Summary
The I Shannon Theorem states how much is the compression limit that a code can reach without introducing distorsion (Distorsion-less Coding).
The minimum average number of binary simbols to represent an arbitrary source symbol (average length of the code words) cannot be smaller than the source entropy.
Let S be a DMS with source alphabet A={a1,a2,…,aN}
According to Large Number Law for a symbol sequence of length n enough large (n→∞) one has:
Pr (ai)
Definition
The typical sequences have the same probabilities equal to 2-nH(X)
We will need only Nτ=2nH(X) codewords, namely, if we utilize binary codes nH(X) binary symbolsfor any
sequence of n source symbols.
Resource saving!!!
The saving is larger and larger whenever H(X) is smaller and smaller than log N !!!!
What happens if the source outpot is a non-typical sequence ?
No codeword has been assigned!!!
An error occurs, namely a distorsion!!!!
For n→∞ such an event will occur with null probability
Such a coding is asymptotically ( n→∞) distortion-less.
Statement
A memory-less source with rate H(X) can be coded with arbitrary small distorsion if n→∞ with Coding Rate (bits /source symbol) such that:
R≥H(X)
On the contrary if R<H(X) asymptotically null distorsion.
The I Shannon Theorem is an existence theorem: it just assures us that if we need a code with coding rate R≥H(X), it does exist.
Huffman Algorithm
Assumption: we know the probabilities pi of DMS symbols ai (Entropic Coding).
The Huffman code is a code block/variable length.
The codewords have different lengths :
Procedure
If n=2 we have 32=9 source symbols with n probabilities pi=1/9
If we built a Huffman code one has that
The Huffman coding requiresthe knowledge of the symbol probabilities.
The Lempel-Ziv coding does not requiresuch such knowledge.
The procedure is autolearning: it is a universal coding.
The Lempel-Ziv coding is a variable length /fixed length code.
It can be shown that for a stationary and ergodig source.
The number of bits for n→∞ is equal to n H(X).
nH(X) < n because H(X) < 1
Problems
It is widely utilized to compress files for computers, routine under UNIX such as ZIP, ZOO, LZH,ARJ etc.
Compression ratio:
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8. Noncoherent ML Demodulation of FSK signals in AWGN
9. Transmission through bandlimited AWGN channels
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