Understanding Compression. Data Compression for Modern Developers - Helion

ebook

Autor: Colt McAnlis, Aleks Haecky
ISBN: 978-14-919-6148-3
stron: 242, Format: ebook
Data wydania: 2016-07-13
Księgarnia: Helion

Cena książki: 118,15 zł (poprzednio: 137,38 zł)
Oszczędzasz: 14% (-19,23 zł)

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Tagi: Analiza danych | Inne - Programowanie

If you want to attract and retain users in the booming mobile services market, you need a quick-loading app that won’t churn through their data plans. The key is to compress multimedia and other data into smaller files, but finding the right method is tricky. This witty book helps you understand how data compression algorithms work—in theory and practice—so you can choose the best solution among all the available compression tools.

With tables, diagrams, games, and as little math as possible, authors Colt McAnlis and Aleks Haecky neatly explain the fundamentals. Learn how compressed files are better, cheaper, and faster to distribute and consume, and how they’ll give you a competitive edge.

Learn why compression has become crucial as data production continues to skyrocket
Know your data, circumstances, and algorithm options when choosing compression tools
Explore variable-length codes, statistical compression, arithmetic numerical coding, dictionary encodings, and context modeling
Examine tradeoffs between file size and quality when choosing image compressors
Learn ways to compress client- and server-generated data objects
Meet the inventors and visionaries who created data compression algorithms

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Spis treści

Understanding Compression. Data Compression for Modern Developers eBook -- spis treści

Foreword
Preface
- How to Read This Book
- How to Read This Book Backwards
- Chapter Synopsis
1. Lets Not Be Boring
- The Five Buckets of Compression Algorithms
- Claude Shannon Is Infuriating!
- The Only Thing You Need to Know about Data Compression
  - A World Built on Data Compression
    - Music compression
    - Image compression
    - Video compression
    - Genome mapping
    - Compression and the economy
2. Do Not Skip This Chapter
- Understanding Binary
  - Base 10 System
  - Binary Number System
    - Converting from binary to decimal
    - Converting from decimal to binary
- Information Theory
  - An Excursion into Binary Search
  - Entropy: The Minimum Bits Needed to Represent a Number
  - Standard Number Lengths
3. Breaking Entropy
- Understanding Entropy
- What This Entropy Stuff Is Good For
- Understanding Probability
- Breaking Entropy
  - Example 1: Delta Coding
  - Example 2: Symbol Grouping
  - Example 3: Permutations
- Information Theory Versus Data Compression
4. Variable-Length Codes
- Morse Code
- Probability, Entropy, and Codeword Size
- Variable-Length Codes
  - Using VLCs
    - Calculating symbol probabilities
    - Assigning codewords to symbols
    - Encoding
    - Decoding
  - Creating VLCs
    - The prefix property
  - A Handful of Example VLCs
    - Binary code
    - Unary codes
    - Elias gamma encoding
    - Elias delta coding
    - And so many more!
  - Finding the Right Code for Your Data Set
5. Statistical Encoding
- Statistically Compressing to Entropy
- Huffman Coding
  - Building a Huffman Tree
  - Generating Codewords
  - Encoding and Decoding
  - Practical Implementations
- Arithmetic Coding
  - Finding the Right Number
  - Encoding
  - Picking the Right Output Value
  - Decoding
  - Practical Implementations
- Asymmetric Numeral Systems
  - Encoding and Decoding Using a Transform Table
  - Creating the Reference Table
    - Choosing a maxVal
  - Using ANS for Compression
  - Decoding Example
  - So Where Does the Compression Come From?
- Practical Compression: Which Statistical Algorithm Do I Choose?
6. Adaptive Statistical Encoding
- Locality Matters for Entropy
- Adaptive VLC Encoding
  - Dynamically Building a VLC Table
    - Decoding
  - Literals
  - Resets
  - Knowing When to Reset
  - Using This in Practice
- Adaptive Arithmetic Coding
- Adaptive Huffman Coding
- The Modern Choice
7. Dictionary Transforms
- A Basic Dictionary Transform
  - Finding the Right Words
- The Lempel-Ziv Algorithm
  - How LZ Works
    - The search buffer
    - Finding matches
    - The sliding window
    - Marking a match with a token
    - When no match is found
  - Encoding
  - Decoding
  - Compressing LZ output
    - Offsets
    - Lengths
    - Literals
  - LZ Variants
    - LZ77
    - LZSS
    - LZ78 or LZ2
    - LZW (LempelZivWelch)
- Collect Them All!
8. Contextual Data Transforms
- Run-Length Encoding
  - Dealing with Short Runs
  - Compressing
- Delta Coding
  - XOR Delta Coding
  - Frame of Reference Delta Coding
  - Patched Frame of Reference Delta Coding
    - Finding b
    - What do we do with exceptions?
  - Compressing Delta-Encoded Data
  - Does It Work on Text?
- Move-to-Front Coding
  - Avoiding Rogue Symbols
    - Move-ahead-k
    - Wait-c-and-move
  - Compressing MTF
- BurrowsWheeler Transform
  - Ordering Is Important!
  - How BWT Works
  - Inverse BWT
  - Practical Implementations
  - Compressing BWT
    - Why not RLE?
    - Why not LZ?
9. Data Modeling
- The Chains of Markov
  - Markov and Compression
    - Encoding
    - Decoding
    - Compression
  - Practical Implementations
- Prediction by Partial Matching
  - The Search Trie
  - Compressing a Symbol
  - Choosing a Sensible N Value
  - Dealing with Unknown Symbols
- Context Mixing
  - Types of Models
  - Types of Mixing
- The Next Big Thing?
10. Switching Gears
- Media-Specific Compression
- General-Purpose Compression
- Compression in Practice
11. Evaluating Compression
- Compression Usage Scenarios
  - Compressed Offline, Decompressed On-Client
  - Compressed On-Client, Decompressed In-Cloud
  - Compressed In-Cloud, Decompressed On-Client
    - Dynamic data that is generated by the cloud resource
    - Large data thats passed off to the cloud for efficient computing
  - Compressed On-Client, Decompressed On-Client
- Compression Need
- Compression Ratio
- Compression Performance
- Decompression Performance
- Ability to Decode-Stream
- Comparing Compressors
12. Compressing Image Data Types
- Understanding Quality Versus File Size
  - What Reduces Image Quality?
  - Measuring Image Quality
  - Making This Work
- Image Dimensions Are Important
- Choosing the Correct Image Format
  - PNG
  - JPG
  - GIF
  - WebP
  - And Now for Choosing...
- GPU Texture Formats
- Vector Formats
- Eyes on the Prize
13. Serialized Data
- Understanding Common Use Cases
  - Dynamically Server-Built Data
  - Statically Built Server-Owned Data
  - Dynamically Client-Built Data
  - Statically Client-Owned Data
- Issues with Serialized Formats
  - Human-Readable Text
  - Slow Decode Times
- Smaller Serialized Data
  - Use a Binary Serialization Format
  - Restructure Lists for Better Compression
  - Organize for Efficient Fetching
  - Segment Out Data into the Proper Compression Format
14. Lossy Data Compression
15. Making the World a Little Smaller
- Data Compression and You
- Data Compression and the Bottom Line
  - User Acquisition and Retention
  - Running Costs
  - Planning Ahead
- Making Your Users Lives a Little More Magical and Less Expensive
- Thinking About Whats Next in Technology
  - The Next Five Billion Users
  - Mobile Networks
- ...Starting Now
Glossary of Compression Words
Index