Huffman Code Algorithm Assignment Help
Huffman coding is usually a process helpful to compress files with regard to transmission.
Makes use of statistical coding - more frequently utilized symbols have shorter code words.
Works well with regard to text as well as fax transmissions.
An application which utilizes several data structures.
Huffman coding:
Reducing strings over arbitrary alphabet ‘Σo’ to be able to strings on the fixed alphabet ‘Σc’ to be able to standardize device operations (‘|Σc|<|Σo|’).
Binary representation related to each operands and operators in machine instructions in computers.
It must be feasible so as to uniquely decode the code-string (string over Σc) to a source-string (string over Σo).
Not every code-string has to correspond to a source-string
Both the actual coding as well as decoding ought to become efficient.
Word: A finite non-empty string over an alphabet (‘Σo or Σc’).
Simple Coding Mechanism:
- code(ai) = a non-empty string over Σc, for ai∈Σo.
- code(a1a2…an) = code(a1).code(a2)…code(an).
Example:
Σ0 =(A, B, C, D, E) and Σc =(0, 1)
| A | B | C | D | E | Pefix-property | |
|---|---|---|---|---|---|---|
| 000 | 001 | 010 | 011 | 100 | Code(AAB)=000.000.001, easy to decode | yes |
| 0 | 01 | 001 | 0001 | 00001 | Code(C)=code(AB)=001, not always possible to uniquely decode | no |
| 1 | 01 | 001 | 0001 | 00001 | Prefix free code | yes |
| 1 | 10 | 100 | 1000 | 10000 | Not prefix free code | no |
Huffman Code Example:
| Symbol | A | B | C | D |
| Frequency | 13% | 25% | 50% | 12% |
| Original Encoding | 00 | 01 | 10 | 11 |
| 2 bits | 2 bits | 2 bits | 2 bits | |
| Huffman Encoding | 110 | 10 | 0 | 111 |
| 3 bits | 2 bits | 1 bit | 3 bits |
Expected size:
Original => 1/8x2 + 1/4x2 + 1/2x2 + 1/8x2 = 2 bits / symbol
Huffman => 1/8x3 + 1/4x2 + 1/2x1 + 1/8x3 = 1.75 bits / symbol
Huffman Coding Example:
| E = 01 I = 00 C = 10 A = 111 H = 110 | Huffman code |
Input:- ACE
Output:- (111)(10)(01) = 1111001
Huffman Coding (coding redundancy):
The variable-length coding technique:
Symbols tend to be encoded one at a time- (There's a one to one correspondence between source symbols as well as code words)
Optimal code (i.e., minimizes code word length per source symbol).
Huffman Code Algorithm Overview:
- Encoding.
- Compute frequency associated with symbols in file.
- Generate binary tree which represents best encoding.
- Make use of binary tree in order to encode compressed file- For every symbol, output path through root to leaf as well as Size of encoding = length of path.
- Save binary tree.
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