What are the types of quantization?

Answer 1

Quantization is commonly divided into two main types:

Uniform Quantization – Uses equally spaced quantization levels across the entire range of values. It is simple to implement and is often used when the input signal has a relatively uniform distribution.

Non-Uniform Quantization – Uses unevenly spaced quantization levels, providing finer precision for smaller signal values and coarser precision for larger ones. This approach is commonly used in audio and speech processing to improve perceived quality.

In machine learning and AI, quantization is also categorized by precision, such as dynamic quantization, static quantization, and quantization-aware training (QAT), which reduce model size and improve inference speed while aiming to maintain accuracy.

Answer 2

Quantization can be broadly categorized into two main types: uniform and non-uniform quantization. Uniform quantization divides the input range into equal-sized intervals, making it simple and efficient for certain applications. Non-uniform quantization, on the other hand, allocates varying interval sizes, often used in scenarios where certain ranges of input values are more significant, such as in audio compression. Additionally, there are techniques like scalar quantization and vector quantization, which refer to the quantization of individual signals versus groups of signals, respectively.