Data compression limit

Is there a limit to data compression?
The maximum compression ratio is 90 percent.
The maximum compression of any sequence of bytes occurs by replacing each group of 15 bytes with a single 12-bit symbol number, yielding a compressed image that is ten percent of the size of the original image..
Lossy compression techniques
Certain data files, such as text files, picture files in the BMP format, and some text style database files can often be compressed by 90% or more.
Some other types of files, such as program files, may compress by 50% or so.
Learn how to make a zip file..
Text compression techniques
You cannot always accurately estimate the compression ratio by simply examining the data.
On average, you can expect to achieve between 2:1 and 15:1 compression for AFP documents and up to 30:1 compression for line data reports..
What is the limit of data compression?
If the typical data distribution skews away from the data that was sampled when the dictionary was created, compression ratios can decrease.
The maximum compression ratio is 90 percent..

Dec 2, 2010The limits of compression are dictated by the randomness of the source. Welcome to the study of information theory! See data compression.What is the maximum theoretically possible compression rate?What can be the least possible value of data-compression-ratio for Relation of Entropy to Lossless Compression Rate - Stack OverflowPrediciting time or compression ratio for lossless compress of a file?More results from stackoverflow.com

Dec 2, 2010The limits of compression are dictated by the randomness of the source. Welcome to the study of information theory! See data compression.What is the maximum theoretically possible compression rate?What can be the least possible value of data-compression-ratio for Relation of Entropy to Lossless Compression Rate - Stack OverflowIs there an algorithm for "perfect" compression? - Stack OverflowMore results from stackoverflow.com

Data compression ratio, also known as compression power, is a measurement of the relative reduction in size of data representation produced by a data compression algorithm. It is typically expressed as the division of uncompressed size by Wikipedia

Definition of The Information Transfer Problem in Its Most General Form

To understand these two answers it is essential to introduce the problem of information transmission in its most general form.
For the transmission of information to take place between two points, it is essential that the two subjects share a communication language.
This requirement is necessary, without a language of communication, the transmitted.

Difference Between The Source Entropy H

The value of NH(X) represents the compressed message.
So entropy, in this case, defines message information in a more general sense, not just Shannon information.Unfortunately, this method does not represent the general case of information transmission, but represents a subproblem in which both the encoder and the decoder know the source that gener.

How do you define a compression limit?

In order to define a compression limit it is essential to report the hypotheses for which the limit is valid.
In information theory, 3 fundamental hypotheses are used which are the following:

the information is defined by the entropy function H (X)

the information that identifies the source is known both by the encoder and by the decoder.

How much space is saved by compressing a file?

Thus, a representation that compresses the storage size of a file from 10MB to 2MB yields a space saving of 1 - 2/10 = 0.8, often notated as a percentage, 80%.
For signals of indefinite size, such as:

streaming audio and video
the compression ratio is defined in terms of uncompressed and compressed data rates instead of data sizes:

Is there a limit to lossless data compression?

The less predictable the data the higher its information content, and that places a theoretical limit on the amount of (lossless) compression possible.
Well, it depends on your algorithm, your data, the length of your data, and how badly you want the exact data back.

Source Coding

Shannon in his famous article “A Mathematical Theory of Communication” introduces a further modification to the information transfer problem, which allows him to obtain a subproblem in which it is possible to define the theoretical compression limit.
For this purpose, Shannon introduces the source that generates the message.
Furthermore, the coding.

What is data compression in information theory?

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.

Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon Cormack and Nigel Horspool.
It uses predictive arithmetic coding similar to prediction by partial matching (PPM), except that the input is predicted one bit at a time.
DMC has a good compression ratio and moderate speed, similar to PPM, but requires somewhat more memory and is not widely implemented.
Some recent implementations include the experimental compression programs external text>hook by Nania Francesco Antonio, external text>ocamyd by Frank Schwellinger, and as a submodel in paq8l by Matt Mahoney.
These are based on the external text>1993 implementation in C by Gordon Cormack.

Data compression limit

Is there a limit to data compression?

Lossy compression techniques

Text compression techniques

What is the limit of data compression?