Data Compression


Criteria

Survey Formats

Basics

Compression Methods

Data Formats


Huffman Code

Example

Characteristics

Variants

Dynamic Huffman Code

Adaptive Huffman Code

Initialization

Standard Distribution

Uniform Distribution

Extension for New Symbols

Pros and Cons

Algorithm

Example


Glossary

Index


Download


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Pros and Cons


static dynamic adaptive extension
standard or uniform distribution distribution explicitly investigated control character for new symbols
Header data
Distribution located at encoder and decoder as well. Distribution has to be transported within the header. No additional data necessary.
Compression efficiency at the beginning
High; provided that the assumed distribution matches the real data. High; provided that the initial distribution does not differ from the entire set of data. Low; because all symbols has to be introduced by the control character.
Tendency of the compression efficiency in the course of the process
Constant; provided that the assumed distribution matches the real data. High; provided that no strong deviations from the chosen distribution appear locally. High; with progressive coding the compression efficiency improves continuously in contrast to the other algorithms.
Compression efficiency for larger amounts of data
Depending on the accordance with the standard distribution. Depending on the accordance with the chosen distribution. High; because the current code tree is adapted to the local distribution.
Run-time
Fast; because no code tree has to be generated. Fast; because the code tree has to be generated only once (or periodically). Slow; because the code tree has to be adapted for each symbol.

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Initialization Extension for New Symbols Algorithm Adaptive Huffman Coding