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UNIT - IV

COMPRESSION TECHNIQUES

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Need for Data compression / Advantages

Huge amount of data is generated in text,

images, audio, speech and video.

Because of compression. Transmission data

rate is reduced

Storage becomes less due to compression.

Due to video compression it is possible to

store one complete movie on two cds. Transportation of the data is easier

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Drawbacks

Due to compression, some of the data

is lost

Compression and Decompression

increases complexity of the transmitter

and receiver

Coding time is increased due tocompression and decompression.

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Infn Source

Principles of Data

Compression

Compression Decompression

Source

Encoder

Destination

DecoderReceiverN/W

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Lossless Compression and Lossy

Compression

Lossless Compression

No part of the original information is lost

during compression

Lossy Compression

Some information lost during

compression.

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Comparison between Lossless

and Lossy Compression

Sr.

No.Lossless Compression Lossy Compression

1 No Information is lost Some information is lost

2 Completely reversible It is not reversible

3 Used for text and data Used for speech and video

4 Compression ratio is less High compression ratio

5 Compression isindependent of human

response

Compression depends uponsensitivity of human ear, eyes

etc.

6 Huffman coding, Run length

coding are examples

Transform coding, vector

quantization are examples

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Entropy Coding

Entropy Coding is based on entropy ofthe source

It assign codes to the source

alphabets according to probability oftheir occurrence.

It is Lossless Compression

Ex. Runlength coding, Prefix codingand Huffman Coding.

They are used for compression of thetext files.

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Runlength Coding

Used for the data generated byscanning the documents, fax machine,typewriters etc.

These information sources produce adata that contains large strings of1s/0s and zeros.

1111110000000011110000..

The above string coded usingRunlength coding as

1,6 ; 0,8 ; 1,4 ; 0,4

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Statistical Encoding Exploits the statistical properties of the

information For e.g the alphabets e,a,i have

higher probabilities of occurrence

compared to alphabets like q,t,z etc. Huffman Coding is an example of

Statistical encoding.

Here shortlength codewords areassigned to frequently occurringalphabets and larger length codewordsare assigned to rarely occurring

alphabets. This is called also as Entropy

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Source Encoding

Source Encoding is based onparticular property of the source.

Examples

Differential Encoding

Transform Encoding

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Differential Encoding

The difference between twosuccessive samples is encoded.

Normally the values of samples are

large but the difference between themis very small.

Hence less number of bits are rquired

to encode the difference. DPCM and Delta Modulation are

based on this principle.

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Transform Encoding

Transform Coding is much power fullcoding technique.

Consider an image consisting of NxN

pixel size. If these pixels are scanned

horizontally, then an electric signal

generated. The frquency of this signal is called

Spatial Frequency.

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Transform Encoding Contd Human eye much sensitive to low spatial

frequencies compared to high spatialfrequencies.

Hence such higher sensitive components are

redundant and they can be removed. This removal of high frequency components

provides compression, since the overall size

of the data is reduced.

Conversion of the image in spatial frequency

domain is obtained with the help of

DCT(Discrete Cosine Transform).

When Thresholding applied, some of the

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Text Compression

Text Compression should be strictly

lossless. Text Compression cannot be lossy.

Therefore lossless compression

techniques such as entropy coding isused.

Two types of statistical encoding

methods 1) Huffman Coding and Arithmetic Coding

Optimum set of codewords are derived for single

characters

2) Lampel ziv (LZ) algorithm

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The Coding used for text can be Static

or Dynamic

1) Static Coding The code words assigned to the alphabets

does not change during compression.

2) Dynamic Coding The code words are dynamically computed

during compression. The code word for a

particular alphabet or string does not

remain fixed throughout the compression.

Also called Adaptive Coding

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Sr.

No.

Static Coding Dynamic Coding

1

Codewords are fixed

throughout

compression

Codewords change

dynamically during

compression

2Statisticalcharacteristics of the

data are known

Statistical characteristicsof the data are not

known

3

Receiver knows the set

of codewords

Receiver dynamically

calculates thecodewords

4Ex. Static Huffman

Coding

Ex. Dynamic Huffman

Coding

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Static Huffman Coding

In Static Huffman Coding, the characterstring to be transmitted is analyzed.

The frequency of occurrence of each

character is determined. The variable length codewords are then

assigned to each character.

This coding operation creates anunbalanced tree. It is also called

Huffman coding tree.

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Arithmetic Coding