1. UNIT I

2. Entropy and Uncertainty Entropy is the irreducible complexity below which a signal cannot be compressed. Entropy is the irreducible complexity below which a signal cannot be compressed. Capacity is the ultimate transmission rate for reliable communication over noisy channel. Capacity is the ultimate transmission rate for reliable communication over noisy channel. Entropy: The probabilistic behavior of a source of information. Entropy: The probabilistic behavior of a source of information. K-1 K-1 H(X) = ∑ pk log2(1/pk) H(X) = ∑ pk log2(1/pk) k=0 k=0 Capacity: Intrinsic ability of a channel to convey information. Capacity: Intrinsic ability of a channel to convey information. Information: Measure of uncertainty of an event. Information: Measure of uncertainty of an event. I k = log2(1/pk)

3. Source coding Theorem Given a discrete memoryless source of entropy H(X). The average length of code word L for any distortionless source-encoding scheme is bounded as L >= H(X). Given a discrete memoryless source of entropy H(X). The average length of code word L for any distortionless source-encoding scheme is bounded as L >= H(X). According to the theorem, the entropy H(X) represents a fundamental limit on L. Therefore, According to the theorem, the entropy H(X) represents a fundamental limit on L. Therefore, Lmin = H(X).

4. Huffman coding Definition: A minimal variable-length character coding based on the frequency of each character. Definition: A minimal variable-length character coding based on the frequency of each character. The Huffman coding algorithm proceeds as follows The Huffman coding algorithm proceeds as follows The source symbols are listed in order of decreasing probability. The two source symbols of lowest probability are assigned a0 and a1. The source symbols are listed in order of decreasing probability. The two source symbols of lowest probability are assigned a0 and a1. These two symbols are regarded as being combined into a new source symbol with probability equal to the sum of the two original probabilities. These two symbols are regarded as being combined into a new source symbol with probability equal to the sum of the two original probabilities. The procedure is repeated until we are left with final list of source statistics on only two for which a0 and a1 are assigned. The procedure is repeated until we are left with final list of source statistics on only two for which a0 and a1 are assigned.

5. Huffman coding An example of the Huffman encoding algorithm.

6. Shannon Fano coding Definition: A variable-length coding based on the frequency of occurrence of each character. Divide the characters into two sets with the frequency of each set as close to half as possible, and assign the sets either 0 or 1 coding. Repeatedly divide the sets until each character has a unique coding. Definition: A variable-length coding based on the frequency of occurrence of each character. Divide the characters into two sets with the frequency of each set as close to half as possible, and assign the sets either 0 or 1 coding. Repeatedly divide the sets until each character has a unique coding.sets Note: Shannon-Fano is a minimal prefix code. Huffman is optimal for character coding (one character-one code word) and simple to program. Arithmetic coding is better still, since it can allocate fractional bits, but is more complicated and has patents. Note: Shannon-Fano is a minimal prefix code. Huffman is optimal for character coding (one character-one code word) and simple to program. Arithmetic coding is better still, since it can allocate fractional bits, but is more complicated and has patents.

7. Discrete Memory less channels When the probabilities of selection of successive events are independent, then the source is said to be discrete memoryless source or zero memory source.

8. Channel capacity Channel capacity of a discrete memoryless channel is the maximum mutual information I(X,Y) in any single use of channel, where the maximization is overall possible input probability distributions { p(x j)} on x. C = max I(X,Y)

9. Channel coding Theorem Let a discrete memoryless source with an alphabet x have entropy H(X) and produces symbols every Ts seconds. Let a discrete memory less channel have capacity C and be used every Tc seconds, then if there exist a coding scheme for which the source information can be transmitted over the channel and be reconstructed at the receiver with very small probability of error.

10. Channel capacity Theorem Shannon’s information capacity theorem states that the channel capacity of a continuous channel of bandwidth W Hz, perturbed by bandlimited Gaussian noise of power spectral density n0 /2, is given by Cc = W log2(1 + SN) bits/s (32.1) where S is the average transmitted signal power and the average noise power is where S is the average transmitted signal power and the average noise power is W N =∫ n0/2 dw = n0W (32.2) N =∫ n0/2 dw = n0W (32.2) -W -W