data compression tech-cs.ppt

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Technical Seminar Presentation 2005

Sudeepta Mishra

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Sudeepta Mishra CS200117052

A Review of DataCompression Techniques

Presentedby

Sudeepta Mishra

Roll# CS200117052

At

NIST,Berhampur

Under the guidance of

Mr. Rowdra Ghatak


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Sudeepta Mishra

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Introduction

Data compression is the process of encoding data so

that it takes less storage space or less transmission time

than it would if it were not compressed.

Compression is possible because most real-world datais very redundant


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Different Compression Techniques Mainly two types of data Compression techniques are

there.

Loss less Compression.

Useful in spreadsheets, text, executable program

Compression. Lossy less Compression.

Compression of images, movies and sounds.


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Types of Loss less data Compression

Dictionary coders.

Zip (file format).

Lempel Ziv.

Entropy encoding. Huffman coding (simple entropy coding).

Run-length encoding.


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Dictionary-Based Compression

Dictionary-based algorithms do not encode singlesymbols as variable-length bit strings; theyencode variable-length strings of symbols assingle tokens.

The tokens form an index into a phrasedictionary.

If the tokens are smaller than the phrases theyreplace, compression occurs.


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Types of Dictionary

Static Dictionary.

Semi-Adaptive Dictionary.

Adaptive Dictionary.

Lempel Ziv algorithms belong to this category ofdictionary coders. The dictionary is being built in a

single pass, while at the same time encoding the data.

The decoder can build up the dictionary in the same

way as the encoder while decompressing the data.


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Using a English Dictionary the string:

A good example of how dictionary based compression works

Gives : 1/1 822/3 674/4 1343/60 928/75 550/32 173/46 421/2

Using the dictionary as lookup table, each word is coded as

x/y, where, x gives the page no. and y gives the number of

the word on that page. If the dictionary has 2,200 pages

with less than 256 entries per page: Therefore x requires 12

bits and y requires 8 bits, i.e., 20 bits per word (2.5 bytes per

word). Using ASCII coding the above string requires 48bytes, whereas our encoding requires only 20 (



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Lempel Ziv

It is a family of algorithms, stemming from the twoalgorithms proposed by Jacob Ziv and Abraham Lempel in

their landmark papers in 1977 and 1978.

LZ77 LZ78

LZR

LZHLZSS LZB

LZFG

LZC LZT LZMW

LZW

LZJ



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LZW Algorithm

It is An improved version of LZ78 algorithm.

Published by Terry Welch in 1984.

A dictionary that is indexed by codesis used. The

dictionary is assumed to be initialized with 256entries (indexed with ASCII codes 0 through 255)

representing the ASCII table.



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The LZW Algorithm (Compression)

W = NIL;while (there is input){

K = next symbol from input;

if (WK exists in the dictionary) {

W = WK;} else {

output (index(W));

add WK to the dictionary;

W = K;}

}



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The LZW Algorithm (Compression) Flow Chart

START

W= NULL

IS EOF?

K=NEXT INPUT

IS WK

FOUND?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) Example

Input string is

The InitialDictionary

contains symbolslike

a, b, c, d with theirindex values as 1, 2,3, 4 respectively.

Now the input stringis read from left toright. Starting froma.

a b d c a d a c

a 1

b 2

c 3

d 4



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W = Null

K = a

WK = a

In 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

dictionary

Output 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 = d

WK = bd

Not in the dictionary.

Add bd to dictionary.

Output code b

Set 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 = a

WK = da

not in the dictionary.

Add it to dictionary.

Output code d

Set W = a

a b d a b d a c

1K

2 4

ab 5a 1

b 2

c 3

d 4

bd 6

da 7



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K = b

WK = ab

It is in the dictionary.

a b d a b d a c

1K

2 4

ab 5a 1

b 2

c 3

d 4

bd 6

da 7



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K = d

WK = abd


Add W to the

dictionary.

Output code for W.

Set W = d

a b d a b d a c

1K

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

1K

2 4 5

ab 5a 1

b 2

c 3

d 4

bd 6

da 7

abd 8



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K = c

WK = dac


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

1K

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

1K

2 4 5

5ab

4d

3c

2b

1a

6bd

7da

8abd

7

9dac

3


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LZW Decompression Algorithmread a character k;output k;

w = k;

while ( read a character k )

/* k could be a character or a code. */

{ entry = dictionary entry for k;

output entry;

add w + entry[0] to dictionary;

w = entry; }


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LZW Decompression AlgorithmFlow ChartSTART

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] to

dictionary

W = entry[0] (i.e. 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] to

dictionary

W = entry[0] (i.e. a)

1

K

2 4 5

4d

3c

2b

1a

7 3

a b

5ab

6bd

d a b

7da

T h i l S i P t ti 2005


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K = 7

entry = da

Output entry

Add W + entry[0] to

dictionary

W = entry[0] (i.e. 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] to

dictionary

W = entry[0] (i.e. 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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Advantages

As LZW is adaptive dictionary coding no need totransfer the dictionary explicitly.

It will be created at the decoder side.

LZW can be made really fast, it grabs a fixed number

of bits from input, so bit parsing is very easy, and tablelook up is automatic.



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Problems with the encoder

What if we run out of space? Keep track of unused entries and use LRU (Last

Recently Used).

Monitor compression performance and flush

dictionary when performance is poor.



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Conclusion

LZW has given new dimensions for the development of

new compression techniques.

It has been implemented in well known compression

format like Acrobat PDF and many other types of

compression packages.

In combination with other compression techniques

many other different compression techniques are

developed like LZMS.



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REFERENCES

[1] http://www.bambooweb.com/articles/d/a/Data_Compression.html

[2] http://tuxtina.de/files/seminar/LempelZivReport.pdf

[3] BELL, T. C., CLEARY, J. G., AND WITTEN, I. H. TextCompression.Prentice Hall, Upper Sadle River, NJ, 1990.

[4] http://www.cs.cf.ac.uk/Dave/Multimedia/node214.html

[5] http://download.cdsoft.co.uk/tutorials/rlecompression/Run-Length Encoding (RLE) Tutorial.htm

[6] David Salomon, Data Compression The Complete Reference,Second Edition.Springer-Verlac, New York, Inc, 2001 reprint.

[7] http://www.programmersheaven.com/2/Art_Huffman_p1.htm

[8] http://www.programmersheaven.com/2/Art_Huffman_p2.htm[9] Khalid Sayood, Introduction to Data Compression Second

Edition, Chapter 5, pp. 137-157, Harcourt India Private Limited.



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S deepta Mishra CS200117052

Thank You

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