new generation gadget for dumb using embedded interface.ppt

7/28/2019 New Generation Gadget for Dumb Using Embedded Interface.ppt

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Lossless Data

Compression andDecompression using

Huffman Algorithm

First ReviewProject guide,

MR. DHANASEKER

Project of,

Amanda Maricia Raphael


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Abstract

Data compression has a wide

application in computer software andcommunication systems.

compression includes lossless andlossy.

In this project lossless data

compression is done using Huffmanalgorithm.

And its Hardware implementation is

done using VHDL language.


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Objective...

A Hardware implementation ofLosseless data compression anddecompression using VHDLLanguage


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Existing model..

Shannon-Fano Coding

First sort all the symbols in nonincreasing frequency

order.

Then split this list into two suchthat probability of one list is half

When the split is done, prefix allthe codes in the first list with 0and the second list with 1.


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Disadvantages of Existingmodel...

not always possible to be sure,because we confuse how tosplit the list.

do not always give the optimalcode


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Lossless Data Compression

Lossless compression techniques

reconstruct the original data from thecompressed file without any loss ofdata

i.e. the information does not changeor loose during the compression anddecompression of data.

Also called reversiblecompression coz the originalmessage is reconstructed by the

decompression process [


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Proposed by David A. HuffmanUsed for the Construction ofMinimum Redundancy Codes.

It is a lossless data compressionalgorithm based on variable-lengthcode table.

The higher probability of symboloccurrence, the shorter code wordlength will be used and length of a

whole message shorter

Huffman coding


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Huffman Algorithm

The Huffman algorithm works by

creating a binary tree.

The length of the binary treedepends on the number of

symbols.

A node of tree can be either a leafnode or an internal node.


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Process of creating a binarytree

Step 1: List the source symbols indescending order of theirprobabilities of occurrence.

Step 2: Form a compound symbolwith the two symbols.

The combination of the twosymbols forms a branch in the tree.


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This step is repeated using twosymbols with the lowest possibilityfrom the new set of symbols until all

the original symbols have beencombined into a single compoundsymbol.

Step 3: A tree is formed, with the topand bottom stems going from thecompound symbol to the symbolswhich form it, labeled with 0 and 1


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Examplemsg- ABRACADABAR

symbol occurence probability codeword

A 5 5/11 0

B 2 2/11 10

R 2 2/11 110

C 1 1/11 1110

D 1 1/11 1111

sum 11


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calculation..

Avg search length

Avg L = Li * P (i)

=5/11*1+2/11*3+2/11*2+1/11*4+1/11*4=23/11 = 2.090

Space required to store theoriginal message=11*8=88 bits

Space required to store thedecoded message=23 bits


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Literature Survey...

1.Data Compression Scheme OfDynamic Huffman code for DifferentLanguages

By

Shivani Pathak ,Smita SinghThis paper paper examines lossless data

compression on languages and compares theperformance based on huffman coding


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2.Implementation and Verification ofHuffman Coding System

by

Shiliang Lu, Qiyun Zhang, andCheng Li

In this paper, we discuss the hardware

implementation of the Huffman coding usingVHDL language.


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3.A NEW LOSSLESS METHOD OF IMAGECOMPRESSION AND

DECOMPRESSION USING HUFFMANCODING TECHNIQUES

1JAGADISH H. PUJAR, 2LOHIT

In this paper we proposed the Losslessmethod of image compression and

decompression using a simple codingtechnique called Huffman coding. Thistechnique is simple in implementation andutilizes less memory. A software algorithm

has been developed and implemented tocompress and decompress the given imageusing Huffman coding techniques in aMATLAB platform.


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4.LOSSLESS HUFFMAN CODINGTECHNIQUE FOR IMAGE COMPRESSIONAND RECONSTRUCTION USING BINARYTREES

1Mridul Kumar Mathur, 2Seema Loonker,

3Dr. Dheeraj SaxenaIn this paper we have converted an image

into an array using Delphi image control tool.

to implement Huffman coding method thatremoves redundant codes from the imageand compresses


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Reference

[1] T. Bell, J. G. Clearly and I. H. Witten, TextCompression. Prentice Hall Reference Series, 1990.

[2] D. A. Huffman, A Method for the Construction of

Minimum Redundancy Codes, Proceedings of the

Institute of Radio Engineers, n. 40, pp. 1098-1101, 1952.

[3] C. E. Shannon, C. E. A Mathematical Theory of

Communication, Bell Syst. Tech. J., pp. 379-423, 1948.

[4] S. H. Sun and S.J. Lee. A JPEG Chip for Image

Compression and Decompression, Journal of VLSI

Signal Processing, Kluwer Academic Publishers, pp. 43

60, August 2003.


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new generation gadget for dumb using embedded interface.ppt

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