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7
compression.bib
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@@ -77,4 +77,11 @@
year = "2018", year = "2018",
url = "https://de.wikipedia.org/w/index.php?title=Kraft-Ungleichung&oldid=172862410", url = "https://de.wikipedia.org/w/index.php?title=Kraft-Ungleichung&oldid=172862410",
note = "[Online; Stand 26. November 2025]" note = "[Online; Stand 26. November 2025]"
}
@misc{ dewiki:partition,
author = "Wikipedia",
title = "Partitionsproblem --- Wikipedia{,} die freie Enzyklopädie",
year = "2025",
url = "https://de.wikipedia.org/w/index.php?title=Partitionsproblem&oldid=255787013",
note = "[Online; Stand 26. November 2025]"
} }

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compression.tex
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@@ -36,31 +36,41 @@ As the volume of data grows exponentially around the world, compression is only
Not only does it enable the storage of large amounts of information needed for research in scientific domains Not only does it enable the storage of large amounts of information needed for research in scientific domains
like DNA sequencing and analysis, it also plays a vital role in keeping stored data accessible by like DNA sequencing and analysis, it also plays a vital role in keeping stored data accessible by
facilitating cataloging, search and retrieval. facilitating cataloging, search and retrieval.
The concept of entropy is closely related to the design of efficient codes. The concept of entropy introduced in the previous entry is closely related to the design of efficient codes for compression.
\begin{figure}[H]
\begin{minipage}{0.5\textwidth}
\begin{equation}
H = E(I) = - \sum_i p_i \log_2(p_i)
\label{eq:entropy-information}
\end{equation}
\end{minipage}
\begin{minipage}{0.5\textwidth}
\begin{equation} \begin{equation}
H = E(I) = - \sum_i p_i \log_2(p_i) E(L) = \sum_i p_i l_i
\label{eq:entropy-information} \label{eq:expected-codelength}
\end{equation} \end{equation}
\end{minipage}
\end{figure}
In coding theory, the events of an information source are to be encoded in a manner that minimizes the bits needed to store
the information provided by the source.
The understanding of entropy as the expected information $E(I)$ of a message provides an intuition that, The understanding of entropy as the expected information $E(I)$ of a message provides an intuition that,
given a source with a given entropy (in bits), any coding can not have a lower average word length (in bits) given a source with a given entropy (in bits), any coding can not have a lower average word length (in bits)
\begin{equation}
E(l) = \sum_i p_i l_i
\end{equation}
than this entropy without losing information. than this entropy without losing information.
This is the content of Shannons's source coding theorem, This is the content of Shannons's source coding theorem,
introduced in \citeyear{shannon1948mathematical} \cite{enwiki:shannon-source-coding}. introduced in \citeyear{shannon1948mathematical} \cite{enwiki:shannon-source-coding}.
In his paper, \citeauthor{shannon1948mathematical} proposed two principal ideas to minimize the average length of a code. In his paper, \citeauthor{shannon1948mathematical} proposed two principal ideas to minimize the average length of a code.
The first is to use short codes for symbols with higher probability. The first is to use short codes for symbols with higher probability.
This is an intuitive approach as more frequent symbols have a higher impact on average code length. This is an intuitive approach as more frequent symbols have a higher impact on average code length.
The second idea is to encode events that frequently occur together at the same time, allowing for greater flexibility
in code design.
\section{Kraft-McMillan inequality} \section{Kraft-McMillan inequality}
\section{Shannon-Fano} \section{Shannon-Fano}
Shannon-Fano coding is one of the earliest methods for constructing prefix codes. Shannon-Fano coding is one of the earliest methods for constructing prefix codes.
It divides symbols into groups based on their probabilities, recursively partitioning them to assign shorter codewords It divides symbols into equal groups based on their probabilities, recursively partitioning them to assign shorter codewords
to more frequent symbols. to more frequent symbols.
While intuitive, Shannon-Fano coding does not always achieve optimal compression, While intuitive, Shannon-Fano coding does not always achieve optimal compression,
paving the way for more advanced techniques like Huffman coding. paving the way for more advanced techniques like Huffman coding.

2
entropy.tex
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@@ -348,7 +348,7 @@ The capacity of the binary symmetric channel is given by:
where $H_2(p) = -p \log_2(p) - (1-p)\log_2(1-p)$ is the binary entropy function. where $H_2(p) = -p \log_2(p) - (1-p)\log_2(1-p)$ is the binary entropy function.
As $p$ increases, uncertainty grows and channel capacity declines. As $p$ increases, uncertainty grows and channel capacity declines.
When $p = 0.5$, output bits are completely random and no information can be transmitted ($C = 0$). When $p = 0.5$, output bits are completely random and no information can be transmitted ($C = 0$).
As already shown in \autoref{fig:graph-entropy}, an error rate over $p > 0.5$ is equivalent to $ 1-p < 0.5$, As illustrated in \autoref{fig:graph-entropy}, an error rate over $p > 0.5$ is equivalent to $ 1-p < 0.5$,
though not relevant in practice. though not relevant in practice.
Shannon’s theorem is not constructive as it does not provide an explicit method for constructing such efficient codes, Shannon’s theorem is not constructive as it does not provide an explicit method for constructing such efficient codes,