quantilization

1. Jai Mahakali shikshan Santha’s
Agnihotri College Of Engineering
Naagthana Road, Sindhi (Meghe) Wardha
Rashtrasant Tukadoji Maharaj Nagpur University
(2023-2024)
Submitted by partial fulfilment of requirement for the degree
BACHELOR OF TECHNOLOGY
(Electronics & communication Engineering)
Study REPORT
ON
“QUANTILIZATION”
Submitted By
Priya Kokate
Submitted To

2. Quantization

3. Quantization
 Quantization is a process to convert the continuous analog signal to the series
of discrete values. A quantizer is a device known to perform the quantization
process.
 The function of quantizer is to represent each level to the fixed discrete finite
set of values.
 The signals during transmission over long distances suffer from noise and
interference.
 To overcome this, the quantization process creates a signal that
is approximately equal to the message signal.
 It selects a quantized signal mq(t) with values nearest to the original analog
signal m(t).
 The quantization process selects a value and rounds off these values to the
nearest stabilized value.
 The quantized signal mq(t) can get easily separable from the additive noise.

4. Quantization
► In order to process the sampled signal digitally, the sample values have to be
quantized to a finite number of levels, and each value can then be represented
by a string of bits.
► To quantize a sample value is to round it to the nearest point among a finite set
of permissible values.
► Therefore, a distortion will inevitably occur. This is called quantization noise (or
error).

5. Types Of Quantization
There are two types of Quantization, uniform Quantization and non-uniform
Quantization.
Uniform Quantization
► As the name implies, the quantized levels in the uniform quantization process
are equally spaced.
► The uniform quantization is further categorized as mid-rise type uniform
quantization and mid-tread type uniform quantization.
► Both the uniform quantization processes are symmetric about the respective
axis.
1. d to a finite number of levels, and each value can then be represented by a string of bits.
• To quantize a sample value is to round it to the nearest point among a finite set of permissible values.
• Therefore, a distortion will inevitably occur. This is called quantization noise (or error).

6. 1. Mid-rise type uniform Quantization
a finite number of levels, and each value can then be represented by a string of bits.
 Rise refers to the rising part.
 The origin of the discrete quantized signal lies in the middle of the rising part of
the stair like graph, as shown below :
• :Luantize a sample value io round it to the nearest point among a finite set of
permissible values.
• Therefore, a distortion will inevitably occur. This is called quantization noise (or error).
• to process the sampled signal digitally, the sample values have to be quantize

7. 2. Mid-tread type uniform Quantization
 Tread refers to the flat part.
 The origin of the discrete quantized signal lies in the middle of the tread part of
the stair like graph, as shown below:

8. Advantages of Uniform Quantization
The advantages of Uniform Quantization are as follows:
1. High approximation
The fixed size of the quantization levels of the uniform quantization increases the
accuracy of the discrete signals. Hence, such a type of Quantization has a high
approximation compared to non-uniform Quantization.
2. Easy and simple to implement
Uniform quantization process is easy and simple to implement due to the uniform
gap between the adjacent quantization levels.

9. Non-uniform Quantization
 The quantized levels in the non-uniform quantization process are unequally
spaced.
 The relation between such quantization is generally logarithmic due to non-
linear nature of the signal.
Advantages of non-uniform Quantization
The advantages of non-uniform Quantization are as follows:
1. High Signal to Noise Ratio (SNR)
SNR of the non-uniform quantization is higher than the uniform quantization. It is
because the non-uniform level requires large number of quantized levels are more
expanded.
2. Low quantizer noise
The quantizer noise of the non-uniform quantization is low than the uniform
quantization. It is because the RMS value of the quantized noise power is
proportional to the sampled value of the signal.

10. Companding
 Companding is a type of non-uniform quantization and is used to increase the
strength of the weak signals.
 It reduces the data rate of the input signal by varying the gap between the two
adjacent quantization levels.
 The unequal quantization level makes it similar to the non-uniform quantization
process.
 Companding is created from the combination of two words, compression and
expanding.
 The signal is passed through the compressor at the transmitting end while it is
passed through the expander at the receiving end.
 The compressor compresses the signal and improves the quality of
transmission. But, it introduces distortion in the signal.
 The expander is used at the receiving end to undo the distortion introduced by
the compressor.
 The inverse distortions of the two processes (compression and expanding) help
in generating the output signal without distortion.

11. Quantization Errors
 The difference in the input value and the quantized value of the signal is known
as the quantization error.
 The quantized signal (mq(t)) is the approximation signal of the message signal
(m(t)). The difference between the message signal and the quantized signal is
termed as quantized noise. It is given by:
 Qe = m(t) - mq(t)
 It depicts that the output of the quantization process or the received signal is
not a perfect copy of the message signal.

12. Advantages of Quantization
The advantages of quantization are as follows:
► It reduces the number of bits used to represent a signal. It further results in the
bandwidth reduction, which has various other advantages, such as low cost,
increased reliability, and effective transmission.
► It enables uniform precision. Precision refers to the accuracy or exactness
required in a method.
► It represents the sampled value of the signal into the finite number of levels,
which helps in converting an input analog signal to the digital signal.

13. Applications of Quantization
Apart from digital communication, quantization process is also used in various fields, such as
signal processing, control systems, image processing, science, and linguistics.
1. Digital Signal Processing
In digital signal processing, quantization maps the large set of the input values to the small set of
the output values with a finite number of elements. The device that performs the quantization
function is known as quantizer.
2. Image processing
In image processing, quantization reduces the number of discrete value in the signal. We can also
say that it compresses the input signal and produces the compressed signal at the output. For
example, reducing the number of colors required to represent a digital image.
3. Physics
Quantization in science is related to the electromagnetic wave, quantum, or photons. It represents
the transitions from the classic mechanics to quantum mechanics. Quantization is used in various
theories of physics, such as nuclear physics and atomic physics.

14. Thank You