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Efficient encoding methods

Efficient encoding methods. Coding theory refers to study of code properties and their suitability to specific applications. Efficient codes are used, e.g., in data compression, cryptography, error-correction, and group testing.

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Efficient encoding methods

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  1. Efficient encoding methods • Coding theory refers to study of code properties and their suitability to specific applications. • Efficient codes are used, e.g., in data compression, cryptography, error-correction, and group testing. • Codes play a central part in information theory, in particular in the design of efficient and reliable data transmission methods. • Encoding methods focus on reduction (clever use) of redundancy in data compression (in error detection and correction mechanisms) Applied Algorithmics - week6

  2. Data compression • Data compression is the process of encoding information using fewer bits or other information-bearing units. • Compression is possible where the input data have statistical redundancy (e.g., in text files) or when relatively minor changes leading to smaller representation do not affect the quality/fidelity of the input (e.g., in pictures, video, or audio files). • Popular instances of data compression that many computer users are familiar with is the ZIP file format (texts), jpeg format (pictures) and mpeg format (for audio and video). Applied Algorithmics - week6

  3. Data compression • Some compression schemes are reversible so that the original data can be reconstructed (lossless data compression), while others accept some loss of data in order to achieve higher compression (lossy data compression). • Compression is important because it helps reduce the consumption of expensive resources, such as disk space or connection bandwidth. However, compression requires increased information processing power, which can also be expensive. Applied Algorithmics - week6

  4. Data compression - simple example • Run-Length Encoding • Data files frequently contain the same character repeated many times in a row. • For example, text files use multiple spaces to separate sentences, indent paragraphs, format tables & charts, etc. • Digitized signals can also have runs of the same value, indicating that the signal is not changing. • For example, an image of the night-time sky would contain long runs of the character or characters representing the black background. Applied Algorithmics - week6

  5. Data compression - simple example • Run-Length Encoding • In this scheme we focus on long runs of characters. • Each time a long run is encountered in the input data, two values are written to the output file. • The first of these values is the character itself, i.e., a flag to indicate that run-length compression is beginning. • The second value is the number of characters in the run. Applied Algorithmics - week6

  6. Move to Front Transform • Move to Front (MTF) transform is an encoding of data (typically a stream of bytes) designed to improve the performance of entropy encoding (coding scheme that assigns codes to symbols so as to match code lengths with the probabilities of the symbols) techniques of compression. • When properly implemented, it is fast enough that its benefits usually justify including it as an extra step in data compression algorithms. Applied Algorithmics - week6

  7. Move to Front Transform • In the context of MTF each byte value is encoded by its index in a list, which changes over the course of the algorithm. • The list is initially stored, e.g., in order by byte value (0, 1, 2, 3, ..., 255). Therefore, the first byte is always encoded by its own value. • However, after encoding a byte, that value is moved to the front of the list before continuing to the next byte. Applied Algorithmics - week6

  8. Move to Front Transform - example • Let S=<9,9,8,8,8,1,9,9,9> be an input sequence and the initial content of the queue Q is [0,1,2,3,4,5,6,7,8,9] • The encoding process will transform S as follows: • S=<9,9,8,8,8,1,9,9,9> and Q=[0,1,2,3,4,5,6,7,8,9] • S=<9,9,8,8,8,1,9,9,9> and Q=[9,0,1,2,3,4,5,6,7,8] • S=<9,0,8,8,8,1,9,9,9> and Q=[9,0,1,2,3,4,5,6,7,8] • S=<9,0,9,8,8,1,9,9,9> and Q=[8,9,0,1,2,3,4,5,6,7] • S=<9,0,9,0,8,1,9,9,9> and Q=[8,9,0,1,2,3,4,5,6,7] • S=<9,0,9,0,0,1,9,9,9> and Q=[8,9,0,1,2,3,4,5,6,7] • S=<9,0,9,0,0,3,9,9,9> and Q=[1,8,9,0,2,3,4,5,6,7] • S=<9,0,9,0,0,3,2,9,9> and Q=[9,1,8,0,2,3,4,5,6,7] • S=<9,0,9,0,0,3,2,0,9> and Q=[9,1,8,0,2,3,4,5,6,7] • S=<9,0,9,0,0,3,2,0,0> and Q=[9,1,8,0,2,3,4,5,6,7] • Where the blue value refers to the position of the symbol in the last instance of Q Applied Algorithmics - week6

  9. Burrows-Wheeler Transform • The Burrows-Wheeler transform (BWT), a.k.a. block-sorting compression, is one of the most popular method in data compression. • It was invented by Michael Burrows and David Wheeler, in 90-ties. • When a character string is transformed by the BWT, none of its characters change value. The transform rearranges in clever for the order of the characters in the string. • If the original string had several substrings that occurred frequently, then the transformed string will have several places where a single character is repeated multiple times in a row. • This is useful for compression, since it tends to be easy to compress a string that has runs of repeated characters by techniques such as move-to-front transform and run-length encoding. Applied Algorithmics - week6

  10. Cyclic rotations • For 0≤k ≤n-1, the kth cyclic rotation of the string w=w[0..n-1] is another string v = v[0..n-1] , s.t., v[i]=w[(i+k) mod n] k w x y v y x Applied Algorithmics - week6

  11. Burrows-Wheeler Transform • The Burrows-Wheeler Transform transform for the string w = w[0..n-1] is defined as follows: • Create a square matrix M[nxn] in which the kth row contains the kth cyclic rotation of w • Sort rows of M in lexicographic order • Store the string represented by the last column of M • And the index of row which contains the position of the original string w (i.e., 0th cyclic rotation) Applied Algorithmics - week6

  12. [0] [0] [4] [1] [1] [1] [2] [2] [6] [3] [3] [3] [0] [4] [4] [5] [5] [5] [2] [6] [6] [7] [7] [7] Burrows-Wheeler Transform (example) • Consider 5th Fibonacci word f5=babbabab b a b b a b a b a b a b b a b b a b b a b a b b BWT a b b a b a b b b b a b a b b a a b b a b b a b b a b a b b a b b a b a b b a b a b a b b a b b b a b b a b a b b a b b a b b a b a b b a b b a a b b a b b a b b b a b a b b a b b a b b a b a b b a b b a b a • The output string bbbbbaaa and position [4] Applied Algorithmics - week6

  13. Burrows-Wheeler Transform • The Burrows-Wheeler transform can be computed by the algorithm that constructs suffix arrays • Which means that the Burrows-Wheeler transform can be computed in linear time • The Burrows-Wheeler transform is reversible and the original string can be recovered efficiently via generation of consecutive columns of matrix M Applied Algorithmics - week6

  14. Burrows-Wheeler (reverse) Transform -- hard way b ba b b b a …….. ab …….. bab aba …….. baba abab …….. babab …….. b b b b ba ab abb babb abba a bab babba …….. …….. …….. …….. …….. …….. …….. b b b b ba ab abb babb abba a bab babba …….. …….. …….. …….. b b b b bb ba bab bbab baba b bba bbaba …….. …….. …….. …….. b b b b bb ba bab bbab babb b bba bbabb …….. …….. …….. …….. ba a bab a abab babb a a ab ababb b aba …….. …….. …….. …….. bb a bba a abba bbab a b a ab abbab abb a a a a ab bb bba abba bbab b …….. ……. abb ……. ……. abbab b ababb ….. bababb b bababba .. b bababbab ababba ….. ababbab ababbabb ….. .. ….. b abbab babbab b babbaba b babbabab abbaba abbabab abbababb ….. .. ….. b abbab babbab b babbabb b babbabba abbabb abbabba abbabbab ….. ….. .. b babab bbabab b bbababb b bbababba bababb bababba bababbab ….. ….. .. b babba bbabba b bbabbab b bbabbaba babbab babbaba babbabab ….. ….. .. babba a ababba ababbab ababbabb babbab a babbabb a babbabba ….. .. ….. bbaba a abbaba abbabab abbababb bbabab a bbababb a bbababba ….. .. ….. a bbabb abbabb a abbabba a abbabbab bbabba bbabbab bbabbaba Applied Algorithmics - week6

  15. b b a a a b b a a a b b b b b b b b b b b b a a b b b a a b b b a a b b b Burrows-Wheeler (reverse) Transform --easy way based on stable sorting property Corresponding symbols Structure Reverse BWT b a a a 4 b 1 b 6 b 3 b 0 a 5 a 2 a 7 1st col BWT 1st col BWT Just follow the cycle b a b b a b a b 0 1 2 3 4 5 6 7 Applied Algorithmics - week6

  16. Lempel-Ziv-Welch Compression • The Lempel-Ziv-Welch (LZW) compression algorithm is an example of dictionary based methods, in which longer fragments of the input text are replaced by much shorter references to code words stored in the special set called dictionary • LZW is an implementation of a lossless data compression algorithm developed by Abraham Lempel and Jacob Ziv. • It was published by Terry Welch in 1984 as an improved version of the LZ78 dictionary coding algorithm developed by Lempel and Ziv. Applied Algorithmics - week7

  17. LZW Compression • The key insight of the method is that it is possible to automatically build a dictionary of previously seen strings in the text being compressed. • The dictionary starts off with 256 entries, one for each possible character (single byte string). • Every time a string not already in the dictionary is seen, a longer string consisting of that string appended with the single character following it in the text, is stored in the dictionary. Applied Algorithmics - week7

  18. LZW Compression • The output consists of integer indices into the dictionary. These initially are 9 bits each, and as the dictionary grows, can increase to up to 16 bits. • A special symbol is reserved for "flush the dictionary" which takes the dictionary back to the original 256 entries, and 9 bit indices. This is useful if compressing a text which has variable characteristics, since a dictionary of early material is not of much use later in the text. • This use of variably increasing index sizes is one of Welch's contributions. Another was to specify an efficient data structure to store the dictionary. Applied Algorithmics - week7

  19. LZW Compression - example • Fibonacci language: w-1 =a, w-2=b, wi = wi-1·wi-2 for i>1 • For example, w6 = babbababbabba • We show how LZW compresses babbababbabba CW0 CW1 CW2 CW3 CW4 CW5 0 1 2 3 4 5 6 7 11 12 13 14 15 Virtual part In general: CWi = CWj o First(CWi+1) and j<i And in particular: CW4 = CW3 o First(CW5) Applied Algorithmics - week7

  20. LZW Compression - example • cw-2= b • cw-1 = a • cw0 = ba • cw1 = ab • cw2 = bb • cw3= bab • cw4 = babb • cw5= babba a b cw-1 cw-2 a b b cw1 cw2 cw0 b cw3 b cw4 a cw5 Applied Algorithmics - week7

  21. LZW Compression - compression stage Applied Algorithmics - week7

  22. LZW Compression - compression stage cw ; while ( read next symbol s from IN ) ifcw·s exists in the dictionary then cwcw·s; else add cw·s to the dictionary; save the index of cw in OUT; cws; Applied Algorithmics - week7

  23. Decompression stage Input IN – Compressed file of integers. Output OUT – Decompressed file of characters. |IN| = Z – Size of the compressed file. • Copy all numbers from file IN to vector V [256………..Z+255] • Create vector F [256………..Z+255] containing first characters of each code word • Create vector CW [256………..Z+255] of all code words fori=256toZ+255do if V[i] < 256then CW[i] Concatenate(char(V[i]), F[i+1]) else CW[i] Concatenate(CW(V[i]), F[i+1]) • Write to the output file OUT all code words without their last symbols Applied Algorithmics - week7

  24. LZW text compression • Theorem: For any input string S LZW algorithm computes its compressed counterpart in time O(n), where n is the length of S. Sketch of proof: The most complex operations are performed on dictionary. With a help of hash tables all operations can be performed in linear time. • Also the decompression stage is linear. Applied Algorithmics - week7

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