The goal of data compression is to represent an information source (e.g. a data file, a speech signal, an image, or a video signal) as accurately as possible using the fewest number of bits.

Theory of Data Compression: 

This page includes an overview of the theory, source modeling (including a statistical study of English text), entropy rate, Shannon lossless source coding theorem, rate-distortion theory, a discussion of the gap between theory and practice, and Blahut algorithm.  A list of recommended books on data-compression theory and some seminal papers (e.g., Shannon's 1948 paper A Mathematical Theory of Communications) are also included.

Lossless Data Compression: 

Description of Huffman coding and Lempel-Ziv coding (including an animation of the Huffman design algorithm and an animation of the Lempel-Ziv encoding). A performance comparison is also included.

Vector Quantization: 

Description of the Linde Buzo Gray vector quantizer (VQ) design algorithm. Includes a two-dimensional animation of the LBG-VQ design algorithm. 

Speech Compression: 

Description of the LPC model, LPC  vocoder, CELP coder, and ADPCM coder.  A comparison of these coders and references are also included.

Image Compression: 

A demonstration of JPEG/JPEG2000 compression of color and gray-scaled images. 

Download: 

Key papers on data compression and various source code (e.g., vector quantizer design using the LBG algorithm) are available.

Links: 

Links to web sites relating to data compression. You many add your link if you wish.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Nam Phamdo
Department of Electrical and Computer Engineering
State University of New York
Stony Brook, NY 11794-2350
phamdo@ieee.org

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