning, quantisointia describes reducing model precision, typically to 8-bit or 4-bit representations, to decrease memory usage and speed up inference. Techniques include post-training quantization and quantization-aware training, each with trade-offs in accuracy and performance.

See also: Quantization; Dithering; Vector quantization; Fixed-point arithmetic; Quantization noise.