'Prefix codes' diaporamas de présentation

Advanced Algorithms

721 views • 71 slides

Algorithms in the Real World

Algorithms in the Real World. Data Compression: Lectures 1 and 2. Compression in the Real World. Generic File Compression Files : gzip (LZ77), bzip (Burrows-Wheeler), BOA (PPM) Archivers : ARC (LZW), PKZip (LZW+) File systems : NTFS Communication

783 views • 55 slides

Algorithms in the Real World

Algorithms in the Real World. Data Compression: Lectures 1 and 2. Compression in the Real World. Generic File Compression Files : gzip (LZ77), bzip (Burrows-Wheeler), BOA (PPM) Archivers : ARC (LZW), PKZip (LZW+) File systems : NTFS Communication

674 views • 55 slides

Trees and Codes

544 views • 42 slides

Algorithms & Data Structures

500 views • 37 slides

15-583:Algorithms in the Real World

15-583:Algorithms in the Real World. Data Compression I Introduction Information Theory Probability Coding. Compression in the Real World. Generic File Compression files: gzip (LZ77), bzip (Burrows-Wheeler), BOA (PPM) archivers: ARC (LZW), PKZip (LZW+) file systems: NTFS Communication

332 views • 33 slides

Audio Coding and Standards

418 views • 31 slides

Basic Data Structures - Trees

Trees (Ch. 9.2) Longin Jan Latecki Temple University based on slides by Simon Langley and Shang-Hua Teng. Basic Data Structures - Trees. Informal : a tree is a structure that looks like a real tree (up-side-down) Formal : a tree is a connected graph with no cycles. Trees - Terminology.

309 views • 30 slides

4.8 Huffman Codes

4.8 Huffman Codes. These lecture slides are supplied by Mathijs de Weerd. Data Compression. Q. Given a text that uses 32 symbols (26 different letters, space, and some punctuation characters), how can we encode this text in bits ?

374 views • 26 slides

4.8 Huffman Codes

4.8 Huffman Codes. These lecture slides are supplied by Mathijs de Weerd. Data Compression. Q. Given a text that uses 32 symbols (26 different letters, space, and some punctuation characters), how can we encode this text in bits ?

334 views • 26 slides

4.8 Huffman Codes

4.8 Huffman Codes. These lecture slides are supplied by Mathijs de Weerd. Data Compression. Q. Given a text that uses 32 symbols (26 different letters, space, and some punctuation characters), how can we encode this text in bits ?

285 views • 26 slides

4.8 Huffman Codes

4.8 Huffman Codes. These lecture slides are supplied by Mathijs de Weerd. Data Compression. Q. Given a text that uses 32 symbols (26 different letters, space, and some punctuation characters), how can we encode this text in bits?

251 views • 25 slides

4.8 Huffman Codes

4.8 Huffman Codes. These lecture slides are supplied by Mathijs de Weerd. Data Compression. Q. Given a text that uses 32 symbols (26 different letters, space, and some punctuation characters), how can we encode this text in bits?

367 views • 24 slides

Rooted tree and binary tree

Rooted tree and binary tree Theorem 5.19: A full binary tree with t leaves contains i=t-1 internal vertices.

387 views • 23 slides

Chapter 10

Chapter 10. Fundamental limits in Information Theory. Chapter 10 :. Lecture edition by K.Heikkinen. Chapter 10 goals. To understand limits of information theory To learn the meaning of entropy and channel capacity To understand coding theorems and their principles

532 views • 21 slides

RAIK 283 Data Structures & Algorithms

RAIK 283 Data Structures & Algorithms. Huffman Coding Dr. Ying Lu ylu@cse.unl.edu. RAIK 283 Data Structures & Algorithms. Giving credit where credit is due: Most of slides for this lecture are based on slides created by Dr. Richard Anderson, University of Washington.

312 views • 15 slides