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# Lecture05s - 1 CSEP 521 Applied Algorithms Spring 2005 Statistical Lossless Data Compression Lecture 5 Statistical Lossless Data Compression 2

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Unformatted text preview: 1 CSEP 521 Applied Algorithms Spring 2005 Statistical Lossless Data Compression Lecture 5 - Statistical Lossless Data Compression 2 Outline for Tonight • Basic Concepts in Data Compression • Entropy • Prefix codes • Huffman Coding • Arithmetic Coding • Run Length Coding (Golomb Code) Lecture 5 - Statistical Lossless Data Compression 3 Reading • Huffman Coding: CLRS 385-392 • Other sources can be found: – Data Compression: The Complete Reference, 3rd Edition by David Salomon – Introduction to Data Compression by Khalid Sayood. Lecture 5 - Statistical Lossless Data Compression 4 Basic Data Compression Concepts Encoder Decoder compressed original x y x ˆ • Lossless compression – Also called entropy coding, reversible coding. • Lossy compression – Also called irreversible coding. • Compression ratio = – is number of bits in x . x x ˆ = x x ˆ ≠ y x x decompressed Lecture 5 - Statistical Lossless Data Compression 5 Why Compress • Conserve storage space • Reduce time for transmission – Faster to encode, send, then decode than to send the original • Progressive transmission – Some compression techniques allow us to send the most important bits first so we can get a low resolution version of some data before getting the high fidelity version • Reduce computation – Use less data to achieve an approximate answer Lecture 5 - Statistical Lossless Data Compression 6 Braille • System to read text by feeling raised dots on paper (or on electronic displays). Invented in 1820s by Louis Braille, a French blind man. a b c z and the with mother th gh ch 2 Lecture 5 - Statistical Lossless Data Compression 7 Braille Example Clear text: Call me Ishmael. Some years ago -- never mind how long precisely -- having \\ little or no money in my purse, and nothing particular to interest me on shore, \\ I thought I would sail about a little and see the watery part of the world. (238 characters) Grade 2 Braille: &¡¢££ ¤¥ &¦§¨¤¢¥£© &ªª« ¬¥­®­« ¢¯° ±± ²ªª¥ ¤³´ µ¶ £·¯ ¸¹¥¡¦«¥£¬ ±± µ¢º» §§ ££ °¹ ²° ¤ªª°¬ ³ ¤¬ ¸¼¹«¥½ § ²°!» ªª¸¦¡¼£­®­ " ³#\$¥% ¤¥ °² §¨°¹¥½ §§ &¦ ­!­ªª­¾­ &¦ ’´ «¢¦£ ¢( ¢ ££ § «¥¥ ) ’¢#\$¬ ªª¸ * ) §+’© (203 characters) 238/203 = 1.17 Lecture 5 - Statistical Lossless Data Compression 8 Lossless Compression • Data is not lost - the original is really needed. – text compression – compression of computer binary files • Compression ratio typically no better than 4:1 for lossless compression on many kinds of files. • Statistical Techniques – Huffman coding – Arithmetic coding – Golomb coding • Dictionary techniques – LZW, LZ77 – Sequitur – Burrows-Wheeler Method • Standards - Morse code, Braille, Unix compress, gzip, zip, bzip, GIF, JBIG, Lossless JPEG Lecture 5 - Statistical Lossless Data Compression 9 Lossy Compression • Data is lost, but not too much.Data is lost, but not too much....
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## This note was uploaded on 02/04/2012 for the course COMPUTER 101 taught by Professor Ahmed during the Spring '11 term at alamo.edu.

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Lecture05s - 1 CSEP 521 Applied Algorithms Spring 2005 Statistical Lossless Data Compression Lecture 5 Statistical Lossless Data Compression 2

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