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How probabilities change can be seen clearly when using different orders with a statistical model. A statistical model tracks the probability of a symbol based on what symbols appeared previously in the input stream. The order of the model determines how many previous symbols are taken into account. An order-0 model, for example, won’t look at previous characters. An order-1 model looks at the one previous character, and so on.
The different order models can yield drastically different probabilities for a character. The letter ‘u’ under an order-0 model, for example, may have only a 1 percent probability of occurrence. But under an order-1 model, if the previous character was ‘q,’ the ‘u’ may have a 95 percent probability.
This seemingly unstable notion of a character’s probability proves troublesome for many people. They prefer that a character have a fixed “true” probability that told what the chances of its “really” occurring are. Claude Shannon attempted to determine the true information content of the English language with a “party game” experiment. He would uncover a message concealed from his audience a single character at a time. The audience guessed what the next character would be, one guess at a time, until they got it right. Shannon could then determine the entropy of the message as a whole by taking the logarithm of the guess count. Other researchers have done more experiments using similar techniques.
While these experiments are useful, they don’t circumvent the notion that a symbol’s probability depends on the model. The difference with these experiments is that the model is the one kept inside the human brain. This may be one of the best models available, but it is still a model, not an absolute truth.
In order to compress data well, we need to select models that predict symbols with high probabilities. A symbol that has a high probability has a low information content and will need fewer bits to encode. Once the model is producing high probabilities, the next step is to encode the symbols using an appropriate number of bits.
Once Information Theory had advanced to where the number of bits of information in a symbol could be determined, the next step was to develop new methods for encoding information. To compress data, we need to encode symbols with exactly the number of bits of information the symbol contains. If the character ‘e’ only gives us four bits of information, then it should be coded with exactly four bits. If ‘x’ contains twelve bits, it should be coded with twelve bits.
By encoding characters using EBCDIC or ASCII, we clearly aren’t going to be very close to an optimum method. Since every character is encoded using the same number of bits, we introduce lots of error in both directions, with most of the codes in a message being too long and some being too short.
Solving this coding problem in a reasonable manner was one of the first problems tackled by practitioners of Information Theory. Two approaches that worked well were Shannon-Fano coding and Huffman coding—two different ways of generating variable-length codes when given a probability table for a given set of symbols.
Huffman coding, named for its inventor D.A. Huffman, achieves the minimum amount of redundancy possible in a fixed set of variable-length codes. This doesn’t mean that Huffman coding is an optimal coding method. It means that it provides the best approximation for coding symbols when using fixed-width codes.
The problem with Huffman or Shannon-Fano coding is that they use an integral number of bits in each code. If the entropy of a given character is 2.5 bits, the Huffman code for that character must be either 2 or 3 bits, not 2.5. Because of this, Huffman coding can’t be considered an optimal coding method, but it is the best approximation that uses fixed codes with an integral number of bits. Here is a sample of Huffman codes:
Symbol | Huffman Code |
---|---|
E | 100 |
T | 101 |
A | 1100 |
I | 11010 |
… | |
X | 01101111 |
Q | 01101110001 |
Z | 01101110000 |
Though Huffman coding is inefficient due to using an integral number of bits per code, it is relatively easy to implement and very economical for both coding and decoding. Huffman first published his paper on coding in 1952, and it instantly became the most-cited paper in Information Theory. It probably still is. Huffman’s original work spawned numerous minor variations, and it dominated the coding world till the early 1980s.
As the cost of CPU cycles went down, new possibilities for more efficient coding techniques emerged. One in particular, arithmetic coding, is a viable successor to Huffman coding.
Arithmetic coding is somewhat more complicated in both concept and implementation than standard variable-width codes. It does not produce a single code for each symbol. Instead, it produces a code for an entire message. Each symbol added to the message incrementally modifies the output code. This is an improvement because the net effect of each input symbol on the output code can be a fractional number of bits instead of an integral number. So if the entropy for character ‘e’ is 2.5 bits, it is possible to add exactly 2.5 bits to the output code.
An example of why this can be more effective is shown in the following table, the analysis of an imaginary message. In it, Huffman coding would yield a total message length of 89 bits, but arithmetic coding would approach the true information content of the message, or 83.56 bits. The difference in the two messages works out to approximately 6 percent. Here are some sample message probabilities:
Symbol | Number of Occurrences | Information Content | Huffman Code Bit Count | Total Bits Huffman Coding | Total Bits Arithmetic Coding |
---|---|---|---|---|---|
E | 20 | 1.26 bits | 1 bits | 20 | 25.2 |
A | 20 | 1.26 bits | 2 bits | 40 | 25.2 |
X | 3 | 4.00 bits | 3 bits | 9 | 12.0 |
Y | 3 | 4.00 bits | 4 bits | 12 | 12.0 |
Z | 2 | 4.58 bits | 4 bits | 8 | 9.16 |
89 | 83.56 | ||||
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