Quantisaatio

Quantisaatio is a term that appears in various contexts, primarily relating to the process of converting continuous data into discrete values. In digital signal processing, quantization is a fundamental step in analog-to-digital conversion. It involves dividing the range of an analog signal into a finite number of intervals, and assigning each interval a specific digital value. The number of intervals, or quantization levels, determines the resolution and accuracy of the digital representation. Higher resolution means more levels and a more faithful representation of the original analog signal, but also requires more bits for storage and processing.

This concept also finds application in computer graphics, particularly in image processing. Quantization can be used to reduce the number of colors in an image, a process often referred to as color quantization. This is done to decrease file size or to adapt an image to a display that supports a limited color palette. Techniques like dithering can be employed to mitigate the visual artifacts that may arise from quantization.

In machine learning and neural networks, quantization refers to the process of reducing the precision of the numerical representations used in a model, such as weights and activations. This can significantly reduce the memory footprint and computational cost of deep learning models, making them more suitable for deployment on resource-constrained devices like mobile phones or embedded systems. While quantization can lead to some loss of accuracy, various techniques exist to minimize this impact. The specific meaning of quantisaatio can therefore be understood by examining the domain in which it is being applied.