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Data compression introduction

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Data compression introduction Data compression introduction Presentation Transcript

  • DATA COMPRESSION Rahul V. Khanwani Roll No. 47 Department Of Computer Science
  • Introduction • WinRaR • Now A days data And Information Being A Major thing. • The Data Compression Refers To the name Compress. It Means To compress The data And Utilize the System Space. Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Why To Utilize Space ? • For Example • Similar Kind Of Starting Character In Database – Amit. – Amin. • Reducing Size Length • Thus To Reduce Unnecessary Space We Need Data Compression. A M I T R A H U L Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Need Of Data Compression • To Reduce The Space: – Compression of space Depends on Compression Technique • Increase Channel bandwith: – Send-Receive Data In Minimal Form – Smaller Data Increase The Channel Bandwith • Security: – Compression Change The Original Value Of data. Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Types Of Data Compression 1. Lossless Compression 1. Shannon-Fano 2. Huffman 3. Lempel-Ziv (LZ) 4. Arithmetic Coding 5. Run Length Encoding 6. Burrows-Wheeler (BWT) 7. Deflate 2. Loosy Compression 1. Image 2. Audio 3. VideoRahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Loosy data compression • In this type of compression data which was compressed are not recovered properly. • In this technique some part of data in range of time period is drop in short some part are cut from chain of data bits. Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Lossless data compression • In this compression technique after compression at recovery time x:-we will get data as we have before compression. – Ex:- » Zip file Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Terms Of Compression • Coding – Describes the procedure defining the transformation of symbols from one set of symbols to another one. • Encoding – Process denotes the coding into a particular destination format. – Converting Bitmap to JPEG • Decoding – Process denotes the reverse process related to Encoding – JPEG to Bitmap Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Data compression an example • Image Conversations: • RAW • BMP(bitmap image): 2.25MB • TTIF(tagged image file format):1.65MB • PNG(Portable Network Graphics):1.44MB • GIF(Graphic Interchange Format):254KB • JPEG(Joint Photographic Experts Group):291KB Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • DATA COMPRESSION TECHNIQUES Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Shannon-Fano Huffman Lempel-Ziv (LZ) Arithmetic Coding Run Length Encoding Burrows-Wheeler (BWT) Deflate 1 2 3 4 5 6 7 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • SHANNON-FANO • Developed In 1960. • Shannon–Fano coding, named after Claude Elwood Shannon and Robert Fano, is a technique for constructing a prefix code based on a set of symbols and their probabilities. • Also Known As Variable Length Coding (VLC). • Top Down Approach.Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Shannon-Fano Algorithm 1. For a given list of symbols, develop a corresponding list of probabilities or frequency counts. 2. Sort the lists of symbols according to frequency, with the most frequently occurring symbols at the left and the least common at the right. 3. Divide the list into two parts, with the total frequency counts of the left part being as close to the total of the right as possible. 4. The left part of the list is assigned the binary digit 0, and the right part is assigned the digit 1. This means that the codes for the symbols in the first part will all start with 0, and the codes in the second part will all start with 1. 5. Recursively apply the steps 3 and 4 to each of the two halves, subdividing groups and adding bits to the codes until each symbol has become a corresponding code leaf on the tree.Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count A 15 B 7 C 6 D 6 E 5 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count Value A 15 0 B 7 0 C 6 1 D 6 1 E 5 1 22 17 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count Value A 15 00 C 6 1 D 6 1 E 5 1 B 7 01 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count Value A 15 00 C 6 10 B 7 01 D 6 110 E 5 111 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count Value A 15 00 C 6 10 B 7 01 D 6 11 E 5 11 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Example: Symbol Count Value A 15 00 C 6 10 B 7 01 D 6 110 E 5 110 39 Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • Conclusion • Shannon–Fano is almost never used. • Huffmam coding is almost as computationally simple and produces prefix codes that always achieve the lowest expected code word length. • Shannon–Fano coding is used in the IMPLODE compression method, which is part of the ZIP file format, where it is desired to apply a simple algorithm with high performance and minimum requirements for programming. Rahul Khanvani For More Visit Binarybuzz.wordpress.com
  • THANK YOU Rahul Khanvani For More Visit Binarybuzz.wordpress.com