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Pseudocode:


Pseudocode is an english like way to state an algorithm.

Simple problem:

1) Determine the sum, average and product of three numbers.

2) We can solve this by writing out steps

3) Then we can then convert these steps to code


Pseudo Code
Do forever [while (true is true)]
{ While () key not pressed)
 if (character received)
   ( getcharacter
     display pattern }
}
send 'Y'
while ( key pressed)
{ if (character received)
 getcharacter
  display pattern }
 } 
send 'N'
}


Optimal_encoding algorithm:


- The degree of compression (size of compressed data is)

- Where f( ch ) is the frequency of ‘ch’ and d( ch ) is the number of bits in its code


Example : Illustration of codeword generation in Huffman coding


Message
Code
probabilities
a1
0
P1 = 5 / 8
a2
110
P2  = 3 / 32
a3
110
P3 = 3 / 32
a4
1110
P4 = 1 / 32
a5
101
P5 = 1 / 8
a6
1111
P6 = 1 / 32


This figure show the Huffman coding here L = 6 having the probability for every message possibility noted at each node. 

MESSAGE
CODEWORD
PROBABILITY
a1
0
P1 = 5 / 8
a2
100
P2 = 3 / 32
a3
110
P3 = 3 /32
a4
1110
P4 = 1 / 32
a5
101
P5 = 1 / 8
a6
1111
P6 = 1 / 32


Huffman coding:


(5 / 8)1 + (3 / 32)3 + (3 / 32)3 + (1 / 32) - 4 + (1 / 8)3 + (1 / 32) - 4

= (29 / 16) bits / message

= (1.813) bits / message


Application of Huffman tree: 


1) To obtain an optimal set of codes for symbols

2) Which constitute messages. Each code is a binary string (combinations of 0's and 1's) which will be used for transmission of messages.

3) Fixed length coding

4) Variable length coding


Huffman Code Data Structures


Binary (Huffman) Tree

     - Represents Huffman code

     - Edge = code(‘0’ or ‘1’)

     - Leaf =  symbol

     - Path to leaf = encoding

     - Example: A = ( 110 ), B = ( 10 ), C= ( 0 )


Priority Queue

       - To efficiently build binary tree




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