Huffman codes
- 1. Huffman Codes Huffman codes are an effective technique of ‘lossless data compression’ which means no information is lost. The algorithm builds a table of the frequencies of each character in a file The table is then used to determine an optimal way of representing each character as a binary string
- 2. Huffman Codes Consider a file of 100,000 characters from a- f, with these frequencies: a = 45,000 b = 13,000 c = 12,000 d = 16,000 e = 9,000 f = 5,000
- 3. Huffman Codes Typically each character in a file is stored as a single byte (8 bits) If we know we only have six characters, we can use a 3 bit code for the characters instead: a = 000, b = 001, c = 010, d = 011, e = 100, f = 101 This is called a fixed-length code With this scheme, we can encode the whole file with 300,000 bits (45000*3+13000*3+12000*3+16000*3+9000*3+5000*3) We can do better Better compression More flexibility
- 4. Huffman Codes Variable length codes can perform significantly better Frequent characters are given short code words, while infrequent characters get longer code words Consider this scheme: a = 0; b = 101; c = 100; d = 111; e = 1101; f = 1100 How many bits are now required to encode our file? 45,000*1 + 13,000*3 + 12,000*3 + 16,000*3 + 9,000*4 + 5,000*4 = 224,000 bits This is in fact an optimal character code for this file
- 5. Huffman Codes Prefix codes Huffman codes are constructed in such a way that they can be unambiguously translated back to the original data, yet still be an optimal character code Huffman codes are really considered “prefix codes” No code word is a prefix of any other code word Prefix code (9,55,50) Not a prefix code (9,5,55,59) because 5 is present in 55 and 59 This guarantees unambiguous decoding Once a code is recognized, we can replace with the decoded data, without worrying about whether we may also match some other code
- 6. Huffman Codes Both the encoder and decoder make use of a binary tree to recognize codes The leaves of the tree represent the unencoded characters Each left branch indicates a “0” placed in the encoded bit string Each right branch indicates a “1” placed in the bit string
- 7. Huffman Codes 100 a:45 0 55 1 25 0 c:12 0 b:13 1 30 1 14 0 d:16 1 f:5 e:9 0 1 A Huffman Code Tree To encode: Search the tree for the character to encode As you progress, add “0” or “1” to right of code Code is complete when you find character To decode a code: Proceed through bit string left to right For each bit, proceed left or right as indicated When you reach a leaf, that is the decoded character
- 8. Huffman Codes Using this representation, an optimal code will always be represented by a full binary tree Every non-leaf node has two children If this were not true, then there would be “waste” bits, as in the fixed-length code, leading to a non-optimal compression For a set of c characters, this requires c leaves, and c-1 internal nodes
- 9. Huffman Codes Given a Huffman tree, how do we compute the number of bits required to encode a file? For every character c: Let f(c) denote the character’s frequency Let dT(c) denote the character’s depth in the tree This is also the length of the character’s code word The total bits required is then: Cc T cdcfTB )()()(
- 10. Constructing a Huffman Code Huffman developed a greedy algorithm for constructing an optimal prefix code The algorithm builds the tree in a bottom-up manner It begins with the leaves, then performs merging operations to build up the tree At each step, it merges the two least frequent members together It removes these characters from the set, and replaces them with a “metacharacter” with frequency = sum of the removed characters’ frequencies
- 11. Example
- 16. For example, we code the 3 letter file ‘abc’ as 000.001.010