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SIMS-201

SIMS-201. Compressing Information. Overview. Chapter 7: Compression Introduction Entropy Huffman coding Universal coding. Introduction. World Wide Web not World Wide Wait.

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SIMS-201

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  1. SIMS-201 Compressing Information

  2. Overview Chapter 7: Compression • Introduction • Entropy • Huffman coding • Universal coding

  3. Introduction World Wide Web not World Wide Wait • Compression techniques can significantly reduce the bandwidth and memory required for sending, receiving, and storing data. • Most computers are equipped with modems that compress or decompress all information leaving or entering via the line. • With a mutually recognized system (e.g. WinZip) the amount of data can be significantly diminished. • Examples of compression techniques: • Compressing BINARY DATA STREAMS • Variable length coding (e.g. Huffman coding) • Universal Coding (e.g. WinZip) • IMAGE-SPECIFIC COMPRESSION (will will see that images are well suited for compression) • GIF and JPEG • VIDEO COMPRESSION • MPEG

  4. Why can we compress information? REDUNDANCY • Compression is possible because information usually contains redundancies, or information that is often repeated. • For example, two still images from a video sequence of images are often similar. This fact can be exploited by transmitting only the changes from one image to the next. • For example, a line of data often contains redundancies: • File compression programs remove this redundancy. “Ask not what your country can do for you - ask what you can do for your country.”

  5. FREQUENCY • Some characters occur more frequently than others. • It’s possible to represent frequently occurring characters with a smaller number of bits during transmission. • This may be accomplished by a variable length code, as opposed to a fixed length code like ASCII. • An example of a simple variable length code is Morse Code. • “E” occurs more frequently than “Z” so we represent “E” with a shorter length code: . = E - = T - - . . = Z - - . - = Q

  6. Information Theory • Variable length coding exploits the fact that some information occurs more frequently than others. • The mathematical theory behind this concept is known as: INFORMATION THEORY • Claude E. Shannon developed modern Information Theory at Bell Labs in 1948. • He saw the relationship between the probability of appearance of a transmitted signal and its information content. • This realization enabled the development of compression techniques.

  7. A Little Probability • Shannon (and others) found that information can be related to probability. • An event has a probability of 1 (or 100%) if we believe this event will occur. • An event has a probability of 0 (or 0%) if we believe this event will not occur. • The probability that an event will occur takes on values anywhere from 0 to 1. • Consider a coin toss: heads or tails each has a probability of .50 • In two tosses, the probability of tossing two heads is: • 1/2 x 1/2 = 1/4 or .25 • In three tosses, the probability of tossing all tails is: • 1/2 x 1/2 x 1/2 = 1/8 or .125 • We compute probability this way because the result of each toss is independent of the results of other tosses.

  8. Entropy • If the probability of a binary event is .5 (like a coin), then, on average, you need one bit to represent the result of this event. • As the probability of a binary event increases or decreases, the number of bits you need, on average, to represent the result decreases • The figure is expressing that unless an event is totally random, you can convey the information of the event in fewer bits, on average, than it might first appear • Let’s do an example... As part of information theory, Shannon developed the concept of ENTROPY Bits Probability of an event

  9. Example from text.. A MEN’S SPECIALTY STORE • The probability of male patrons is .8 • The probability of female patrons is .2 • Assume for this example, groups of two enter the store. Calculate the probabilities of different pairings: • Event A, Male-Male. P(MM) = .8 x .8 = .64 • Event B, Male-Female. P(MF) = .8 x .2 = .16 • Event C, Female-Male. P(FM) = .2 x .8 = .16 • Event D, Female-Female. P(FF) = .2 x .2 = .04 • We could assign the longest codes to the most infrequent events while maintaining unique decodability.

  10. A MM B MF B MF C FM B MF A MM Example (cont..) • Let’s assign a unique string of bits to each event based on the probability of that event occurring. • Event Name Code A Male-Male 0 B Male-Female 10 C Female-Male 110 D Female-Female 111 • Given a received code of: 01010110100, determine the events: • The above example has used a variable length code.

  11. Variable Length Coding Takes advantage of the probabilistic nature of information. • Unlike fixed length codes like ASCII, variable length codes: • Assign the longest codes to the most infrequent events. • Assign the shortest codes to the most frequent events. • Each code word must be uniquely identifiable regardless of length. • Examples of Variable Length Coding • Morse Code • Huffman Coding If we have total uncertainty about the information we are conveying, fixed length codes are preferred.

  12. Morse Code • Characters represented by patterns of dots and dashes. • More frequently used letters use short code symbols. • Short pauses are used to separate the letters. • Represent “Hello” using Morse Code: • H . . . . • E . • L . - . . • L . - . . • O - - - • Hello . . . . . . - . . . - . . - - -

  13. Creates a Binary Code Tree Nodes connected by branches with leaves Top node – root Two branches from each node Start Branches Root 1 0 A Node 0 1 B 1 0 C D Leaves Huffman Coding The Huffman coding procedure finds the optimum, uniquely decodable, variable length code associated with a set of events, given their probabilities of occurrence.

  14. A 0 B 10 C 110 D 111 Given the adjacent Huffman code tree, decode the following sequence: 11010001110 Start Branches Root 1 0 A Node 0 1 B 1 0 C D Leaves 110 C 10 B 0 A 0 A 111 D 0 A Huffman Coding

  15. Huffman Code Construction • First list all events in descending order of probability. • Pair the two events with lowest probabilities and add their probabilities. .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F 0.15 .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F

  16. Huffman Code Construction • Repeat for the pair with the next lowest probabilities. 0.25 0.15 .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F

  17. Huffman Code Construction • Repeat for the pair with the next lowest probabilities. 0.4 0.25 0.15 .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F

  18. Huffman Code Construction • Repeat for the pair with the next lowest probabilities. 0.4 0.6 0.25 0.15 .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F

  19. Huffman Code Construction • Repeat for the last pair and add 0s to the left branches and 1s to the right branches. 1 0 0.4 0.6 0 1 0.25 0.15 0 1 1 0 1 0 .3 Event A .3 Event B .13 Event C .12 Event D .1 Event E .05 Event F 00 01 100 101 110 111

  20. 00 A 00 A 111 F 01 B 01 B 100 C 01 B 00 A 00 A 00 A 111 F Exercise • Given the code we just constructed: • Event A: 00 Event B: 01 • Event C: 100 Event D: 101 • Event E: 110 Event F: 111 • How can you decode the string: 0000111010110001000000111? • Starting from the leftmost bit, find the shortest bit pattern that matches one of the codes in the list. The first bit is 0, but we don’t have an event represented by 0. We do have one represented by 00, which is event A. Continue applying this procedure:

  21. Universal Coding • Huffman has its limits • We must know a priori the probability of the characters or symbols we are encoding. • What if a document is “one of a kind?” • Universal Coding schemes do not require a knowledge of the statistics of the events to be coded. • Universal Coding is based on the realization that any stream of data consists of some repetition. • Lempel-Ziv coding is one form of Universal Coding presented in the text. • Compression results from reusing frequently occurring strings. • Works better for long data streams. Inefficient for short strings. • Used by WinZip to compress information.

  22. Lempel-Ziv Coding • The basis for Lempel-Ziv coding is the idea that we can achieve compression of a string by always coding a series of zeroes and ones as some previous string (prefix string) plus one new bit. Compression results from reusing frequently occurring strings • We will not go through Lempel-Ziv coding in detail..

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