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Processing of large document collections. Part 4. Text compression. Despite a continuous increase in storage and transmission capacities, more and more effort has been put into using compression to increase the amount of data that can be handled

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text compression
Text compression
  • Despite a continuous increase in storage and transmission capacities, more and more effort has been put into using compression to increase the amount of data that can be handled
  • no matter how much storage space or transmission bandwidth is available, someone always finds ways to fill it with
text compression1
Text compression
  • Efficient storage and representation of information is an old problem (before the computer era)
    • Morse code: uses shorter representations for common characters
    • Braille code for the blind: includes contractions, which represent common words with 2 or 3 characters
text compression2
Text compression
  • On a computer: changing the representation of a file so that it takes less space to store or less time to transmit
    • original file can be reconstructed exactly from the compressed representation
  • different than data compression in general
    • text compression has to be lossless
    • compare with sound and images: small changes and noise is tolerated
text compression methods
Text compression methods
  • Huffman coding (in the 50’s)
    • compressing English: 5 bits/character
  • Ziv-Lempel compression (in the 70’s)
    • 4 bits/character
  • arithmetic coding
    • 2 bits/char (more processing power needed)
  • prediction by partial matching (80’s)
text compression methods1
Text compression methods
  • Since 80’s compression rate has been about the same
  • improvements are made in processor and memory utilization during compression
  • also: amount of compression may increase when more memory (for compression and uncompression) is available
text compression methods2
Text compression methods
  • Most text compression methods can be placed in one of two classes:
    • symbolwise methods
    • dictionary methods
symbolwise methods
Symbolwise methods
  • Work by estimating the probabilities of symbols (often characters)
    • coding one symbol at a time
    • using shorter codewords for the most likely symbols (in the same way as Morse code does)
dictionary methods
Dictionary methods
  • Achieve compression by replacing words and other fragments of text with an index to an entry in a ”dictionary”
    • the Braille code is a dictionary method since special codes are used to represent whole words
symbolwise methods1
Symbolwise methods
  • Usually based on Huffman coding or arithmetic coding
  • variations differ mainly in how they estimate probabilities for symbols
    • the more accurate these estimates are, the greater the compression that can be achieved
    • to obtain good compression, the probability estimate is usually based on the context in which a symbol occurs
symbolwise methods2
Symbolwise methods
  • Modeling
    • estimating probabilities
    • there does not appear to be any single ”best” method
  • Coding
    • converting the probabilities into a bitstream for transmission
    • well understood, can be performed effectively
dictionary methods1
Dictionary methods
  • Generally use quite simple representations to code references to entries in the dictionary
  • obtain compression by representing several symbols as one output codeword
dictionary methods2
Dictionary methods
  • often based on Ziv-Lempel coding
    • replaces strings of characters with a reference to a previous occurrence of a string
    • adaptive
    • effective: most characters can be coded as part of a string that has occurred earlier in the text
    • compression is achieved if the reference is stored in fewer bits than the string it replaces
  • Compression methods obtain high compression by forming good models of the data that is to be coded
  • the function of a model is to predict symbols
    • e.g. during the encoding of a text , the ”prediction” for the next symbol might include a probability of 2% for the letter ’u’, based on its relative frequency in a sample of text
  • The set of all possible symbols is called the alphabet
  • the probability distribution provides an estimated probability for each symbol in the alphabet
encoding decoding
Encoding, decoding
  • the model provides the probability distribution to the encoder, which uses it to encode the symbol that actually occurs
  • the decoder uses an identical model together with the output of the encoder to find out what the encoded symbol was
information content of a symbol
Information content of a symbol
  • The number of bits in which a symbol s should be coded is called the information content I(s) of the symbol
  • the information content I(s) is directly related to the symbol’s predicted probability P(s), by the function
    • I(s) = -log P(s) bits
information content of a symbol1
Information content of a symbol
  • The average amount of information per symbol over the whole alphabet is known as the entropy of the probability distribution, denoted by H:
information content of a symbol2
Information content of a symbol
  • Provided that the symbols appear independently and with the assumed probabilities, H is a lower bound on compression, measured in bits per symbol, that can be achieved by any coding method
information content of a symbol3
Information content of a symbol
  • If the probability of symbol ’u’ is estimated to be 2%, the corresponding information content is 5.6 bits
  • if ’u’ happens to be the next symbol that is to be coded, it should be transmitted in 5.6 bits
  • predictions can usually be improved by taking account of the previous symbol
information content of a symbol4
Information content of a symbol
  • predictions can usually be improved by taking account of the previous symbol
  • if a ’q’ has just occurred, the probability of ’u’ may jump to 95 %, based on how often ’q’ is followed by ’u’ in a sample of text
  • information content of ’u’ in this case is 0.074 bits
information content of a symbol5
Information content of a symbol
  • Models that take a few immediately preceding symbols into account to make a prediction are called finite-context models of order m
    • m is the number of previous symbols used to make a prediction
adaptive models
Adaptive models
  • There are many ways to estimate the probabilities in a model
  • we could use static modelling:
    • always use the same probabilities for symbols, regardless of what text is being coded
    • compressing system may not perform well, if different text is received
      • e.g. a model for English with a file of numbers
adaptive models1
Adaptive models
  • One solution is to generate a model specifically for each file that is to be compressed
  • an initial pass is made through the file to estimate symbol probabilities, and these are transmitted to the decode before transmitting the encoded symbols
  • this is called semi-static modelling
adaptive models2
Adaptive models
  • Semi-static modelling has the advantage that the model is invariably better suited to the input than a static one, but the penalty paid is
    • having to transmit the model first,
    • as well as the preliminary pass over the data to accumulate symbol probabilities
adaptive models3
Adaptive models
  • Adaptive model begins with a bland probability distribution and gradually alters it as more symbols are encountered
  • as an example, assume a zero-order model, i.e., no context is used to predict the next symbol
adaptive models4
Adaptive models
  • Assume that a encoder has already encoded a long text and come to a sentence: It migh
  • now the probability that the next character is ’t’ is estimated to be 49,983/768,078 = 6.5 %, since in the previous text, 49,983 characters of the total of 768,078 characters were ’t’
adaptive models5
Adaptive models
  • Using the same system, ’e’ has the probability 9.4 % and ’x’ has probability 0.11 %
  • the model provides this estimated probability distribution to an encoder
  • the decoder is able to generate the same model since it has the same probability estimates as the encoder
adaptive models6
Adaptive models
  • For a higher-order model, such as a first-order model, the probability is estimated by how often that character has occurred in the current context
  • in a zero-order model earlier, a symbol ’t’ occurred in a context: It migh , but the model made no use of the characters of the phrase
adaptive models7
Adaptive models
  • A first-order model would use the final ’h’ as a context with which to condition the probability estimates
  • the letter ’h’ has occurred 37,525 times in the prior text, and 1,133 of these times it was followed by a ’t’
  • the probability of ’t’ occurring after an ’h’ can be estimated to be 1,133/37,525=3.02 %
adaptive models8
Adaptive models
  • For ’t’, a prediction of 3.2% is actually worse than in the zero-order model because ’t’ is rare in this context (’e’ follows ’h’ much more often)
  • second-order model would use the relative frequency that the context ’gh’ is followed by ’t’, which is the case in 64,6%
adaptive models9
Adaptive models
  • Good: robust, reliable, flexible
  • Bad: not suitable for random access to compressed files
    • a text can be decoded only from the beginning: the model used for coding a particular part of the text is determined from all the preceding text
    • -> not suitable for full-text retrieval
  • Coding is the task of determining the output representation of a symbol, based on a probability distribution supplied by a model
  • general idea: the coder should output short codewords for likely symbols and long codewords for rare ones
  • symbolwise methods depend heavily on a good coder to achieve compression
huffman coding
Huffman coding
  • A phrase is coded by replacing each of its symbols with the codeword given by a table
  • Huffman coding generates codewords for a set of symbols, given some probability distribution for the symbols
  • the type of code is called prefix-free code
    • no codeword is the prefix of another symbol’s codeword
huffman coding1
Huffman coding
  • The codewords can be stored in a tree (a decoding tree)
  • Huffman’s algorithm works by constructing the decoding tree from the bottom up
huffman coding2
Huffman coding
  • Algorithm
    • create for each symbol a leaf node containing the symbol and its probability
    • two nodes with the smallest probabilities become siblings under a new parent node, which is given a probability equal to the sum of its two children’s probabilities
    • the combining operation is repeated until there is only one node without a parent
    • the two branches from every nonleaf node are then labeled 0 and 1
huffman coding3
Huffman coding
  • Huffman coding is generally fast for both encoding and decoding, provided that the probability distribution is static
    • adaptive Huffman coding is possible, but needs either a lot of memory or is slow
  • coupled with a word-based model (rather than character-based model), gives a good compression
canonical huffman codes
Canonical Huffman codes
  • The frequency of each word is counted
  • the codewords are chosen for each word to minimize the size of the compressed file -> static zero-order word-level model
  • the codewords are shown in decreasing order of length (and therefore in increasing order of word frequency)
    • within each block of codes of the same length, words are ordered alphabetically
canonical huffman codes1
Canonical Huffman codes
  • The list begins with the thousands of words (and numbers) that appear only once
  • when the codewords are sorted in lexical order, they are also in order from the longest to the shortest codeword
canonical huffman codes2
Canonical Huffman codes
  • A word’s encoding can be determined quickly from the length of its codeword, how far through the list it is, and the codeword for the first word of that length
canonical huffman codes3
Canonical Huffman codes
  • It is not necessary to store a decode tree
  • all the is required is
    • a list of the symbols ordered according to the lexical order of the codewords
    • an array storing the first codeword of each distinct length
dictionary models
Dictionary models
  • Dictionary-based compression methods use the principle of replacing substrings in a text with a codeword that identifies that substring in a dictionary
  • dictionary contains a list of substrings and a codeword for each substring
  • often fixed codewords used
    • reasonable compression is obtained even if coding is simple
dictionary models1
Dictionary models
  • The simplest dictionary compression methods use small dictionaries
  • for instance, digram coding
    • selected pairs of letters are replaced with codewords
    • a dictionary for the ASCII character set might contain the 128 ASCII characters, as well as 128 common letter pairs
dictionary models2
Dictionary models
  • Digram coding…
    • the output codewords are eight bits each
    • the presence of the full ASCII character set ensures that any (ASCII) input can be represented
    • at best, every pair of characters is replaced with a codeword, reducing the input from 7 bits/character to 4 bits/characters
    • at worst, each 7 bit character will be expanded to 8 bits
dictionary models3
Dictionary models
  • Natural extension:
    • put even larger entries in the dictionary, e.g. common words like ’and’, ’the’,… or common components of words like ’pre’, ’tion’…
  • a predefined set of dictionary phrases make the compression domain-dependent
    • or very short phrases have to be used -> good compression is not achieved
dictionary models4
Dictionary models
  • One way to avoid the problem of the dictionary being unsuitable for the text at hand is to use a semi-static dictionary scheme
    • constuct a new dictionary for every text that is to be compressed
    • overhead of transmitting or storing the dictionary is significant
    • decision of which phrases should be included is a difficult problem
dictionary models5
Dictionary models
  • Solution: use an adaptive dictionary scheme
  • Ziv-Lempel coders (LZ77 and LZ78)
  • a substring of text is replaced with a pointer to where it has occurred previously
  • dictionary: all the text prior to the current position
  • codewords: pointers
dictionary models6
Dictionary models
  • Ziv-Lempel…
    • the prior text makes a very good dictionary since it is usually in the same style and language as upcoming text
    • the dictionary is transmitted implicitly at no extra cost, because the decoder has access to all previously encoded text
  • Key benefits:
    • relatively easy to implement
    • decoding can be performed extremely quickly using only a small amount of memory
  • suitable when the resources required for decoding must be minimized, like when data is distributed or broadcast from a central source to a number of small computers
  • The output of an encoder consists of a sequence of triples, e.g. <3,2,b>
    • the first component of a triple indicates how far back to look in the previous (decoded) text to find the next phrase
    • the second component records how long the phrase is
    • the third component gives the next character from the input
  • The components 1 and 2 consitute a pointer back into the text
  • the component 3 is actually necessary only when the character to be coded does not occur anywhere in the previous input
  • Encoding
    • for the text from the current point ahead:
      • search for the longest match in the previous text
      • output a triple that records the position and length of the match
      • the search for a match may return a length of zero, in which case the position of the match is not relevant
    • search can be accelerated by indexing the prior text with a suitable data structure
  • limitations on how far back a pointer can refer and the maximum size of the string referred to
  • e.g. for English text, a window of a few thousand characters
  • the length of the phrase e.g. maximum of 16 characters
  • otherwise too much space wasted without benefit
  • The decoding program is very simple, so it can be included with the data at very little cost
  • in fact, the compressed data is stored as part of the decoder program, which makes the data self-expanding
  • common way to distribute files
  • Good compression methods perform best when compressing large files
  • this tends to preclude random access because the decompression algorithms are sequential by nature
  • full-text retrieval systems require random access, and special measures need to be taken to facilitate this
  • There are two reasons that the better compression methods require files to be decoded from the beginning
    • they use variable-length codes
    • their models are adaptive
  • With variable-length codes, it is not possible to begin decoding at an arbitrary position in the file
    • we cannot be sure of starting on the boundary between two codewords
  • adaptive modelling: even if the codeword boundary is known, the model required for decoding can be constructed only using all the preceding text
  • To achieve synchronization with adaptive modelling, large files must be broken up into small sections of a few kilobytes
  • a good compression cannot be obtained by compressing the small sections separately ->the compression model has to be constructed based on a sample text of the whole data
  • For full-text retrieval, it is usually preferable to use a static model rather than an adaptive one
  • static models are more appropriate given the static nature of a large textual database
  • In a full-text retrieval system, the main text usually consists of a number of documents, which represent the smallest unit to which random access is required
  • in an uncompressed text, a document can be identified simply by specifying how many bytes it is from the start of the file
    • when a variable-length code is used, a document may not start on a byte boundary
  • A solution is to insist that documents begin on byte boundaries, which means that the last byte may contain some wasted bits
  • there has to be some way to tell the decoder that it should not interpret some bits