This document discusses data compression techniques including lossless compression methods like run-length encoding and statistical compression, as well as lossy compression methods like JPEG and MPEG. It also covers recent developments in data compression technology and the use of neural networks for image compression.

1. DATA
COMPRESSION
Prepared by – JAYPAL SINGH CHOUDHARY
SOURABH JAIN
Graphics from - http://plus.maths.org/issue23/features/data/data.jpg

2. Why Data Compression
 Definition:
Reducing the amount of data required to
represent a source of information.
Preserve the output data original to
the input as much as possible.
 Objectives:
Reduce the space required for the data
storage.
Also reduce the time of data transmission
over network.
SOURCES - www.data-compression.com/index.shtml

3. Types of Compression
 Lossless compression.
 Lossy compression.
Basic principle of both :
Graphics from - http://img.zdnet.com/techDirectory/LOSSY.GIF

4. Lossless Compression
In this the compressing and
decompressing algorithms are
inverse of each other.
TECHNIQUES :
 Run-Length Encoding.
When data contains repeated strings then these
can be replaced by special marker.
original data compressed data
Sources- www.data-compression.com/lossless.shtml
572744444444321333333333335278222222 5727#408321#3115278#206

5. Lossless (contd.)
 Statistical compression:
In this the short codes are used for
frequent symbols and long for infrequent.
Three common principles are :-
1. Morse code.
2. Huffman encoding.
3. Lempel- Ziv -Welch encoding.
 Relative compression:
Extremely useful for sending
video, commercial TVs and30 frames in
every second.
References - www.data-compression.com/lossless.shtml

6. Lossy compression
 Some data in output is lost but not
detected by users.
 Mostly used for pictures, videos and
sounds.
Basic techniques are :
1. JPEG
2. MPEG
Referenced -http://searchciomidmarket.techtarget.com/sDefinition/0,,sid183_gci214453,00.html
Transformation
Quantisation
Encoding
decompress
compress

7. Latest Developments
 Fathom 3.0
Developed by Inlet technologies in
cooperation with Microsoft and Scientific
Atlanta.
Work with media files for
mobiles, portable, web and high
definition.

8. Histor􀁜
A literature compendium for a large variety of
Audiocoding systems was published in the IEEE
Journal on Selected Areas in Communications
(JSAC), February 1988. While there were
some papers from before that time, this
Collection documented an entire variety of
finished, working audio coders, nearly all of
them using perceptual (i.e. masking)
Techniquce and some kind of frequency
analysis and back End noiseless coding.

9. Image Compression
Using Neural Networks
Overview :
- Introduction to neural networks.
Back Propagated (BP) neural
network.
- Image compression using BP
neural
network.
- Comparison with existing image
compression techniques

10. Image Compression using BP
Neural Network
- Future of Image
Coding(analogous to
Our visual system).
- Narrow Channel K-L.
transform .
- The entropy coding of
the state vector h i's
at the hidden Layer.

11. Image Compression
using continued…
- A set of image samples is used to
train the network.
- This is equivalent to compressing
the input into the narrow channel
and then reconstructing the input
from the hidden layer.

12. - The image to be subdivided into
non-overlapping blocks of n x n
pixels each. Such block
represents N-dimensional vector
x, N = n x n, in N-dimensional
space. Transformation process
maps this set of vectors into y=W
(input)
output=W-1y
Transform coding with
multilayer Neural Network:

13. Image Compression
continued…
The inverse transformation need to
reconstruct original image with
minimum ofdistortions.

14. Proposed Method:
- Wavelet packet decomposition.
- Quantization.
- Organization of vectors.
- Neural network approximation.
- Lossless encoding and reduction

15. Wavelet Packet Decomposition
The image is first put through a few
levels ofwavelet packet decomposition.

16. Quantization
- Each of the decomposed wavelet
sections is divided by the quantization
value and rounded to the nearest
integer.
- This creates redundancy in the data
which is easier to work with.
- Quantization is not lossless.

17. Neural Network Approximation
-An example of the vector with the trained
Neural network attempting to fit it.

18. Lossless Encoding and
Reduction
- The entire data stream is then run-
lengthencoded (RLE).
- Afterwards, we can save the data
using the ZIP file format, which
applies some other lossless encoding
methods.

19. Conclusion
- Neural networks can be used to compress
images!
- However, they are probably not the
best way to go unless the data can be
represented in some easier way.
- Most of the compression came from the
quantization, organization, and
Lossless compression stages.

20. References
1. http://en.wikipedia.org/wiki/Data_comp
ression
2. http://en.wikipedia.org/wiki/Lossless_d
ata_compression
3. http://en.wikibooks.org/wiki/Data_Codi
ng_Theory/Data_Compression
4. http://en.wikibooks.org/wiki/Data_Com
pression
5. http://datacompression.dogma.net/inde
x.php?title=Comp.compression_FAQ

21. Annotated Bibliography
 I choose the text from –
www.data-compression.com/index.shtml
www.data-compression.com/lossless.shtml
http://searchciomidmarket.techtarget.com/sDefinition/0,,sid183_gci214453
,00.html
http://localtechwire.com/business/local_tech_wire/wire/story/1276887
http://www.futureofgadgets.com/futureblogger/show/1730
because it fulfills mine requirement for the topic.
 I choose the graphics from –
http://img.zdnet.com/techDirectory/LOSSY.GIF
http://plus.maths.org/issue23/features/data/data.jpg
because it clears the situation which I want to explain.