
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, ratedistortion
theory, a discussion of the gap between theory and practice, and
Blahut algorithm. A list of recommended
books on datacompression
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 LempelZiv coding (including an animation of the Huffman design
algorithm and an animation of the LempelZiv
encoding). A performance comparison is also included.
Vector Quantization:
 Description of the Linde Buzo Gray vector quantizer (VQ) design algorithm. Includes a twodimensional animation
of the LBGVQ 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 grayscaled 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.
