Aufgaben:Exercise 2.5Z: Compression Factor vs. Residual Redundancy: Difference between revisions
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{{quiz-Header|Buchseite= | {{quiz-Header|Buchseite=Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch | ||
}} | }} | ||
[[File:P_ID2449__Inf_Z_2_5_neu.png|right|]] | [[File:P_ID2449__Inf_Z_2_5_neu.png|right|frame|Data length $L(N)$ after LZW coding for $\rm BQ1$ and $\rm BQ2$]] | ||
: | As in [[Aufgaben:2.5_Restredundanz_bei_LZW-Codierung|Exercise 2.5]] we consider data compression using the [[Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch#The_Lempel-Ziv-Welch_algorithm|Lempel–Ziv–Welch algorithm]]. Here holds: | ||
* Let the input sequence have the length $N$. | |||
* The length of the LZW coder output is $L$. | |||
:* | The graph shows for two different binary sources $\rm BQ1$ and $\rm BQ2$ the relationship between the sequence lengths $N$ and $L$, represented by the function $L(N)$. | ||
The two sources have the same statistical properties as in [[Aufgaben:Aufgabe_2.5:_Restredundanz_bei_LZW-Codierung|Exercise 2.5]]: | |||
* $\rm BQ1$ is redundant due to unequal symbol probabilities $(p_{\rm A} = 0.89$ and $p_{\rm B} = 0.11)$ . There are no dependencies between the individual symbols. The entropy is $H = 0.5$ bit/source symbol. | |||
* $\rm BQ2$ is redundancy-free and thus has the entropy $H = 1$ bit/source symbol. | |||
Furthermore, you need two definitions for the solution of this task: | |||
*Let the "compression factor" be by definition | |||
:$$K(N) = \frac{L(N)}{N}\hspace{0.05cm}.$$ | |||
* The relative redundancy of the LZW code sequence (called "residual redundancy" in the following) is | |||
:$$r(N) = \frac{L(N) - N \cdot H}{L(N)}= 1 - \frac{ N \cdot H}{L(N)}\hspace{0.05cm}.$$ | |||
: | Hints: | ||
*The exercise belongs to the chapter [[Information_Theory/Komprimierung_nach_Lempel,_Ziv_und_Welch|Compression according to Lempel, Ziv and Welch]]. | |||
*In particular, reference is made to the pages | |||
:: [[Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch#Remaining_redundancy_as_a_measure_for_the_efficiency_of_coding_methods|Remainding redundancy as a measure of the efficiency of coding methods]], | |||
:: [[Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch#Efficiency_of_Lempel-Ziv_coding|Efficiency of Lempel-Ziv coding]] and | |||
:: [[Information_Theory/Compression_According_to_Lempel,_Ziv_and_Welch#Quantitative_statements_on_asymptotic_optimality|Quantitative statements on asymptotic optimality]]. | |||
=== | ===Questions=== | ||
<quiz display=simple> | <quiz display=simple> | ||
{ | {Which compression factors $K(N)$ result with $N = 10000$? | ||
|type="{}"} | |type="{}"} | ||
$N = 10000 | $\rm BQ1$: $K(N = 10000) \ = \ $ { 0.68 3% } | ||
$BQ2:\ | $\rm BQ2$: $K(N = 10000) \ = \ $ { 1.233 3% } | ||
{ | {What is the residual redundancy $r(N)$ (in percent)? Let $N = 10000$ apply again. | ||
|type="{}"} | |type="{}"} | ||
$N = 10000 | $\rm BQ1$: $r(N = 10000) \ = \ $ { 26.5 3% } $\ \%$ | ||
$BQ2:\ | $\rm BQ2$: $r(N = 10000) \ = \ $ { 19.0 3% } $\ \%$ | ||
{ | {What conclusions can be drawn from the comparison of $N = 10000$ and $N = 50000$? | ||
|type="[]"} | |type="[]"} | ||
+ | + For both sources $K(N = 50000)$ is smaller than $K(N = 10000)$. | ||
+ | + For both sources $r(N = 50000)$ is smaller than $r(N = 10000)$. | ||
- | - Only $\rm BQ1$ has more favourable values with $N = 50000$ . | ||
| Line 51: | Line 63: | ||
</quiz> | </quiz> | ||
=== | ===Solution=== | ||
{{ML-Kopf}} | {{ML-Kopf}} | ||
'''(1)''' The compression factor is defined as the quotient of the lengths of the LZW output sequence $(L)$ and input sequence $(N = 10000)$: | |||
:$${\rm BQ1:}\hspace{0.3cm} K \hspace{0.2cm} = \hspace{0.2cm} \frac{6800}{10000}\hspace{0.15cm}\underline{= 0.680}\hspace{0.05cm}, | :$${\rm BQ1:}\hspace{0.3cm} K \hspace{0.2cm} = \hspace{0.2cm} \frac{6800}{10000}\hspace{0.15cm}\underline{= 0.680}\hspace{0.05cm},$$ | ||
:$$ {\rm BQ2:}\hspace{0.3cm} K \hspace{0.2cm} = \hspace{0.2cm} \frac{12330}{10000}\hspace{0.15cm}\underline{= 1.233}\hspace{0.05cm}. $$ | |||
*Of course, the LZW coding only makes sense with the redundant binary source $\rm BQ1$ . Here the amount of data can be reduced by $1-0.68=32\%$ . | |||
*With the redundancy-free binary source $\rm BQ2$ , on the other hand, the LZW coding leads to a $1.233-1=23.3\%$ larger data quantity. | |||
'''(2)''' From the given equation we obtain with $N = 10000$: | |||
:$${\rm BQ1:}\hspace{0.3cm} H = 0.5\hspace{0.05cm},\hspace{0.2cm} r(N=10000) \hspace{0.2cm} = \hspace{0.2cm}1 - \frac{0.5 \cdot N}{L } = 1 - \frac{5000}{6800 } \hspace{0.15cm}\underline{\approx 26.5\,\%}\hspace{0.05cm},$$ | |||
:$$ {\rm BQ2:}\hspace{0.3cm} H = 1.0\hspace{0.05cm},\hspace{0.2cm} r(N=10000) \hspace{0.2cm} = \hspace{0.2cm}1 - \frac{N}{L } = 1 - \frac{10000}{12330 } \hspace{0.15cm}\underline{\approx 19\,\%}\hspace{0.05cm}.$$ | |||
*The residual redundancy indicates the (relative) redundancy of the LZW output sequence. | |||
*Although the source $\rm BQ1$ is more suitable for LZW coding than the redundancy-free source $\rm BQ2$, $\rm BQ1$ results in a larger residual redundancy because of the redundancy in the input sequence. | |||
*A smaller residual redundancy $r(N)$ for a given $N$ therefore says nothing about whether LZW coding is useful for the source at hand. | |||
*For this, the compression factor $K(N)$ must be considered. In general, the following relationship between $r(N)$ and $K(N)$ applies: | |||
:$$r(N) = 1 - \frac{H}{K(N)}\hspace{0.05cm},\hspace{0.5cm} K(N) = H \cdot (1- r(N))\hspace{0.05cm}.$$ | |||
'''(3)''' From the table on the information page one can read (or deduce) | |||
*for the redundant binary source $\rm BQ1$: | |||
:$$L(N = 50000) = 32100\hspace{0.05cm},\hspace{0.5cm} K(N = 50000) = 0.642\hspace{0.05cm},\hspace{0.5cm}r(N = 50000) \hspace{0.15cm}\underline {= 22.2\,\% \hspace{0.05cm}},$$ | |||
*for the redundancy-free binary source $\rm BQ2$: | |||
:$$L(N = 50000) = 59595\hspace{0.05cm},\hspace{0.5cm} K(N = 50000) = 1.192\hspace{0.05cm},\hspace{0.5cm}r(N = 50000) \hspace{0.15cm}\underline {= 16.1\,\% \hspace{0.05cm}}.$$ | |||
<u>Statements 1 and 2</u> are therefore correct: | |||
* For both sources the compression factor $K(N)$ is smaller for $N = 50000$ than for $N = 10000$. | |||
* The same applies to the residual redundancy: $r(N = 50000) < r(N = 10000)$. | |||
:$ | * Both with regard to $K(N)$ and $r(N)$ "more favourable" values result with larger $N$ , even if, as with the redundancy-free binary source $\rm BQ2$, <br>the application of Lempel–Ziv actually leads to a deterioration. | ||
{{ML-Fuß}} | {{ML-Fuß}} | ||
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[[Category: | [[Category:Information Theory: Exercises|^2.2 Lempel-Ziv-Welch Compression^]] | ||
[[de:Aufgaben:Aufgabe 2.5Z: Komprimierungsfaktor vs. Restredundanz]] | |||
Latest revision as of 17:55, 16 March 2026

As in Exercise 2.5 we consider data compression using the Lempel–Ziv–Welch algorithm. Here holds:
- Let the input sequence have the length $N$.
- The length of the LZW coder output is $L$.
The graph shows for two different binary sources $\rm BQ1$ and $\rm BQ2$ the relationship between the sequence lengths $N$ and $L$, represented by the function $L(N)$.
The two sources have the same statistical properties as in Exercise 2.5:
- $\rm BQ1$ is redundant due to unequal symbol probabilities $(p_{\rm A} = 0.89$ and $p_{\rm B} = 0.11)$ . There are no dependencies between the individual symbols. The entropy is $H = 0.5$ bit/source symbol.
- $\rm BQ2$ is redundancy-free and thus has the entropy $H = 1$ bit/source symbol.
Furthermore, you need two definitions for the solution of this task:
- Let the "compression factor" be by definition
- $$K(N) = \frac{L(N)}{N}\hspace{0.05cm}.$$
- The relative redundancy of the LZW code sequence (called "residual redundancy" in the following) is
- $$r(N) = \frac{L(N) - N \cdot H}{L(N)}= 1 - \frac{ N \cdot H}{L(N)}\hspace{0.05cm}.$$
Hints:
- The exercise belongs to the chapter Compression according to Lempel, Ziv and Welch.
- In particular, reference is made to the pages
Questions
Solution
(1) The compression factor is defined as the quotient of the lengths of the LZW output sequence $(L)$ and input sequence $(N = 10000)$:
- $${\rm BQ1:}\hspace{0.3cm} K \hspace{0.2cm} = \hspace{0.2cm} \frac{6800}{10000}\hspace{0.15cm}\underline{= 0.680}\hspace{0.05cm},$$
- $$ {\rm BQ2:}\hspace{0.3cm} K \hspace{0.2cm} = \hspace{0.2cm} \frac{12330}{10000}\hspace{0.15cm}\underline{= 1.233}\hspace{0.05cm}. $$
- Of course, the LZW coding only makes sense with the redundant binary source $\rm BQ1$ . Here the amount of data can be reduced by $1-0.68=32\%$ .
- With the redundancy-free binary source $\rm BQ2$ , on the other hand, the LZW coding leads to a $1.233-1=23.3\%$ larger data quantity.
(2) From the given equation we obtain with $N = 10000$:
- $${\rm BQ1:}\hspace{0.3cm} H = 0.5\hspace{0.05cm},\hspace{0.2cm} r(N=10000) \hspace{0.2cm} = \hspace{0.2cm}1 - \frac{0.5 \cdot N}{L } = 1 - \frac{5000}{6800 } \hspace{0.15cm}\underline{\approx 26.5\,\%}\hspace{0.05cm},$$
- $$ {\rm BQ2:}\hspace{0.3cm} H = 1.0\hspace{0.05cm},\hspace{0.2cm} r(N=10000) \hspace{0.2cm} = \hspace{0.2cm}1 - \frac{N}{L } = 1 - \frac{10000}{12330 } \hspace{0.15cm}\underline{\approx 19\,\%}\hspace{0.05cm}.$$
- The residual redundancy indicates the (relative) redundancy of the LZW output sequence.
- Although the source $\rm BQ1$ is more suitable for LZW coding than the redundancy-free source $\rm BQ2$, $\rm BQ1$ results in a larger residual redundancy because of the redundancy in the input sequence.
- A smaller residual redundancy $r(N)$ for a given $N$ therefore says nothing about whether LZW coding is useful for the source at hand.
- For this, the compression factor $K(N)$ must be considered. In general, the following relationship between $r(N)$ and $K(N)$ applies:
- $$r(N) = 1 - \frac{H}{K(N)}\hspace{0.05cm},\hspace{0.5cm} K(N) = H \cdot (1- r(N))\hspace{0.05cm}.$$
(3) From the table on the information page one can read (or deduce)
- for the redundant binary source $\rm BQ1$:
- $$L(N = 50000) = 32100\hspace{0.05cm},\hspace{0.5cm} K(N = 50000) = 0.642\hspace{0.05cm},\hspace{0.5cm}r(N = 50000) \hspace{0.15cm}\underline {= 22.2\,\% \hspace{0.05cm}},$$
- for the redundancy-free binary source $\rm BQ2$:
- $$L(N = 50000) = 59595\hspace{0.05cm},\hspace{0.5cm} K(N = 50000) = 1.192\hspace{0.05cm},\hspace{0.5cm}r(N = 50000) \hspace{0.15cm}\underline {= 16.1\,\% \hspace{0.05cm}}.$$
Statements 1 and 2 are therefore correct:
- For both sources the compression factor $K(N)$ is smaller for $N = 50000$ than for $N = 10000$.
- The same applies to the residual redundancy: $r(N = 50000) < r(N = 10000)$.
- Both with regard to $K(N)$ and $r(N)$ "more favourable" values result with larger $N$ , even if, as with the redundancy-free binary source $\rm BQ2$,
the application of Lempel–Ziv actually leads to a deterioration.