spandana image processing and compression techniques (7840228)

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IMAGE PROCESSING AND COMPRESSION

TECHNIQUES

By T. Spandana094D1A0426

E.C.ESSIET,Vadiyampeta.

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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).

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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.

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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.

Terminology

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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 ”

Definition

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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.

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Digital image processing sequence

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Key Stages in Digital Image Processing

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Image acquisition

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Image Enhancement

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Image Restoration

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Morphological Processing

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Segmentation

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Object Recognition

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Representation & Description

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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Key Stages in Digital Image Processing:Colour Image Processing

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

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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.

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Goal of Image CompressionDigital 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.

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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

Encoder 0101100111... Decoder

Original Image Decoded ImageBitstream

Figure: Flow of compression

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Different Compression Techniques Mainly two types of data Compression techniques are

there.

Loss less Compression.

Lossy Compression.

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Figure : Data compression methods

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Lossless Compression

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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.

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Figure : Run-length encoding example

For instance,

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Huffman coding

Huffman coding assigns shorter codes to symbols that occur more

frequently and longer codes to those that occur less frequently.

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Figure Huffman coding

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Figure Final tree and code

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.

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Encoding

Figure Huffman encoding

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

characters. Figure shows the original and the encoded text.

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Decoding

Figure : Huffman decoding

The recipient has a very easy job in decoding the data it receives.

Figure shows how decoding takes place.

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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.

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The LZW Algorithm (Compression) Flow ChartSTART

W= NULL

IS EOF?

K=NEXT INPUT

IS WKFOUND

?

W=WK

OUTPUT INDEX OF W

ADD WK TO DICTIONARY

STOP

W=K

YES

NO

YES

NO

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The LZW Algorithm (Compression) ExampleInput string is

The Initial Dictionarycontains symbols like a, b, c, d with their index values as 1, 2, 3, 4 respectively.

Now the input string is read from left to right. Starting from a.

a b d c a d a c

a 1

b 2

c 3

d 4

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The LZW Algorithm (Compression) Example

W = NullK = aWK = aIn the dictionary.

a b d c a d a c

a 1

b 2

c 3

d 4

K

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K = b.WK = ab is not in the

dictionary.Add WK to

dictionaryOutput code for

a. Set W = b

a b d c a d a c

K

1

ab 5a 1

b 2

c 3

d 4

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K = dWK = bdNot in the

dictionary.Add bd to

dictionary.Output code bSet W = d

a b d c a d a c

1

K

2

ab 5a 1

b 2

c 3

d 4

bd 6

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K = aWK = da not in the

dictionary.Add it to

dictionary.Output code dSet W = a

a b d a b d a c

1

K

2 4

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

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K = bWK = ab It is in the

dictionary.

a b d a b d a c

1

K

2 4

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

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K = dWK = abd Not in the

dictionary.Add W to the

dictionary.Output code for

W.Set W = d

a b d a b d a c

1

K

2 4 5

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

abd 8

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• K = a• WK = da In the dictionary.

a b d a b d a c

1

K

2 4 5

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

abd 8


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The LZW Algorithm (Compression) Example• K = c• WK = dac Not in the

dictionary.• Add WK to the

dictionary.• Output code for

W.• Set W = c• No input left so

output code for W.

a b d a b d a c

1

K

2 4 5

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

abd 8

7

dac 9

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• The final output string is

1 2 4 5 7 3• Stop.

cadbadba

1

K

2 4 5

5ab

4d

3c

2b

1a

6bd

7da

8abd

7

9dac

3


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LZW Decompression Algorithm Flow Chart

START

Output K

IS EOF?

K=NEXT INPUT

ENTRY=DICTIONARY INDEX (K)

ADD W+ENTRY[0] TO DICTIONARY

STOP

W=ENTRY

K=INPUT

W=K

YES

NO

Output ENTRY

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The LZW Algorithm (Decompression) Example

• K = 1• Out put K (i.e. a)• W = K

1

K

2 4 5

4d

3c

2b

1a

7 3

a

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• K = 2• entry = b• Output entry• Add W + entry[0]

to dictionary• W = entry[0] (i.e.

b)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab


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• K = 4• entry = d• Output entry• Add W + entry[0]


d)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab

6bd

d


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• K = 5• entry = ab• Output entry• Add W + entry[0]


a)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab

6bd

d a b

7da


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• K = 7• entry = da• Output entry• Add W + entry[0]


d)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab

6bd

d a b

7da

d a

8abd


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• K = 3• entry = c• Output entry• Add W + entry[0]


c)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab

6bd

d a b

7da

d a

8abd

c

9dac


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Information loss is tolerable.

Many-to-1 mapping in compression eg. Quantization

LOSSY COMPRESSION METHODS

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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.

LOSSY COMPRESSION METHODS

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JPEG Compression

image, ~150KB

JPEG compressed, ~14KB

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Image compression – JPEG encoding

JPEG encoding is done in four steps:

1. Image preparation

2. Discrete Cosine Transform (DCT)

3. Quantization

4. Entropy Encoding

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Figure : JPEG grayscale example, 640 × 480 pixels

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Block diagram for JPEG encoder.

The JPEG compression process

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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.

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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.

.

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Reading the table

Zig zag recording

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Block diagram for JPEG Decoder.

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Examples of varying JPEG compression ratios

500KB image, minimum compression

40KB image, half compression

11KB image, max compression

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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.

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Spatial compressionThe 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.

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Figure MPEG frames

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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.

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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).

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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.

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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.

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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

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ApplicationsOne of the most common uses of DIP techniques: improve quality,

remove noise etc

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The Hubble TelescopeLaunched in 1990 the Hubble telescope can take images of very distant objectsHowever, an incorrect mirror made many of Hubble’s images uselessImage processing techniques were used to fix this

http://en.wikipedia.org/wiki/Image:Hst_sts82.jpg

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MedicineTake slice from MRI scan of canine heart, and find boundaries between types of tissues

Original MRI Image of a Dog Heart Edge Detection Image

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GIS Geographic Information Systems

Digital image processing techniques are used extensively to manipulate satellite imagery

Terrain classificationMeteorology

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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

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HCITry to make human computer interfaces

more natural

Face recognition

Gesture recognition

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Inserting Artificial Objects into a Scene

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Human Activity Recognition

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CONCLUSION: Image processing plays a vital role in many applications such

asFingerprint Identification SystemMedicineGeographic Information SystemsPrinted Circuit Board (PCB) inspectionhuman computer interfacesInserting Artificial Objects into a SceneHuman Activity Recognitionsoon…….

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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

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