What is the Difference Between Uniform and Nonuniform Quantization?

The main difference between uniform and nonuniform quantization lies in the step sizes used to divide the signal amplitude range.

Uniform Quantization:

• The signal amplitude range is divided into equal intervals, with each interval assigned a quantization level.
• This type of quantization is simpler and more common, but it can result in some amount of quantization error.
• There are two types of uniform quantization: mid-rise and mid-tread, both of which are symmetric about the origin.

Nonuniform Quantization:

• The signal amplitude range is divided into unequal intervals, with each interval assigned a quantization level.
• This type of quantization reduces quantization error and allows for better capture of signal information by non-uniformly allocating bits and discretizing the signal.
• Nonuniform quantization is more complex and requires more processing power compared to uniform quantization.

In summary, uniform quantization is a simpler method that divides the signal amplitude range into equal intervals, while nonuniform quantization divides the range into unequal intervals, reducing quantization error and providing better signal representation. The choice between the two methods depends on the application requirements, such as the desired trade-off between complexity and performance.

Comparative Table: Uniform vs Nonuniform Quantization

Here is a table comparing the differences between uniform and nonuniform quantization:

Uniform quantization has an equal step size between quantization levels, resulting in a simpler mathematical formulation and direct mapping process. However, it has some quantization error, which can be lower in accuracy compared to nonuniform quantization.

Nonuniform quantization, on the other hand, has unequal step sizes between quantization levels, leading to a more complex mathematical formulation and non-linear mapping process. It can better capture the underlying distributions by learning to allocate more quantization levels to important value regions, resulting in higher accuracy.