Spandana image processing and compression techniques (7840228)

IMAGE PROCESSING AND
COMPRESSION
TECHNIQUES

By T. Spandana
094D1A0426
E.C.E
SSIET,Vadiyampeta.
1

Objective
 The objective of image processing is to sharpen, minimize the

effect of degradation, reduce the amount of memory to store the image

information (image compression).

2

Introduction
Image processing pertains to the alteration and analysis

of pictorial information.

Common case of image processing is the adjustment

of brightness and contrast controls on a television set by

doing this we enhance the image until its subjective

appearing to us is most appealing.
3

Terminology
What is the Digital Image Processing?

 Digital:
Operating by the use of discrete signals to represent data in the
form of numbers.
 Image:
An image (from Latin imago) or picture is an artefact, usually
two-dimensional.
 Processing:
To perform operations on data according to programmed
instructions.

4

Definition
Thus the definition of the digital image processing may be given
as:

“Digital image processing is the use of
computer algorithms to perform image
processing on digital images ”

5

Digital image:
 An image may be defined as a two-
dimensional function, f(x, y).

 A digital image is composed of a finite
number of elements.

 These elements are referred to as picture
elements, image elements, pels, and pixels.

6

Digital image processing sequence

7

Key Stages in Digital Image Processing
Image Morphological
Restoration Processing

Image
Segmentation
Enhancement

Image Object
Acquisition Recognition

Representation
Problem Domain
& Description
Colour Image Image
Processing Compression
8

Key Stages in Digital Image Processing:
Image acquisition
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
9

Image Enhancement
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
10

Image Restoration
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
11

Morphological Processing
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
12

Segmentation
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
13

Object Recognition
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
14

Representation & Description
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
15

Image Compression
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
16

Colour Image Processing
Image Morphological

Image
Segmentation
Enhancement

Image Object

Representation
Problem Domain
& Description
Colour Image Image
17

Image Compression
 Image compression addresses the problem of reducing the

amount of data required to represent a digital image.

 It is the sub areas of image processing.

 The underlying basis of the reduction process is the removal of

the redundant data.

18

Goal of Image Compression
 Digital images require huge amounts of space for storage and large
bandwidths for transmission.

 The goal of image compression is to reduce the amount of data
required to represent a digital image.

19

The Flow of Image Compression

To store the image into bit-stream as compact as possible
and to display the decoded image in the monitor as exact
as possible
Original Image Decoded Image
Bitstream

Encoder 0101100111... Decoder

Figure: Flow of compression

20

Different Compression Techniques

Mainly two types of data Compression techniques are

there.

 Loss less Compression.

 Lossy Compression.

21

Figure : Data compression methods

22

Lossless Compression

24

Run-length:
•Simplest method of compression.
• It can be used to compress data made of any combination of symbols.
•It does not need to know the frequency of occurrence of symbols and
can be very efficient if data is represented as 0s and 1s.
•The general idea behind this method is to replace consecutive
repeating occurrences of a symbol by one occurrence of the symbol
followed by the number of occurrences.

26

For instance,

Figure : Run-length encoding example

27

Huffman coding
Huffman coding assigns shorter codes to symbols that occur more
frequently and longer codes to those that occur less frequently.

29

Figure Huffman coding
30

A character’s code is found by starting at the root and following the
branches that lead to that character. The code itself is the bit value of each
branch on the path, taken in sequence.

Figure Final tree and code

31

Encoding
Let us see how to encode text using the code for our five

characters. Figure shows the original and the encoded text.

Figure Huffman encoding
32

Decoding
The recipient has a very easy job in decoding the data it receives.
Figure shows how decoding takes place.

Figure : Huffman decoding
33

Lempel Ziv encoding
Lempel Ziv (LZ) encoding is an example of a category of algorithms

called dictionary-based encoding.

The idea is to create a dictionary (a table) of strings used during the

communication session.

35

The LZW Algorithm (Compression)
Flow Chart START

W= NULL

YES
IS EOF STOP
? N
O
K=NEXT INPUT

YES
IS WK
W=WK
FOUND?
N
O
OUTPUT INDEX OF W

ADD WK TO DICTIONARY

W=K

36

Example
 Input string is
a b d c a d a c
 The Initial Dictionary
contains symbols like
a, b, c, d with their
index values as 1, 2, 3,
4 respectively.
a 1
 Now the input string b 2
is read from left to c 3
right. Starting from a.
d 4
37

Example
 W = Null a b d c a d a c
 K=a
 WK = a
K
In the dictionary.

a 1
b 2
c 3
d 4

38

Example
 K = b. a b d c a d a c
 WK = ab
is not in the dictionary.
K
 Add WK to
dictionary 1
 Output code for a.
a 1 ab 5
 Set W = b
b 2
c 3
d 4

39

Example
 K=d a b d c a d a c
 WK = bd
Not in the dictionary.
K
Add bd to dictionary.
 Output code b 1 2
 Set W = d
a 1 ab 5
b 2 bd 6
c 3
d 4

40

Example
 K=a a b d a b d a c
 WK = da
not in the dictionary. K
 Add it to dictionary.
 Output code d
1 2 4
 Set W = a
a 1 ab 5
b 2 bd 6
c 3 da 7
d 4

41

Example
 K=b a b d a b d a c
 WK = ab
It is in the dictionary. K

1 2 4

a 1 ab 5
b 2 bd 6
c 3 da 7
d 4

42

Example
 K=d a b d a b d a c
 WK = abd
Not in the dictionary. K
 Add W to the
dictionary. 1 2 4 5
 Output code for W.
a 1 ab 5
 Set W = d
b 2 bd 6
c 3 da 7
d 4 abd 8

43

Example
• K=a a b d a b d a c
• WK = da
In the dictionary. K

1 2 4 5

a 1 ab 5
b 2 bd 6
c 3 da 7
d 4 abd 8

44

The LZW Algorithm (Compression) Example

• K=c a b d a b d a c
• WK = dac
Not in the dictionary. K
• Add WK to the
dictionary. 1 2 4 5 7
• Output code for W.
a 1 ab 5 dac 9
• Set W = c
b 2 bd 6
• No input left so
output code for W. c 3 da 7
d 4 abd 8

45

Example
• The final output a b d a b d a c
string is
124573
K
• Stop.
1 2 4 5 7 3
a 1 ab 5 dac 9
b 2 bd 6
c 3 da 7
d 4 abd 8

46

LZW Decompression Algorithm Flow Chart
START

K=INPUT

Output K

W=K

YES
IS EOF STOP
?
NO

K=NEXT INPUT

ENTRY=DICTIONARY INDEX (K)

Output ENTRY

ADD W+ENTRY[0] TO DICTIONARY

W=ENTRY
47

The LZW Algorithm (Decompression) Example

1 2 4 5 7 3
• K=1
• Out put K (i.e. a)
K
• W=K
a

a 1
b 2
c 3
d 4

48


1 2 4 5 7 3
• K=2
• entry = b
K
• Output entry
• Add W + entry[0] to a b
dictionary
• W = entry[0] (i.e. b)
a 1 ab 5
b 2
c 3
d 4

49

1 2 4 5 7 3
• K=4
• entry = d
K
• Output entry
• Add W + entry[0] to a b d
dictionary
• W = entry[0] (i.e. d)
a 1 ab 5
b 2 bd 6
c 3
d 4

50

1 2 4 5 7 3
• K=5
• entry = ab
K
• Output entry
• Add W + entry[0] to a b d a b
dictionary
• W = entry[0] (i.e. a)
a 1 ab 5
b 2 bd 6
c 3 da 7
d 4

51

1 2 4 5 7 3
• K=7
• entry = da
K
• Output entry
• Add W + entry[0] to a b d a b d a
dictionary
• W = entry[0] (i.e. d)
a 1 ab 5
b 2 bd 6
c 3 da 7
d 4 abd 8

52

1 2 4 5 7 3
• K=3
• entry = c
K
• Output entry
• Add W + entry[0] to a b d a b d a c
dictionary
• W = entry[0] (i.e. c)
a 1 ab 5 dac 9
b 2 bd 6
c 3 da 7
d 4 abd 8

53

LOSSY COMPRESSION
METHODS

Information loss is tolerable.

Many-to-1 mapping in compression eg. Quantization

55

LOSSY COMPRESSION
METHODS
Several methods have been developed using lossy compression
techniques.
JPEG (Joint Photographic Experts Group) encoding is used to
compress pictures and graphics.
 MPEG (Moving Picture Experts Group) encoding is used to compress
video.
MP3 (MPEG audio layer 3) for audio compression.

56

JPEG Compression

image, ~150KB JPEG compressed, ~14KB

58

Image compression – JPEG encoding

JPEG encoding is done in four steps:

1. Image preparation

2. Discrete Cosine Transform (DCT)

3. Quantization

4. Entropy Encoding

59

Figure : JPEG grayscale example, 640 × 480 pixels

60

Block diagram for JPEG encoder.

The JPEG compression process

61

Discrete cosine transform (DCT)
In this step, each block of 64 pixels goes through a transformation
called the discrete cosine transform (DCT).
The transformation changes the 64 values so that the relative
relationships between pixels are kept but the redundancies are
revealed.
P(x, y) defines one value in the block, while T(m, n) defines the
value in the transformed block.

62

Quantization

After the T table is created, the values are quantized to reduce the

number of bits needed for encoding.

 Quantization divides the number of bits by a constant and then drops

the fraction. This reduces the required number of bits even more.

.

64

Zig zag recording

Reading the table

65

Block diagram for JPEG Decoder.

66

Examples of varying JPEG compression
ratios

500KB image, minimum 40KB image, half 11KB image, max
compression compression compression

67

Video compression – MPEG encoding
The Moving Picture Experts Group (MPEG) method is used to
compress video.
Principle, a motion picture is a rapid sequence of a set of frames in
which each frame is a picture.
Compressing video, then, means spatially compressing each frame
and temporally compressing a set of frames.

69

Spatial compression
The spatial compression of each frame is done with JPEG, or a
modification of it. Each frame is a picture that can be independently
compressed.

Temporal compression
In temporal compression, redundant frames are removed. When we
watch television, for example, we receive 30 frames per second.
However, most of the consecutive frames are almost the same.

70

Figure MPEG frames

71

Audio compression
Audio compression can be used for speech or music. For speech

we need to compress a 64 kHz digitized signal, while for music we

need to compress a 1.411 MHz signal.

Two categories of techniques are used for audio compression:

predictive encoding and perceptual encoding.

73

Predictive encoding

In predictive encoding, the differences between samples are

encoded instead of encoding all the sampled values.

 This type of compression is normally used for speech. Several

standards have been defined such as GSM (13 kbps), G.729 (8 kbps),

and G.723.3 (6.4 or 5.3 kbps).

74

Perceptual encoding: MP3

The most common compression technique used to create CD-

quality audio is based on the perceptual encoding technique.

This type of audio needs at least 1.411 Mbps, which cannot be sent

over the Internet without compression. MP3 (MPEG audio layer 3)

uses this technique.

75

Advantages:
 In medicine
 Vision Systems are flexible, inexpensive, powerful tools that can
be used with ease.
 In Space Exploration the robots play vital role which in turn use
the image processing techniques
 Astronomical Observations.
 Used in Remote Sensing, Geological Surveys for detecting
mineral resources etc.
 Also used for character recognizing techniques, inspection for
abnormalities in industries.

76

Disadvantages:
 A Person needs knowledge in many fields to develop an application /
or part of an application using image processing.

 Calculations and computations are difficult and complicated so needs
an expert in the field related. Hence it’s unsuitable and unbeneficial to
ordinary programmers with mediocre knowledge

77

Applications
One of the most common uses of DIP techniques: improve quality,
remove noise etc

78

The Hubble Telescope
Launched in 1990 the Hubble
telescope can take images of
very distant objects
However, an incorrect mirror
made many of Hubble’s
images useless
Image processing
techniques were
used to fix this

79

Medicine
Take slice from MRI scan of canine heart, and find boundaries
between types of tissues

Original MRI Image of a Dog Heart Edge Detection Image

80

GIS
 Geographic Information Systems
 Digital image processing techniques are used extensively to
manipulate satellite imagery
 Terrain classification
 Meteorology

81

PCB Inspection
 Printed Circuit Board (PCB) inspection
 Machine inspection is used to determine that all components are
present and that all solder joints are acceptable
 Both conventional imaging and x-ray imaging are used

82

HCI
Try to make human computer interfaces

more natural

 Face recognition

 Gesture recognition

83

Inserting Artificial Objects into a Scene

84

Human Activity Recognition

85

CONCLUSION:
Image processing plays a vital role in many applications such
as
Fingerprint Identification System
Medicine
Geographic Information Systems
Printed Circuit Board (PCB) inspection
human computer interfaces
Inserting Artificial Objects into a Scene
Human Activity Recognition
soon…….

86

Future scope
 The digital Image Processing is now finding wide range of uses in
different modern applications. Few of them (in which researcher are
trying developments)include:

 Expert Systems

 Parallel Processing

 Neural Networks

87

Spandana image processing and compression techniques (7840228)

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