2. Agenda
LZW Example
Huffman Example
Shanon Fano Example
Arithmetic Coding Example
Advantages for Every method

3. BILLGATES
1
B
B B BILLGATES
1 2
B I
BI I ILLGATES
1 2 3
B I L
BIL L LLGATES
1 2 3
B I L
BIL3 L LGATES
1 2 3 4
B I L G
BIL3G G GATES
1 2 3 4 5
B I L G A
BIL3G A A ATES

4. 1 2 3 4 5 6
B I L G A T
BIL3G AT T TES
1 2 3 4 5 6 7
B I L G A T E
BIL3G ATE E ES
1 2 3 4 5 6 7 8
B I L G A T E S
BIL3G ATES S S

5. Advantages and Disadvantages of
LZW
Advantage:
Is a lossless compression algorithm.Hence no information
is lost.
One need not pass the code table between the two
compression and the decompression.
Simple ,fast and good compression
Disadvantage:
What happens when the dictionary becomes too large
One approach is to throw the dictionary away when it
reaches a certain size
Useful only for a large amount of test data where
redundancy is high

6. Shannon–Fano
Algorithm
BILLGATES
Symbols B I L G A T E S
Probabilities 1/9 1/9 2/9 1/9 1/9 1/9 1/9 1/9

7. L B I G A T E S
A
L B I G A T E S
B

8. For a given list of symbols, develop a
corresponding list of probabilities or
frequency counts so that each
symbol’s relative frequency of
occurrence is known.
Sort the lists of symbols according to
frequency, with the most frequently
occurring symbols at the left and the
least common at the right.

9. Divide the list into two parts,
with the total frequency
counts of the left part being
as close to the total of the
right as possible.

10. C
L B I G A T S
E

11. D
L B I G A T S
E

12. E
L B I G A T S
E

13. 1
0
1
0 1
0
F 0
1
0 1 0 1
0
1
L B I G A T S
E

14. Symbols
L : 00
B: 01
I : 100
G: 101
A: 1100
T: 1101
E: 1110
S: 1111

15. 2Bit(1+1)+3Bit*(1+1)+4B
it*(1+1+1+1)/9
=2.888888Bits/symbol

16. What are the disadvantages of
shanon fano coding?
What is better shannon fano
and huffman coding?
huffman has a better
compression rate.

17. Huffman
Algorithm
BILLGATES
Symbols B I L G A T E S
Probabilities 1/9 1/9 2/9 1/9 1/9 1/9 1/9 1/9

18. G A T E S L
A B I
B I G A S
T E L
B

19.  Create a leaf node for each symbol and add it to frequency
of occurrence.
 While there is more than one node in the queue:
 Remove the two nodes of lowest probability or frequency
from the queue
 Prepend 0 and 1 respectively to any code already assigned
to these nodes
 Create a new internal node with these two nodes as children
and with probability equal to the sum of the two nodes'
probabilities.
 Add the new node to the queue.
 The remaining node is the root node and the tree is
complete.

20. B I G A T E L S

21. 1
1
0
0
1
0
0 0
1 1
0 1
1 0
B I G A T E L S

22. Symbols :
B: 00
I:01
G:100
A:101
T:1100
E:1101
L:1110
S:1111

23. 2Bit*(1+1)+3Bit*(1+1)+4Bit*(1+1+1+1)/9
=2.888888 Bits/symbol

24. Advantages and
disadvantages of
Huffman
This compression algorithm is mainly
efficient in compressing text or
program files. Images like they are
often used in prepress are better
handled by other compression
algorithms.

25. What are the advantages of Huffman
coding?
Algorithm is easy to
implement
Produce a lossless
compression of images

26. Adaptive Huffman Coding
(Dynamic Huffman
Coding)
 Advantage: Source is encoded in real time.
 Disadvantage: More chance for transmission error.

27. Arithmetic Coding

30. What are the Advantages of
arithmetic coding over Huffman
coding?
 1.the compression ratio is higher compared to huffman
coding.
 2.efficiency is greater comparatively.
 3.Redundancy is much reduced.

31. THANK YOU