Comm. 502: Communication Theory. Lecture 6. - Introduction to Source Coding

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1 Comm. 50: Communication Theory Lecture 6 - Introduction to Source Coding

2 Digital Communication Systems Source of Information User of Information Source Encoder Source Decoder Channel Encoder Channel Decoder Modulator De-Modulator Channel Communication systems are designed to transmit the information generated by a source to some destination.

3 Types of Information Sources Types of Information Sources Analog Discrete Analog : The output is analog signals (examples: TV and Radio broadcasting). Discrete : The output is discrete (a sequence of letters or symbols (example: Computers, storage devices, ).

4 Source Encoder Whether a source is analog or discrete, a digital communication system is designed to transmit information in digital form. Consequently, the output of the source must be converted to a format that can be transmitted digitally. This conversion of the source output to a digital form is generally performed by the source encoder, whose output is a sequence of binary digits. Ex: ASCII code which converts characters to binary bits.

5 Why Source Coding is Important It enables us to determine: - The amount of information from a given source. - The minimum storage and bandwidth needed to transfer data from a given source. - The limit on the transmission rate of information over a noisy channel? - Data compression.

6 Fixed Length Codes The standard character codes are of fixed length such as 5, 6, or 7 bits. The length is usually chosen so that there are enough binary characters to assign a unique binary sequence to each input alphabet character. Fixed length codes have the property that character boundaries are separated by a fixed bit count. Example: This allows the conversion of a serial data stream to a parallel data stream by a simple bit counter.

7 Variable Length Codes Data compression codes are often variable length codes. We expect that the length of a binary sequence assigned to each alphabet symbol is inversely related to the probability of that symbol. A significant amount of data compression can be realized when there is a wide differences in probabilities of the symbols. To achieve this compression, there must also be a sufficiently large number of symbols.

8 Discrete Information Source Information Source Assume S Pr S S k pk Information Source Generates a group of symbols from a given alphabet S Each symbol has a probability: p k o, S 1,..., SK 1 - Symbols are independent S Information Source s, s,..., s 0 1 K 1 Pr s p,k 0, 1,...,K 1 k k K-1 k 0 p k 1

9 If =1: Measure of Information - The occurrence of the event does not correspond to any gain of information (i.e there is no uncertainty). In this case, there is no need for communications because the receiver knows everything. As p k p k decreases, The uncertainty increases The reception of s k corresponds to some gain in information. BUT HOW MUCH?

10 Measure of Information (Cont.) Information is measured by: (1) Self information () Entropy We use the probability theory to quantify and measure information.

11 (1) Self Information The amount of information in bits about a symbol is closely related to its probability of occurrence. A function which measures the amount of information after observing the symbol sk is the self-information: I ( s k ) 1 I( sk ) log log p p k k [bits] 0 1 P S k

12 Properties of Self Information Properties of I(s): I( s k ) 1) I(s) 0 (a real nonnegative measure). 0 1 P S k ) I( S k ) I( S ) i if P k P i 3) I(s) is a continuous function of p.

13 () Entropy Entropy is the average amount of information of a finite discrete source, More precisely, it is the average number of bits per symbol required to describe that source. For a source containing N independent symbols, its entropy is defined as H Since Then E( I( S N i1 H H i1 Unit of entropy: bits/symbol (Infor. bits/symb) p i I( S i i )) I( S i ) log N i1 N ) p i log ( p i ) ( p p i log ( p i i ) )

14 H is a Properties of Entropy positive quantity H 0 The unit bit is a measure of information content and is not to be confused with the term bit meaning binary digit. If all a priori probabilities are equally likely ( P i 1/ N for all N symbols) then the entropy is maximum and given by: H log N Then 0 H log N

15 0 H log N If p i H all a p N) N (1/ N) p Proof 1/ N for all symbols N i1 (1/ log log priori probabilities i log N i1 (1/ N)[ N ( i log log ) (1/ N) (1/ N)] are equally likely

16 Example A source puts out one of five possible messages during each message interval. The probs. of these messages are p 1 = 1 ; p = 1 ; 4 p 3 = 1 : p 4 = 1 1, p 5 = What is the information content of these messages? I (m 1 ) = - log = 1 bit I (m ) = - log = bits I (m 3 ) = - log = 3 bits I (m 4 ) = - log = 4 bits I (m 5 ) = - log 1 16 = 4 bits

17 Entropy Example Find and plot the entropy of the binary code in which the probability of occurrence for the symbol 1 is p and for the symbol 0 is 1-p H i1 P p log i log p P i 1 plog 1 p 1 H p v logv 0 as v 0 0 1/ 1 p p H log log 1bit/symbol p H log log bits/symbol H 0 bit/symbol; p 1 H 0 bit/symbol

18 Average Information Content in English Language Calculate the average information in bits/character in English assuming each letter is equally likely Since characters do not appear with the same frequency in English, use the probabilities P=0.70 for a,e,o,t P=0. for h,i,n,r,s P=0.08 for c,d,f,l,m,p,u,y P=0.0 for b,g,j,k,q,v,w,x,z H log 6 6 i1 4.7 bits / char Solve this problem

19 Objective Source Coding Efficient representation of data generated by an information source What Does the word EFFIECIENT Mean? Efficient Source Coding means: - Minimum average number of bits per source symbol

20 How could we be EFFICIENT in Source Coding? By using knowledge of the statistics of the source Clearly: Frequent source symbols should be assigned SHORT CODEWORDS Rare source symbols should be assigned LONGER CODEWORDS Example Morse Code E is represented by:. Q is represented by:.

21 Morse Code Morse code is a method of transmitting text information as a series of on-off tones, lights, or clicks that can be directly understood by a skilled listener or observer without special equipment. The letters is transmitted as standardized sequences of short and long signals called "dots" and "dashes. The duration of a dash is three times the duration of a dot. Each dot or dash is followed by a short silence, equal to the dot duration.

22 Morse Code The letters of a word are separated by a space equal to three dots (one dash), and the words are separated by a space equal to seven dots. The dot duration is the basic unit of time measurement in code transmission. For efficiency, the length of each character in Morse is approximately inversely proportional to its frequency of occurrence in English. Thus, the most common letter in English, the letter "E," has the shortest code, a single dot.

23 Morse Code

24 Average Code Length Information Source s k Source Encoder c k Source has K symbols Each symbol s k has probability p k Each symbol s k is represented by a codeword c k of length v k bits Average codeword length Variance of the code length: L K k1 K k1 p k v k p k ( v k L)

25 Example: Average Codeword Length L L = 0.5() +0.30() + 0.1(3) +0.15(3) () =.7 bits It does not mean that we have to find a way to transmit a noninteger number of bits. Rather, it means that on the average the length of the code is.7 bits. K k1 p k v k Symbol p (S) Code A B C D E Calculate the variance of the code length

26 Code Efficiency L represents the average number of bits per source symbol used in the source encoding process. L min If denotes the minimum possible codeword length, the coding efficiency of the source encoder is defined as L min An efficient code means η1 What is L min? L

27 Shannon s First Theorem: The Source Coding Theorem L HS L min = H(S) The outputs of an information source cannot be represented by a source code whose average length is less than the source entropy

28 Compression Ratio We define the compression ratio as: CR Number of bits of the fixed code that represents the symbols Average code length of the variable length code We define the code efficiency as: Code efficiency Entropy Average code length H( S) L L L min It measures how much the code achieves from the possible compression ratio.

29 Source Symbols k Symbol Probability p k Example Symbol Code word Code I Code word Length Symbol Code word Code II Code word Length c k v k c k v k s 0 1/ s 1 1/ s 1/ s 3 1/ Source Entropy: H i1 H(S) =1/log ()+1/4log (4)+ 1/8log (8)+1/log (8)= 1.75 bits/symbol= Lmin CR 1 CR Code I L N p i log (1/ p i L ) K p k v k Code II k L H ( S) bits L

Entropy, Coding and Data Compression

Entropy, Coding and Data Compression Data vs. Information yes, not, yes, yes, not not In ASCII, each item is 3 8 = 24 bits of data But if the only possible answers are yes and not, there is only one bit