Data compression theory

What is compression theory?
In computational complexity theory, the compression theorem is an important theorem about the complexity of computable functions.
The theorem states that there exists no largest complexity class, with computable boundary, which contains all computable functions..
What is data compression in information theory?
data compression, also called compaction, the process of reducing the amount of data needed for the storage or transmission of a given piece of information, typically by the use of encoding techniques._{Sep 21, 2023}.
What theory is data compression based on?
Information theory is defined to be the study of efficient coding and its consequences, in the form of speed of transmission and probability of error [Ingels 1971].
Data compression may be viewed as a branch of information theory in which the primary objective is to minimize the amount of data to be transmitted..

Overview

In information theory, data compression, source coding

Lossless

Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information

Lossy

In the late 1980s, digital images became more common, and standards for lossless image compression emerged. In the early 1990s

Uses

Entropy coding originated in the 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was developed in 1950

Outlook and currently unused potential

It is estimated that the total amount of data that is stored on the world's storage devices could be further compressed with existing compression

What is the theoretical background of data compression?

• A

Information Theoretical Background The theoretical background of data compression is mainly based on some re sults of information theory

The fundamental of this theory was worked out by Claude Shannon more than 50 years ago , and later many books and pub lications appeared in this topic (see, e

g , [1,28,33])

What types of data compression techniques are included in the book?

Encompassing the entire field of data compression, the book includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, and scalar and vector quantization

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy.Information theory is defined to be the study of efficient coding and its consequences, in the form of speed of transmission and probability of error [Ingels 1971]. Data compression may be viewed as a branch of information theory in which the primary objective is to minimize the amount of data to be transmitted.Compression reduces the cost of storage, increases the speed of algorithms, and reduces the transmission cost. Compression is achieved by removing redundancy, that is repetition of unnecessary data.

Data compression, also called compaction, the process of reducing the amount of data needed for the storage or transmission of a given piece of information, typically by the use of encoding techniques.

In information theory, redundancy measures the fractional difference between the entropy texhtml mvar style=font-style:italic>H(X) of an ensemble texhtml mvar style=font-style:italic>X, and its maximum possible value mwe-math-element>.
Informally, it is the amount of wasted space used to transmit certain data.
Data compression is a way to reduce or eliminate unwanted redundancy, while forward error correction is a way of adding desired redundancy for purposes of error detection and correction when communicating over a noisy channel of limited capacity.