new generation gadget for dumb using embedded interface.ppt
TRANSCRIPT
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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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Queries????