Difference between revisions of "Aufgaben:Exercise 2.5: Residual Redundancy with LZW Coding"

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{{quiz-Header|Buchseite=Informationstheorie/Komprimierung nach Lempel, Ziv und Welch
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{{quiz-Header|Buchseite=Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch
 
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[[File:P_ID2446__Inf_A_2_5_neu.png|right|frame|Residual redundancy  $r(N)$  & approximation   $r\hspace{0.05cm}'(N)$  of three binary sources]]
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[[File:P_ID2446__Inf_A_2_5_neu.png|right|frame|Residual redundancy  $r(N)$  and approximation   $r\hspace{0.05cm}'(N)$  of three sources]]
We assume here a binary input sequence of length  $N$  and consider three different binary message sources:
+
We assume here a binary input sequence of length  $N$  and consider three different binary sources:
* $\rm BQ1$: &nbsp; Symbol probabilities&nbsp;  $p_{\rm A} = 0.89$&nbsp; and&nbsp; $p_{\rm B} = 0.11$, i.e. different<br> &nbsp;  &#8658; &nbsp; entropy &nbsp; $H = 0.5\text{ bit/source symbol}$ &nbsp; &#8658; &nbsp; the source is redundant.
+
* $\rm BQ1$: &nbsp; Symbol probabilities&nbsp;  $p_{\rm A} = 0.89$&nbsp; and&nbsp; $p_{\rm B} = 0.11$, i.e. different<br> &nbsp;  &#8658; &nbsp; entropy &nbsp; $H = 0.5\text{ bit/source&nbsp;symbol}$ &nbsp; &#8658; &nbsp; the source is redundant.
 
* $\rm BQ2$:  &nbsp; $p_{\rm A} = p_{\rm B} = 0.5$&nbsp; (equally probable)<br> &nbsp; &#8658; &nbsp; entropy &nbsp; $H = 1\text{ bit/source symbol}$ &nbsp; &#8658; &nbsp; the source is redundancy-free.
 
* $\rm BQ2$:  &nbsp; $p_{\rm A} = p_{\rm B} = 0.5$&nbsp; (equally probable)<br> &nbsp; &#8658; &nbsp; entropy &nbsp; $H = 1\text{ bit/source symbol}$ &nbsp; &#8658; &nbsp; the source is redundancy-free.
 
* $\rm BQ3$: &nbsp;  There is no concrete information on the statistics here.&nbsp; <br>In subtask&nbsp; '''(6)'''&nbsp; you are to estimate the entropy&nbsp; $H$&nbsp; of this source.
 
* $\rm BQ3$: &nbsp;  There is no concrete information on the statistics here.&nbsp; <br>In subtask&nbsp; '''(6)'''&nbsp; you are to estimate the entropy&nbsp; $H$&nbsp; of this source.
  
  
For these three sources, the respective <i>residual redundancy</i>&nbsp; $r(N)$&nbsp; was determined by simulation, which remains in the binary sequence after&nbsp; [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#The_Lempel-Ziv-Welch_algorithm |Lempel&ndash;Ziv&ndash;Welch&ndash;coding]]&nbsp; in der Binärfolge verbleibt.&nbsp;  
+
For these three sources, the respective&nbsp; "residual redundancy"&nbsp; $r(N)$&nbsp; was determined by simulation, which remains in the binary sequence after&nbsp; [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#The_Lempel-Ziv-Welch_algorithm |Lempel&ndash;Ziv&ndash;Welch coding]].&nbsp;  
  
 
The results are shown in the first column of the above table for the sources
 
The results are shown in the first column of the above table for the sources
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whereby we have restricted ourselves to sequence lengths&nbsp; $N &#8804; 50000$&nbsp; in the simulation.
 
whereby we have restricted ourselves to sequence lengths&nbsp; $N &#8804; 50000$&nbsp; in the simulation.
  
The <i>relative redundancy of the output sequence</i> &ndash; simplified called <i>residual redundancy</i> &ndash;  can be calculated from  
+
The&nbsp; "relative redundancy of the output sequence" &ndash; simplified called&nbsp; "residual redundancy" &ndash;  can be calculated from  
 
* the length&nbsp;  $N$&nbsp;  of the input sequence,
 
* the length&nbsp;  $N$&nbsp;  of the input sequence,
 
* the length&nbsp;  $L(N)$&nbsp;  of the output sequence and
 
* the length&nbsp;  $L(N)$&nbsp;  of the output sequence and
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can be calculated in the following way:
+
in the following way:
 
:$$r(N) = \frac{L(N) - N \cdot H}{L(N)}= 1 -  \frac{ N \cdot H}{L(N)}\hspace{0.05cm}.$$
 
:$$r(N) = \frac{L(N) - N \cdot H}{L(N)}= 1 -  \frac{ N \cdot H}{L(N)}\hspace{0.05cm}.$$
  
 
This takes into account that with perfect source coding the length of the output sequence could be lowered to the value&nbsp;  $L_{\rm min} = N &middot; H$&nbsp;.
 
This takes into account that with perfect source coding the length of the output sequence could be lowered to the value&nbsp;  $L_{\rm min} = N &middot; H$&nbsp;.
*With non-perfect source coding,&nbsp;  $L(n) - N &middot; H$&nbsp;  gives the remaining redundancy (with the pseudo&ndash;unit "bit").  
+
*With non-perfect source coding,&nbsp;  $L(n) - N &middot; H$&nbsp;  gives the remaining redundancy&nbsp; (with the pseudo&ndash;unit&nbsp; "bit").  
*After dividing by&nbsp;  $L(n)$&nbsp;  , one obtains the relative redundancy&nbsp;  $r(n)$&nbsp;  with the value range between zero and one; $r(n)$ should be as small as possible.
+
*After dividing by&nbsp;  $L(n)$,&nbsp;  one obtains the relative redundancy&nbsp;  $r(n)$&nbsp;  with the value range between zero and one;&nbsp; $r(n)$&nbsp; should be as small as possible.
  
  
A second parameter for measuring the efficiency of LZW coding is the&nbsp;  <i>compression factor</i>&nbsp;  $K(N)$, which should also be small as the quotient of the lengths of the output and input sequences:
+
A second parameter for measuring the efficiency of LZW coding is the&nbsp;  "compression factor"&nbsp;  $K(N)$&nbsp; as the quotient of the lengths of the output and input sequences, which should also be very small:
  
 
:$$K(N) = {L(N) }/{N} \hspace{0.05cm},$$
 
:$$K(N) = {L(N) }/{N} \hspace{0.05cm},$$
  
In the&nbsp;  [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Quantitative_statements_on_asymptotic_optimality|theory section]]&nbsp;  it was shown that the residual redundancy&nbsp;  $r(n)$&nbsp;  is often given by the function
+
In the&nbsp;  [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Quantitative_statements_on_asymptotic_optimality|theory section]]&nbsp;  it was shown that the residual redundancy&nbsp;  $r(n)$&nbsp;  is well approximated by the function
 
:$$r\hspace{0.05cm}'(N) =\frac {A}{{\rm lg}\hspace{0.1cm}(N)}
 
:$$r\hspace{0.05cm}'(N) =\frac {A}{{\rm lg}\hspace{0.1cm}(N)}
 
\hspace{0.5cm}{\rm mit}\hspace{0.5cm} A = 4 \cdot {r(N = 10000)}  
 
\hspace{0.5cm}{\rm mit}\hspace{0.5cm} A = 4 \cdot {r(N = 10000)}  
 
\hspace{0.05cm}.$$
 
\hspace{0.05cm}.$$
is well approximated.
+
 
  
 
*This approximation&nbsp;  $r\hspace{0.05cm}'(N)$&nbsp;  is given for&nbsp;  $\rm BQ1$&nbsp;  in the second column of the table above.
 
*This approximation&nbsp;  $r\hspace{0.05cm}'(N)$&nbsp;  is given for&nbsp;  $\rm BQ1$&nbsp;  in the second column of the table above.
Line 57: Line 57:
 
*The task belongs to the chapter&nbsp;  [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch|Compression according to Lempel, Ziv and Welch]].
 
*The task belongs to the chapter&nbsp;  [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch|Compression according to Lempel, Ziv and Welch]].
 
*In particular, reference is made to the pages
 
*In particular, reference is made to the pages
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Remaining_redundancy_as_a_measure_for_the_efficiency_of_coding_methods|Residual redundancy as a measure for the efficiency of coding methods]],
+
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Remaining_redundancy_as_a_measure_for_the_efficiency_of_coding_methods|Remaining redundancy as a measure for the efficiency of coding methods]],
 
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Efficiency_of_Lempel-Ziv_coding|Efficiency of Lempel-Ziv coding]] and
 
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Efficiency_of_Lempel-Ziv_coding|Efficiency of Lempel-Ziv coding]] and
 
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Quantitative_statements_on_asymptotic_optimality|Quantitative statements on asymptotic optimality]].   
 
:: [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch#Quantitative_statements_on_asymptotic_optimality|Quantitative statements on asymptotic optimality]].   
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{According to this result, what source entropy&nbsp; $H$&nbsp; could&nbsp; $\rm BQ3$&nbsp; ?&nbsp; <i>Hint:</i> Exactly one answer is correct.
+
{According to this result, what source entropy&nbsp; $H$&nbsp; could&nbsp; $\rm BQ3$&nbsp; ?&nbsp; <u>Hint:</u>&nbsp; Exactly one answer is correct.
 
|type="()"}
 
|type="()"}
- $H = 1.00 \ \rm bit/source symbol$,
+
- $H = 1.00 \ \rm bit/source\hspace{0.15cm}symbol$,
- $H = 0.75 \ \rm bit/source symbol$,
+
- $H = 0.75 \ \rm bit/source\hspace{0.15cm} symbol$,
- $H = 0.50 \ \rm bit/source symbol$,
+
- $H = 0.50 \ \rm bit/source\hspace{0.15cm} symbol$,
+ $H = 0.25 \ \rm bit/source symbol$.
+
+ $H = 0.25 \ \rm bit/source\hspace{0.15cm} symbol$.
  
  
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'''(3)'''&nbsp; In general, &nbsp;$r(N) = 1 - {H}/{K(N)} \hspace{0.05cm}.$&nbsp;  
 
'''(3)'''&nbsp; In general, &nbsp;$r(N) = 1 - {H}/{K(N)} \hspace{0.05cm}.$&nbsp;  
*$\rm BQ1$&nbsp; has entropy&nbsp; $H = 0.5$ bit/symbol.&nbsp;  
+
*$\rm BQ1$&nbsp; has entropy&nbsp; $H = 0.5$ bit/source symbol.&nbsp;  
*It follows that because &nbsp;$r(N) &asymp; r\hspace{0.05cm}'(N)$&nbsp; für&nbsp; $K(N_3) = 0.6$:
+
*It follows that because &nbsp;$r(N) &asymp; r\hspace{0.05cm}'(N)$&nbsp; for&nbsp; $K(N_3) = 0.6$:
:$$r(N_{\rm c}) = 1 - \frac{0.5}{0.6} = 0.167 \hspace{0.1cm}\Rightarrow\hspace{0.1cm}  
+
:$$r(N_{\rm 3}) = 1 - \frac{0.5}{0.6} = 0.167 \hspace{0.1cm}\Rightarrow\hspace{0.1cm}  
 
{\rm lg}\hspace{0.1cm}N_{\rm 3} = \frac{A}{0.167} = 6.36
 
{\rm lg}\hspace{0.1cm}N_{\rm 3} = \frac{A}{0.167} = 6.36
 
\hspace{0.1cm}\Rightarrow\hspace{0.1cm}
 
\hspace{0.1cm}\Rightarrow\hspace{0.1cm}
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[[File:EN_Inf_A_2_5d_v2.png|right|frame|Results for&nbsp;  $\rm BQ2$]]
 
[[File:EN_Inf_A_2_5d_v2.png|right|frame|Results for&nbsp;  $\rm BQ2$]]
'''(4)'''&nbsp; For&nbsp; $N = 10000$&nbsp; gilt &nbsp;$r(N) &asymp; r\hspace{0.05cm}'(N) = 0.19$:
+
'''(4)'''&nbsp; For&nbsp; $N = 10000$ &nbsp; &rArr; &nbsp; $r(N) &asymp; r\hspace{0.05cm}'(N) = 0.19$:
 
:$$\frac{A}{{\rm lg}\hspace{0.1cm}10000} = 0.19 \hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
:$$\frac{A}{{\rm lg}\hspace{0.1cm}10000} = 0.19 \hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
A = 0.19 \cdot 4 = 0.76 \hspace{0.05cm}. $$
 
A = 0.19 \cdot 4 = 0.76 \hspace{0.05cm}. $$
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r'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.063}.$$
 
r'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.063}.$$
  
*For the compression factor, (the apostrophe indicates that the approximation &nbsp;$r\hspace{0.05cm}'(N)$&nbsp; was assumed):
+
*For the compression factor &nbsp; &ndash; &nbsp; the apostrophe indicates that the approximation &nbsp;$r\hspace{0.05cm}'(N)$&nbsp; was assumed:
 
:$$K\hspace{0.05cm}'(N) = \frac{1}{1 - r\hspace{0.05cm}'(N)}\hspace{0.05cm}.$$
 
:$$K\hspace{0.05cm}'(N) = \frac{1}{1 - r\hspace{0.05cm}'(N)}\hspace{0.05cm}.$$
 
*Thus, for the length of the LZW output string:
 
*Thus, for the length of the LZW output string:
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[[File:EN_Inf_A_2_5e_v2.png|right|frame|Results for&nbsp; $\rm BQ3$]]
 
[[File:EN_Inf_A_2_5e_v2.png|right|frame|Results for&nbsp; $\rm BQ3$]]
'''(5)'''&nbsp; Following a similar procedure as in subtask&nbsp; '''(4)'''&nbsp; we obtain the fitting parameter&nbsp; $\rm BQ3$&nbsp; for the binary source&nbsp; $A = 1.36$&nbsp; and from this the results according to the table with a blue background.
+
'''(5)'''&nbsp; Following a similar procedure as in subtask&nbsp; '''(4)'''&nbsp; we obtain the fitting parameter&nbsp; $A = 1.36$&nbsp; for the binary source&nbsp; $\rm BQ3$&nbsp; and from this the results according to the table with a blue background.
  
<u>Hint:</u> &nbsp; The last column of this table is only understandable with knowledge of subtask&nbsp; '''(6)'''&nbsp;. There it is shown that the source&nbsp; $\rm BQ3$&nbsp; has the entropy&nbsp; $H = 0.25$ bit/source symbol.
+
<u>Hint:</u> &nbsp; The last column of this table is only understandable with knowledge of subtask&nbsp; '''(6)'''.&nbsp; There it is shown that the source&nbsp; $\rm BQ3$&nbsp; has the entropy&nbsp; $H = 0.25$&nbsp; bit/source symbol.
  
 
*In this case, the following applies to the compression factor:
 
*In this case, the following applies to the compression factor:
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:$$r\hspace{0.05cm}'(N = 50000)\hspace{0.15cm}\underline{ = 0.289},\hspace{0.3cm}r\hspace{0.05cm}'(N = 10^{6})\hspace{0.15cm}\underline{ = 0.227},\hspace{0.3cm}
 
:$$r\hspace{0.05cm}'(N = 50000)\hspace{0.15cm}\underline{ = 0.289},\hspace{0.3cm}r\hspace{0.05cm}'(N = 10^{6})\hspace{0.15cm}\underline{ = 0.227},\hspace{0.3cm}
 
r\hspace{0.05cm}'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.113}.$$
 
r\hspace{0.05cm}'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.113}.$$
*Thus, for&nbsp; $N = 10^{12}$&nbsp; , the compression factor&nbsp; $(0.282)$&nbsp; still deviates significantly from the entropy&nbsp; $(0.25)$&nbsp; which can only be achieved for&nbsp; $N \to \infty$&nbsp; (source coding theorem).
+
*Thus, for&nbsp; $N = 10^{12}$&nbsp; , the compression factor&nbsp; $(0.282)$&nbsp; still deviates significantly from the entropy&nbsp; $(0.25)$&nbsp; which can only be achieved for&nbsp; $N \to \infty$&nbsp; ("Source Coding Theorem").
  
  
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# Source&nbsp; $\rm BQ1$&nbsp; with&nbsp; $H = 0.50$ &nbsp; &rArr; &nbsp; $A = 1.06$ &nbsp; &rArr; &nbsp; according to the specification sheet,
 
# Source&nbsp; $\rm BQ1$&nbsp; with&nbsp; $H = 0.50$ &nbsp; &rArr; &nbsp; $A = 1.06$ &nbsp; &rArr; &nbsp; according to the specification sheet,
 
# Source&nbsp; $\rm BQ2$&nbsp; with&nbsp; $H = 1.00$ &nbsp; &rArr; &nbsp; $A = 0.76$ &nbsp; &rArr; &nbsp; see subtask&nbsp; '''(4)''',
 
# Source&nbsp; $\rm BQ2$&nbsp; with&nbsp; $H = 1.00$ &nbsp; &rArr; &nbsp; $A = 0.76$ &nbsp; &rArr; &nbsp; see subtask&nbsp; '''(4)''',
# Source&nbsp; $\rm BQ3$&nbsp; $(H$ unknown$)$: $A = 4 &middot; 0.34 =1.36$ &nbsp; &rArr; &nbsp; corresponding to the last column in the table.
+
# Source&nbsp; $\rm BQ3$&nbsp; $(H$ unknown$)$: $A = 4 &middot; 0.34 =1.36$ &nbsp; &rArr; &nbsp; corresponding to the table on the right.
 
 
  
 
*Obviously, the smaller the entropy&nbsp; $H$&nbsp; the larger the adjustment factor&nbsp; $A$&nbsp; (and vice versa).  
 
*Obviously, the smaller the entropy&nbsp; $H$&nbsp; the larger the adjustment factor&nbsp; $A$&nbsp; (and vice versa).  
*Since exactly one solution is possible, &nbsp; $H = 0.25$&nbsp; bit/source symbol must be correct &nbsp;&nbsp;&#8658;&nbsp;&nbsp; <u>answer 4</u>.
+
*Since exactly one solution is possible, &nbsp; $H = 0.25$&nbsp; bit/source&nbsp; symbol must be correct &nbsp;&nbsp;&#8658;&nbsp;&nbsp; <u>answer 4</u>.
  
*In fact, the probabilities&nbsp;$p_{\rm A} = 0.96$&nbsp; and &nbsp;$p_{\rm B} = 0.04$&nbsp; &nbsp; &#8658; &nbsp; $H &asymp; 0.25$ were used in the simulation for source&nbsp; $\rm BQ3$&nbsp;.
+
*In fact, the probabilities&nbsp;$p_{\rm A} = 0.96$&nbsp; and &nbsp;$p_{\rm B} = 0.04$&nbsp; &nbsp; &#8658; &nbsp; $H &asymp; 0.25$ were used in the simulation for the source&nbsp; $\rm BQ3$.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  

Latest revision as of 13:12, 10 August 2021

Residual redundancy  $r(N)$  and approximation   $r\hspace{0.05cm}'(N)$  of three sources

We assume here a binary input sequence of length  $N$  and consider three different binary sources:

  • $\rm BQ1$:   Symbol probabilities  $p_{\rm A} = 0.89$  and  $p_{\rm B} = 0.11$, i.e. different
      ⇒   entropy   $H = 0.5\text{ bit/source symbol}$   ⇒   the source is redundant.
  • $\rm BQ2$:   $p_{\rm A} = p_{\rm B} = 0.5$  (equally probable)
      ⇒   entropy   $H = 1\text{ bit/source symbol}$   ⇒   the source is redundancy-free.
  • $\rm BQ3$:   There is no concrete information on the statistics here. 
    In subtask  (6)  you are to estimate the entropy  $H$  of this source.


For these three sources, the respective  "residual redundancy"  $r(N)$  was determined by simulation, which remains in the binary sequence after  Lempel–Ziv–Welch coding

The results are shown in the first column of the above table for the sources

  • $\rm BQ1$  (yellow background),
  • $\rm BQ2$  (green background) and
  • $\rm BQ3$  (blue background)


whereby we have restricted ourselves to sequence lengths  $N ≤ 50000$  in the simulation.

The  "relative redundancy of the output sequence" – simplified called  "residual redundancy" – can be calculated from

  • the length  $N$  of the input sequence,
  • the length  $L(N)$  of the output sequence and
  • the entropy  $H$


in the following way:

$$r(N) = \frac{L(N) - N \cdot H}{L(N)}= 1 - \frac{ N \cdot H}{L(N)}\hspace{0.05cm}.$$

This takes into account that with perfect source coding the length of the output sequence could be lowered to the value  $L_{\rm min} = N · H$ .

  • With non-perfect source coding,  $L(n) - N · H$  gives the remaining redundancy  (with the pseudo–unit  "bit").
  • After dividing by  $L(n)$,  one obtains the relative redundancy  $r(n)$  with the value range between zero and one;  $r(n)$  should be as small as possible.


A second parameter for measuring the efficiency of LZW coding is the  "compression factor"  $K(N)$  as the quotient of the lengths of the output and input sequences, which should also be very small:

$$K(N) = {L(N) }/{N} \hspace{0.05cm},$$

In the  theory section  it was shown that the residual redundancy  $r(n)$  is well approximated by the function

$$r\hspace{0.05cm}'(N) =\frac {A}{{\rm lg}\hspace{0.1cm}(N)} \hspace{0.5cm}{\rm mit}\hspace{0.5cm} A = 4 \cdot {r(N = 10000)} \hspace{0.05cm}.$$


  • This approximation  $r\hspace{0.05cm}'(N)$  is given for  $\rm BQ1$  in the second column of the table above.
  • In subtasks  (4)  and  (5)  you are to make the approximation for sources  $\rm BQ2$  and  $\rm BQ3$ .





Hints:

Remaining redundancy as a measure for the efficiency of coding methods,
Efficiency of Lempel-Ziv coding and
Quantitative statements on asymptotic optimality.
  • The descriptive variables  $K(N)$  and  $r(N)$  are deterministically related.


Questions

1

With which parameter  $A$  was the approximation  $r\hspace{0.05cm}'(N)$  of the residual redundancy for the binary source  $\rm BQ1$  created?

$A \ = \ $

2

What is the minimum size of  $N = N_2$  for  $\rm BQ1$  so that the residual redundancy satisfies the condition  $r(N) ≈ r\hspace{0.05cm}'(N) \le 5\%$ ?

$N_{2} \ = \ $

$\ \cdot 10^{21}$

3

What is the minimum size of  $N = N_3$  at  $\rm BQ1$  so that the compression factor  $K(N)= L(N)/N$  is not greater than  $0.6$?

$N_{3} \ = \ $

$\ \cdot 10^{6}$

4

Now determine the redundancy approximation  $r\hspace{0.05cm}'(N)$  for the redundancy-free binary source $\rm BQ2$, in particular:

$r'(N = 50000)\ = \ $

$r'(N = 10^6)\ = \ $

$r'(N = 10^{12})\ = \ $

5

What values does the redundancy approximation  $r\hspace{0.05cm}'(N)$  yield for the unspecified binary source  $\rm BQ3$? In particular:

$r'(N = 50000)\ = \ $

$r'(N = 10^6)\ = \ $

$r'(N = 10^{12})\ = \ $

6

According to this result, what source entropy  $H$  could  $\rm BQ3$  ?  Hint:  Exactly one answer is correct.

$H = 1.00 \ \rm bit/source\hspace{0.15cm}symbol$,
$H = 0.75 \ \rm bit/source\hspace{0.15cm} symbol$,
$H = 0.50 \ \rm bit/source\hspace{0.15cm} symbol$,
$H = 0.25 \ \rm bit/source\hspace{0.15cm} symbol$.


Solution

(1)  The approximation  $r\hspace{0.05cm}'(N)$  agrees exactly by definition for the sequence length  $N = 10000$  with the residual redundancy  $r(N) = 0.265$  determined by simulation.

  • Thus
$$A = 4 \cdot r(N = 10000) =4 \cdot {0.265} \hspace{0.15cm}\underline{= 1.06} \hspace{0.05cm}. $$


(2)  From the relationship  ${A}/{\rm lg}\hspace{0.1cm}(N) ≤ 0.05$    ⇒    ${A}/{\rm lg}\hspace{0.1cm}(N) = 0.05$  it follows:

$${{\rm lg}\hspace{0.1cm}N_{\rm 2}} = \frac{A}{0.05} = 21.2 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} N_{\rm 2} = 10^{21.2} \hspace{0.15cm}\underline{= 1.58 \cdot 10^{21}} \hspace{0.05cm}.$$


(3)  In general,  $r(N) = 1 - {H}/{K(N)} \hspace{0.05cm}.$ 

  • $\rm BQ1$  has entropy  $H = 0.5$ bit/source symbol. 
  • It follows that because  $r(N) ≈ r\hspace{0.05cm}'(N)$  for  $K(N_3) = 0.6$:
$$r(N_{\rm 3}) = 1 - \frac{0.5}{0.6} = 0.167 \hspace{0.1cm}\Rightarrow\hspace{0.1cm} {\rm lg}\hspace{0.1cm}N_{\rm 3} = \frac{A}{0.167} = 6.36 \hspace{0.1cm}\Rightarrow\hspace{0.1cm} N_{\rm 3} = 10^{6.36} \hspace{0.15cm}\underline{= 2.29 \cdot 10^{6}} \hspace{0.05cm}.$$


Results for  $\rm BQ2$

(4)  For  $N = 10000$   ⇒   $r(N) ≈ r\hspace{0.05cm}'(N) = 0.19$:

$$\frac{A}{{\rm lg}\hspace{0.1cm}10000} = 0.19 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} A = 0.19 \cdot 4 = 0.76 \hspace{0.05cm}. $$
  • The results are summarised in the table opposite.
  • One can see the very good agreement between   $r(N)$  and  $r\hspace{0.05cm}'(N)$.
  • The numerical values sought are marked in red in the table:

$$r'(N = 50000)\hspace{0.15cm}\underline{ = 0.162},\hspace{0.3cm}r'(N = 10^{6})\hspace{0.15cm}\underline{ = 0.127},\hspace{0.3cm} r'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.063}.$$

  • For the compression factor   –   the apostrophe indicates that the approximation  $r\hspace{0.05cm}'(N)$  was assumed:
$$K\hspace{0.05cm}'(N) = \frac{1}{1 - r\hspace{0.05cm}'(N)}\hspace{0.05cm}.$$
  • Thus, for the length of the LZW output string:
$$L\hspace{0.05cm}'(N) = K\hspace{0.05cm}'(N) \cdot N = \frac{N}{1 - r\hspace{0.05cm}'(N)}\hspace{0.05cm}.$$


Results for  $\rm BQ3$

(5)  Following a similar procedure as in subtask  (4)  we obtain the fitting parameter  $A = 1.36$  for the binary source  $\rm BQ3$  and from this the results according to the table with a blue background.

Hint:   The last column of this table is only understandable with knowledge of subtask  (6).  There it is shown that the source  $\rm BQ3$  has the entropy  $H = 0.25$  bit/source symbol.

  • In this case, the following applies to the compression factor:
$$K\hspace{0.05cm}'(N) = \frac{H}{1 - r\hspace{0.05cm}'(N)} = \frac{0.25}{1 - r'(N)} \hspace{0.05cm}.$$
  • Thus, for the values of residual redundancy we are looking for, we obtain:
$$r\hspace{0.05cm}'(N = 50000)\hspace{0.15cm}\underline{ = 0.289},\hspace{0.3cm}r\hspace{0.05cm}'(N = 10^{6})\hspace{0.15cm}\underline{ = 0.227},\hspace{0.3cm} r\hspace{0.05cm}'(N = 10^{12})\hspace{0.15cm}\underline{ = 0.113}.$$
  • Thus, for  $N = 10^{12}$  , the compression factor  $(0.282)$  still deviates significantly from the entropy  $(0.25)$  which can only be achieved for  $N \to \infty$  ("Source Coding Theorem").



(6)  The individual approximations  $r\hspace{0.05cm}'(N)$  differ only by the parameter  $A$.  Here we found:

  1. Source  $\rm BQ1$  with  $H = 0.50$   ⇒   $A = 1.06$   ⇒   according to the specification sheet,
  2. Source  $\rm BQ2$  with  $H = 1.00$   ⇒   $A = 0.76$   ⇒   see subtask  (4),
  3. Source  $\rm BQ3$  $(H$ unknown$)$: $A = 4 · 0.34 =1.36$   ⇒   corresponding to the table on the right.
  • Obviously, the smaller the entropy  $H$  the larger the adjustment factor  $A$  (and vice versa).
  • Since exactly one solution is possible,   $H = 0.25$  bit/source  symbol must be correct   ⇒   answer 4.
  • In fact, the probabilities $p_{\rm A} = 0.96$  and  $p_{\rm B} = 0.04$    ⇒   $H ≈ 0.25$ were used in the simulation for the source  $\rm BQ3$.