LOSSLESS DATA COMPRESSION ALGORITHMS
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PRESENTED BY:
NAVEEN KUMAR.V
CHETAN KUMAR.N

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INTRODUCTION:
Def: The main problem is that this “great idea requires too many bits. In fact there exist many coding techniques that will effectively reduce the total number of bits needed to represent the above information. In this lossless data compression algorithms we perform that compression of the data, and also it involves that no distortion of the original signal once it is decompressed or reconstituted. So much data will exist, in archives and elsewhere, that it has become critical to compress this information. Lossless compression is one way to proceed.
Generally compression in the sense encoding the data, i.e., reducing the data. Compression is performed by an encoder and decompressions performed by the decoder. We call the output of the encoder Codes or code works. The intermediate medium could either be data storage or a communication/computer network. If the compression and decompression procedure induces no information loss, the compression scheme is lossless; other it is lossy.
Here the data will be compressed by using three coding techniques. They are run-length coding, variable -length coding, fixed-length coding. Run length coding is simplest form of data compression. In the variable-length coding we are using Shannon-fano algorithm and haffman coding algorithm.Shannon-fano algorithm is a top down approach, and the haffman coding algorithm is a bottom up approach.In the fixed-Length coding we are using the dictionary-based coding algorithm.
The emergency of multimedia technologies has made digital libraries a reality.noe a day’s libraries, museums, film studios and government are converting more and more data archives into digital form. Some of the data indeed to be stored without any loss.
If the total number of bits required to represent the data before compression is Bo and the total number of bits required to represent the data after compression is b1. Then we define the compression ratio as
Compression ratio=Bo\B1
Basics of information theory: According to the famous scientist Claude e.shannon,of bell labs,the entropy n of an information source with alphabets S={s1,s2……………….sn} is defined as:
n=H(s)=£n I=1 Pi LOG²1\PI
=-£ n I=1 Pi LOG²PI
Where pi is the probability that symbol si in S will occur.
ENTROPY: Entropy is measure of the disorder of a system.
There are three types of coding methods. Those are
1. run length coding
2. variable length coding
3. fixed length coding
Run length coding is simplest form of data compression. In the variable-length coding we are using Shannon-fano algorithm and haffman coding algorithm.Shannon-fano algorithm is a top down approach, and the haffman coding algorithm is a bottom up approach.In the fixed-Length coding we are using the dictionary-based coding algorithm.
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