Post on 16-Dec-2015
Greedy Algorithms(Huffman Coding)
Slide 1
Huffman Coding
• A technique to compress data effectively – Usually between 20%-90% compression
• Lossless compression– No information is lost– When decompress, you get the original file
Slide 2
Original file
Compressed file
Huffman coding
Huffman Coding: Applications
• Saving space– Store compressed files instead of original files
• Transmitting files or data– Send compressed data to save transmission time and power
• Encryption and decryption– Cannot read the compressed file without knowing the “key”
Slide 3
Original file
Compressed file
Huffman coding
Main Idea: Frequency-Based Encoding
• Assume in this file only 6 characters appear
– E, A, C, T, K, N
• The frequencies are: Character Frequency
E 10,000
A 4,000
C 300
T 200
K 100
N 100• Option I (No Compression)
– Each character = 1 Byte (8 bits)– Total file size = 14,700 * 8 = 117,600 bits
• Option 2 (Fixed size compression)– We have 6 characters, so we need
3 bits to encode them– Total file size = 14,700 * 3 = 44,100 bits
Character Fixed Encoding
E 000
A 001
C 010
T 100
K 110
N 111
Main Idea: Frequency-Based Encoding(Cont’d)
• Assume in this file only 6 characters appear
– E, A, C, T, K, N
• The frequencies are: Character Frequency
E 10,000
A 4,000
C 300
T 200
K 100
N 100• Option 3 (Huffman compression)– Variable-length compression– Assign shorter codes to more frequent characters and
longer codes to less frequent characters
– Total file size:
Char.
HuffmanEncoding
E 0
A 10
C 110
T 1110
K 11110
N 11111
(10,000 x 1) + (4,000 x 2) + (300 x 3) + (200 x 4) + (100 x 5) + (100 x 5) = 20,700 bits
Huffman Coding
• A variable-length coding for characters– More frequent characters shorter codes
– Less frequent characters longer codes
• It is not like ASCII coding where all characters have the same coding length (8 bits)
• Two main questions – How to assign codes (Encoding process)?
– How to decode (from the compressed file, generate the original file) (Decoding process)?
Slide 6
Decoding for fixed-length codes is much easier
Slide 7
Character Fixed-length Encoding
E 000
A 001
C 010
T 100
K 110
N 111
010001100110111000
010 001 100 110 111 000
Divide into 3’s
C A T K N E
Decode
Decoding for variable-length codes is not that easy…
Slide 8
Character Variable-length Encoding
E 0
A 00
C 001
… …
… …
… …
000001
It means what???
EEEC EAC AEC
Huffman encoding guarantees to avoid this uncertainty …Always have a single decoding
Huffman Algorithm
• Step 1: Get Frequencies– Scan the file to be compressed and count the occurrence of
each character
– Sort the characters based on their frequency
• Step 2: Build Tree & Assign Codes– Build a Huffman-code tree (binary tree)
– Traverse the tree to assign codes
• Step 3: Encode (Compress)– Scan the file again and replace each character by its code
• Step 4: Decode (Decompress)
– Huffman tree is the key to decompress the file
Slide 9
Step 1: Get Frequencies
Slide 10
Eerie eyes seen near lake.Input File:
Step 2: Build Huffman Tree & Assign Codes
• It is a binary tree in which each character is a leaf node– Initially each node is a separate root
• At each step– Select two roots with smallest frequency and connect them to
a new parent (Break ties arbitrary) [The greedy choice]
– The parent will get the sum of frequencies of the two child nodes
• Repeat until you have one root
Slide 11
Example
Slide 12
Each char. has a leaf node with its frequency
Find the smallest two frequencies…Replace them with their parent
Slide 13
Slide 14
Find the smallest two frequencies…Replace them with their parent
Slide 15
Find the smallest two frequencies…Replace them with their parent
Slide 16
Find the smallest two frequencies…Replace them with their parent
Slide 17
Find the smallest two frequencies…Replace them with their parent
Slide 18
Find the smallest two frequencies…Replace them with their parent
Slide 19
Find the smallest two frequencies…Replace them with their parent
Slide 20
Find the smallest two frequencies…Replace them with their parent
Slide 21
Find the smallest two frequencies…Replace them with their parent
Slide 22
Find the smallest two frequencies…Replace them with their parent
Slide 23
Find the smallest two frequencies…Replace them with their parent
Slide 24
Now we have a single root…This is the Huffman Tree
Lets Analyze Huffman Tree
Slide 25
• All characters are at the leaf nodes
• The number at the root = # of characters in the file
• High-frequency chars (E.g., “e”) are near the root
• Low-frequency chars are far from the root
Lets Assign Codes
• Traverse the tree
– Any left edge add label 0
– As right edge add label 1
• The code for each character is its root-to-leaf label sequence
Slide 26
Slide 27
01
0
0
0
0
0
0 0
1
1
11
1
1
1
10
001 1
• Traverse the tree
– Any left edge add label 0
– As right edge add label 1
• The code for each character is its root-to-leaf label sequence
Lets Assign Codes
Slide 28
• Traverse the tree
– Any left edge add label 0
– As right edge add label 1
• The code for each character is its root-to-leaf label sequence
Lets Assign Codes
Coding Table
Huffman Algorithm
• Step 1: Get Frequencies– Scan the file to be compressed and count the occurrence of
each character
– Sort the characters based on their frequency
• Step 2: Build Tree & Assign Codes– Build a Huffman-code tree (binary tree)
– Traverse the tree to assign codes
• Step 3: Encode (Compress)– Scan the file again and replace each character by its code
• Step 4: Decode (Decompress)
– Huffman tree is the key to decompess the file
Slide 29
Generate the encoded file
Step 3: Encode (Compress) The File
Slide 30
Eerie eyes seen near lake.
Input File:
Coding Table
+
000010 1100000110 ….
Notice that no code is prefix to any other code Ensures the decoding will be unique (Unlike Slide 8)
Step 4: Decode (Decompress)
• Must have the encoded file + the coding tree
• Scan the encoded file– For each 0 move left in the tree– For each 1 move right– Until reach a leaf node Emit that character and go back to the root
Slide 31
000010 1100000110 ….
+
Eerie …
Generate the original file
Huffman Algorithm
• Step 1: Get Frequencies– Scan the file to be compressed and count the occurrence of
each character
– Sort the characters based on their frequency
• Step 2: Build Tree & Assign Codes– Build a Huffman-code tree (binary tree)
– Traverse the tree to assign codes
• Step 3: Encode (Compress)– Scan the file again and replace each character by its code
• Step 4: Decode (Decompress)
– Huffman tree is the key to decompess the file
Slide 32