Aufgaben:Exercise 1.4Z: Entropy of the AMI Code: Difference between revisions

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In this task, the decision content  $H_0$  and the resulting entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  are to be determined for the three parameter sets mentioned above.  The relative redundancy of the code sequence results from this according to the equation
In this task, the decision content  $H_0$  and the resulting entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  are to be determined for the three parameter sets mentioned above.  The relative redundancy of the code sequence results from this according to the equation
:$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}
:$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$




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*In general, the following relations exist between the decision content  $H_0$,  the entropy  $H$  $($here equal to  $H_{\rm C})$  and the entropy approximations:  
*In general, the following relations exist between the decision content  $H_0$,  the entropy  $H$  $($here equal to  $H_{\rm C})$  and the entropy approximations:  
:$$H \le \ \text{...} \  \le H_3 \le H_2 \le H_1 \le H_0  
:$$H \le \ \text{...} \  \le H_3 \le H_2 \le H_1 \le H_0\hspace{0.05cm}.$$
\hspace{0.05cm}.$$
*In  [[Aufgaben:Exercise_1.4:_Entropy_Approximations_for_the_AMI_Code|Exercise 1.4]]    the entropy approximations were calculated for equally probable symbols  $\rm L$  and  $\rm H$  as follows (each in "bit/symbol"):  
*In  [[Aufgaben:Exercise_1.4:_Entropy_Approximations_for_the_AMI_Code|Exercise 1.4]]    the entropy approximations were calculated for equally probable symbols  $\rm L$  and  $\rm H$  as follows (each in "bit/symbol"):  
:$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292
:$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292\hspace{0.05cm}.$$
\hspace{0.05cm}.$$




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'''(1)'''  Since the AMI code neither adds new information nor causes information to disappear, the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  is equal to the source entropy  $H_{\rm Q}$.   
'''(1)'''  Since the AMI code neither adds new information nor causes information to disappear, the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  is equal to the source entropy  $H_{\rm Q}$.   
*Therefore, for equally probable and statistically independent source symbols, the following holds:
*Therefore, for equally probable and statistically independent source symbols, the following holds:
:$$H_{\rm Q}    {= 1 \,{\rm bit/binary \ symbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C}    \hspace{0.15cm} \underline {= 1 \,{\rm bit/ternary \ symbol}}  
:$$H_{\rm Q}    {= 1 \,{\rm bit/binary \ symbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C}    \hspace{0.15cm} \underline {= 1 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$




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'''(2)'''  The decision content of a ternary source is  $H_0 = \log_2 \; (3) = 1.585\; \rm bit/symbol$. 
'''(2)'''  The decision content of a ternary source is  $H_0 = \log_2 \; (3) = 1.585\; \rm bit/symbol$. 
* This gives the following for the relative redundancy
* This gives the following for the relative redundancy
:$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)  
:$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)\hspace{0.15cm} \underline {= 36.9  \,\%}\hspace{0.05cm}.$$
\hspace{0.15cm} \underline {= 36.9  \,\%}
\hspace{0.05cm}.$$






'''(3)'''    $H_{\rm C} = H_{\rm Q}$ is still valid.   However, because of the unequal symbol probabilities,  $H_{\rm Q}$  is now smaller:
'''(3)'''    $H_{\rm C} = H_{\rm Q}$ is still valid.   However, because of the unequal symbol probabilities,  $H_{\rm Q}$  is now smaller:
:$$H_{\rm Q}  =  \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot  
:$$H_{\rm Q}  =  \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot{\rm log}_2\hspace{0.1cm} (4/3){= 0.811 \,{\rm bit/binary \ symbol}} \hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_{\rm C}  = H_{\rm Q}  \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
{\rm log}_2\hspace{0.1cm} (4/3)
{= 0.811 \,{\rm bit/binary \ symbol}} \hspace{0.3cm}
\Rightarrow\hspace{0.3cm} H_{\rm C}  = H_{\rm Q}  \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/ternary \ symbol}}
\hspace{0.05cm}.$$




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  \hspace{0.05cm}$ now holds.
  \hspace{0.05cm}$ now holds.
*One can generalize this result.  Namely, it holds:
*One can generalize this result.  Namely, it holds:
:$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}
:$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge})  = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})\hspace{0.05cm}.$$
\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge})  = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})
\hspace{0.05cm}.$$






'''(5)'''  Since each  $\rm L$  is mapped to  $\rm N$  and  $\rm H$  is mapped alternately to  $\rm M$  and  $\rm P$, it holds that
'''(5)'''  Since each  $\rm L$  is mapped to  $\rm N$  and  $\rm H$  is mapped alternately to  $\rm M$  and  $\rm P$, it holds that
:$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}  
:$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_1  = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3)  \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
\Rightarrow\hspace{0.3cm} H_1  = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +  
2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3)  \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/ternary \ symbol}}  
\hspace{0.05cm}.$$




'''(6)'''  Now the probabilities of occurrence of the ternary symbols are    $p_{\rm N} = 3/4$  sowie  $p_{\rm P} = p_{\rm M} =1/8$.  Thus:
'''(6)'''  Now the probabilities of occurrence of the ternary symbols are    $p_{\rm N} = 3/4$  sowie  $p_{\rm P} = p_{\rm M} =1/8$.  Thus:
:$$H_1  = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +  
:$$H_1  = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8)  \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8)  \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/ternary \ symbol}}  
\hspace{0.05cm}.$$


''Interpretation:''
''Interpretation:''
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*For  $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$ , on the other hand,  $H_1 = 1.06 \; \rm bit/symbol$  results in a significantly smaller value.
*For  $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$ , on the other hand,  $H_1 = 1.06 \; \rm bit/symbol$  results in a significantly smaller value.
*For both parameter combinations, however, the same applies:
*For both parameter combinations, however, the same applies:
:$$H_0  = 1.585 \,{\rm bit/symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =  
:$$H_0  = 1.585 \,{\rm bit/symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/symbol}\hspace{0.05cm}.$$
\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/symbol}
\hspace{0.05cm}.$$
It follows from this: <br>
It follows from this: <br>
*If one considers two message sources&nbsp; $\rm Q1$&nbsp; and&nbsp; $\rm Q2$&nbsp; with the same symbol set size&nbsp; $M$ &nbsp; &#8658; &nbsp; decision content&nbsp; $H_0 = \rm const.$, whereby the first order entropy approximation&nbsp; $(H_1)$&nbsp;  is clearly greater for source&nbsp; $\rm Q1$&nbsp; than for source&nbsp; $\rm Q2$, one cannot conclude from this by any means that the entropy of&nbsp; $\rm Q1$&nbsp; is actually greater than the entropy of $\rm Q2$.&nbsp;  
*If one considers two message sources&nbsp; $\rm Q1$&nbsp; and&nbsp; $\rm Q2$&nbsp; with the same symbol set size&nbsp; $M$ &nbsp; &#8658; &nbsp; decision content&nbsp; $H_0 = \rm const.$, whereby the first order entropy approximation&nbsp; $(H_1)$&nbsp;  is clearly greater for source&nbsp; $\rm Q1$&nbsp; than for source&nbsp; $\rm Q2$, one cannot conclude from this by any means that the entropy of&nbsp; $\rm Q1$&nbsp; is actually greater than the entropy of $\rm Q2$.&nbsp;  
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[[Category:Information Theory: Exercises|^1.2 Sources with Memory^]]
[[Category:Information Theory: Exercises|^1.2 Sources with Memory^]]
[[de:Aufgaben:Aufgabe 1.4Z: Entropie der AMI-Codierung]]

Latest revision as of 17:57, 16 March 2026

Binary source signal (top) and
ternary encoder signal (bottom)

We assume similar prerequisites as in  Exercise 1.4 :  

A binary source provides the source symbol sequence  $\langle q_\nu \rangle$  with  $q_\nu \in \{ {\rm L}, {\rm H} \}$, where there are no statistical bindings between the individual sequence elements.

For the symbol probabilities, let:

  • $p_{\rm L} =p_{\rm H} = 1/2$  (in subtasks 1 und 2),
  • $p_{\rm L} = 1/4, \, p_{\rm H} = 3/4$  (subtasks 3, 4 and 5),
  • $p_{\rm L} = 3/4, \, p_{\rm H} = 1/4$  (subtask 6).


The presented coded signal  $c(t)$  and the corresponding encoded sequence  $\langle c_\nu \rangle$  with  $c_\nu \in \{{\rm P}, {\rm N}, {\rm M} \}$  results from the AMI coding  ("Alternate Mark Inversion")  according to the following rule:

  • The binary symbol  $\rm L$   ⇒   "Low"  is always represented by the ternary symbol  $\rm N$   ⇒   "German: Null"  ⇒  "Zero".
  • The binary symbol  $\rm H$   ⇒   "High"  is also encoded deterministically but alternately  (hence the name "Alternate Mark Inversion")  by the symbols  $\rm P$  ⇒  "Plus"  and  $\rm M$  ⇒  "Minus".



In this task, the decision content  $H_0$  and the resulting entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  are to be determined for the three parameter sets mentioned above.  The relative redundancy of the code sequence results from this according to the equation

$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}\hspace{0.05cm}.$$




Hints:

  • In general, the following relations exist between the decision content  $H_0$,  the entropy  $H$  $($here equal to  $H_{\rm C})$  and the entropy approximations:
$$H \le \ \text{...} \ \le H_3 \le H_2 \le H_1 \le H_0\hspace{0.05cm}.$$
  • In  Exercise 1.4   the entropy approximations were calculated for equally probable symbols  $\rm L$  and  $\rm H$  as follows (each in "bit/symbol"):
$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292\hspace{0.05cm}.$$




Questions

1 Let the source symbols be equally probable  $(p_{\rm L} = p_{\rm H}= 1/2)$.  What is the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$?

$H_{\rm C} \ = \ $ $\ \rm bit/ternary \ symbol$

2 What is the relative redundancy of the encoded sequence?

$r_{\rm C} \ = \ $ $\ \rm \%$

3 For the binary source,  $p_{\rm L} = 1/4$  and  $p_{\rm H} = 3/4$.  What is the entropy of the encoded sequence?

$H_{\rm C} \ = \ $ $\ \rm bit/ternary \ symbol$

4 What is the relative redundancy of the encoded sequence?

$r_{\rm C} \ = \ $ $\ \rm \%$

5 Calculate the approximation  $H_{\rm 1}$  of the code entropy for  $p_{\rm L} = 1/4$  and  $p_{\rm H} = 3/4$.

$H_{\rm 1} \ = \ $ $\ \rm bit/ternary \ symbol$

6 Calculate the approximation  $H_{\rm 1}$  of the code entropy for  $p_{\rm L} = 3/4$  and  $p_{\rm H} = 1/4$.

$H_{\rm 1} \ = \ $ $\ \rm bit/ternary \ symbol$


Solution

(1)  Since the AMI code neither adds new information nor causes information to disappear, the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  is equal to the source entropy  $H_{\rm Q}$. 

  • Therefore, for equally probable and statistically independent source symbols, the following holds:
$$H_{\rm Q} {= 1 \,{\rm bit/binary \ symbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C} \hspace{0.15cm} \underline {= 1 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(2)  The decision content of a ternary source is  $H_0 = \log_2 \; (3) = 1.585\; \rm bit/symbol$. 

  • This gives the following for the relative redundancy
$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)\hspace{0.15cm} \underline {= 36.9 \,\%}\hspace{0.05cm}.$$


(3)    $H_{\rm C} = H_{\rm Q}$ is still valid.  However, because of the unequal symbol probabilities,  $H_{\rm Q}$  is now smaller:

$$H_{\rm Q} = \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot{\rm log}_2\hspace{0.1cm} (4/3){= 0.811 \,{\rm bit/binary \ symbol}} \hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_{\rm C} = H_{\rm Q} \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(4)  By analogy with sub-task  (2)    $r_{\rm C} = 1 - 0.811/1.585

\hspace{0.15cm} \underline {= 48.8  \,\%}
\hspace{0.05cm}$ now holds.
  • One can generalize this result.  Namely, it holds:
$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge}) = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})\hspace{0.05cm}.$$


(5)  Since each  $\rm L$  is mapped to  $\rm N$  and  $\rm H$  is mapped alternately to  $\rm M$  and  $\rm P$, it holds that

$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_1 = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3) \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(6)  Now the probabilities of occurrence of the ternary symbols are   $p_{\rm N} = 3/4$  sowie  $p_{\rm P} = p_{\rm M} =1/8$.  Thus:

$$H_1 = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8) \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$

Interpretation:

  • For  $p_{\rm L} = 1/4, \ p_{\rm H} = 3/4$  gives  $H_1 = 1.56 \; \rm bit/symbol$.
  • For  $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$ , on the other hand,  $H_1 = 1.06 \; \rm bit/symbol$  results in a significantly smaller value.
  • For both parameter combinations, however, the same applies:
$$H_0 = 1.585 \,{\rm bit/symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/symbol}\hspace{0.05cm}.$$

It follows from this:

  • If one considers two message sources  $\rm Q1$  and  $\rm Q2$  with the same symbol set size  $M$   ⇒   decision content  $H_0 = \rm const.$, whereby the first order entropy approximation  $(H_1)$  is clearly greater for source  $\rm Q1$  than for source  $\rm Q2$, one cannot conclude from this by any means that the entropy of  $\rm Q1$  is actually greater than the entropy of $\rm Q2$. 
  • Rather, one must
  • calculate enough entropy approximations  $H_1$,  $H_2$,  $H_3$,  ... for both sources and
  • determine from them  (graphically or analytically)  the limit value of  $H_k$  for  $k \to \infty$.
  • Only then a final statement about the entropy ratios is possible.