14 - Huffman Coding

14 - Huffman Coding - Part IV Greedy Algorithms Lecture 14...

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Unformatted text preview: Part IV: Greedy Algorithms Lecture 14: Huffman Coding Lecture 14: Huffman Coding Part IV: Greedy Algorithms Objective and Outline Objective : Another example of greedy algorithms Reference : Section 16.3 of CLRS Outline Coding and Decoding The optimal source coding problem Huffman coding: A greedy algorithm Correctness Lecture 14: Huffman Coding Part IV: Greedy Algorithms Encoding Example Suppose that we have a 100 , 000 character data file that we wish to store. The file contains only 6 characters, appearing with the following frequencies: a b c d e f Frequency in ’000s 45 13 12 16 9 5 A binary code encodes each character as a binary string or codeword . a code is a set of codewords e.g., { 000 , 001 , 010 , 011 , 100 , 101 } and { , 101 , 100 , 111 , 1101 , 1100 } Lecture 14: Huffman Coding Part IV: Greedy Algorithms Encoding Given a code (corresponding to some alphabet Σ) and a message it is easy to encode the message. Just replace the characters by the codewords. Example Σ = { a , b , c , d } If the code is C 1 = { a = 00 , b = 01 , c = 10 , d = 11 } then bad is encoded into 01 00 11 If the code is C 2 = { a = 0 , b = 110 , c = 10 , d = 111 } then bad is encoded into 110 0 111 Lecture 14: Huffman Coding Part IV: Greedy Algorithms Fixed-Length vs Variable-Length In a fixed-length code each codeword has the same length. In a variable-length code codewords may have different lengths. Example a b c d e f Freq in ’000s 45 13 12 16 9 5 fixed-len code 000 001 010 011 100 101 variable-len code 101 100 111 1101 1100 (note that, since there are 6 characters, a fixed-length code must have at least 3 bits per codeword). The fixed-length code requires 300 , 000 bits to store the file. The variable-length code uses only (45 · 1 + 13 · 3 + 12 · 3 + 16 · 3 + 9 · 4 + 5 · 4) · 1000 = 224 , 000bits, saving a lot of space! Lecture 14: Huffman Coding Part IV: Greedy Algorithms Decoding C 1 = { a = 00 , b = 01 , c = 10 , d = 11 } . C 2 = { a = 0 , b = 110 , c = 10 , d = 111 } . C 3 = { a = 1 , b = 110 , c = 10 , d = 111 } Given an encoded message, decoding is the process of turning it back into the original message. A message is uniquely decodable (vis-a-vis a particular code) if it can only be decoded in one way. Example Relative to C 1 , 010011 is uniquely decodable to bad. Relative to C 2 , 1100111 is uniquely decodable to bad. But, relative to C 3 , 1101111 is not uniquely decipherable since it could have encoded either bad or acad. In fact, one can show that every message encoded using C 1 or C 2 is uniquely decipherable. The unique decipherability property is needed in order for a code to be useful....
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This note was uploaded on 10/18/2009 for the course COMP 271 taught by Professor Arya during the Spring '07 term at HKUST.

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14 - Huffman Coding - Part IV Greedy Algorithms Lecture 14...

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