**Rounding Example**

As an example, rounding a real number to the nearest integer value forms a very basic type of quantizer – a *uniform* one. A typical (*mid-tread*) uniform quantizer with a quantization *step size* equal to some value can be expressed as

- ,

where the function ( ) is the sign function (also known as the *signum* function). For simple rounding to the nearest integer, the step size is equal to 1. With or with equal to any other integer value, this quantizer has real-valued inputs and integer-valued outputs, although this property is not a necessity – a quantizer may also have an integer input domain and may also have non-integer output values. The essential property of a quantizer is that it has a countable set of possible output values that has fewer members than the set of possible input values. The members of the set of output values may have integer, rational, or real values (or even other possible values as well, in general – such as vector values or complex numbers).

When the quantization step size is small (relative to the variation in the signal being measured), it is relatively simple to show that the mean squared error produced by such a rounding operation will be approximately .

Because the set of possible output values of a quantizer is countable, any quantizer can be decomposed into two distinct stages, which can be referred to as the *classification* stage (or *forward quantization* stage) and the *reconstruction* stage (or *inverse quantization* stage), where the classification stage maps the input value to an integer *quantization index* and the reconstruction stage maps the index to the *reconstruction value* that is the output approximation of the input value. For the example uniform quantizer described above, the forward quantization stage can be expressed as

- ,

and the reconstruction stage for this example quantizer is simply .

This decomposition is useful for the design and analysis of quantization behavior, and it illustrates how the quantized data can be communicated over a communication channel – a *source encoder* can perform the forward quantization stage and send the index information through a communication channel (possibly applying entropy coding techniques to the quantization indices), and a *decoder* can perform the reconstruction stage to produce the output approximation of the original input data. In more elaborate quantization designs, both the forward and inverse quantization stages may be substantially more complex. In general, the forward quantization stage may use any function that maps the input data to the integer space of the quantization index data, and the inverse quantization stage can conceptually (or literally) be a table look-up operation to map each quantization index to a corresponding reconstruction value. This two-stage decomposition applies equally well to vector as well as scalar quantizers.

Read more about this topic: Quantization (signal Processing)

### Famous quotes containing the word rounding:

“The past absconds

With our fortunes just as we were *rounding* a major

Bend in the swollen river; not to see ahead

Becomes the only predicament when what

Might be sunken there is mentioned only

In crabbed allusions but will be back tomorrow.”

—John Ashbery (b. 1927)

“I look for the new Teacher that shall follow so far those shining laws that he shall see them come full circle; shall see their *rounding* complete grace; shall see the world to be the mirror of the soul; shall see the identity of the law of gravitation with purity of the heart; and shall show that the Ought, that Duty, is one thing with Science, with Beauty, and with Joy.”

—Ralph Waldo Emerson (1803–1882)