Lossless data compression limit

  • Does lossless compression lose any data?

    Lossless compression uses an algorithm to shrink the image without losing any important data.
    Essentially, it rewrites the file to make it more efficient - resulting in a smaller storage or transfer size.
    One way the algorithm does this is by replacing non-essential information and storing them in an index file..

  • How does lossless compression compress?

    Lossless compression “packs” data into a smaller file size by using a kind of internal shorthand to signify redundant data.
    If an original file is 1.

    1. MB, for example, lossless compression can reduce it to about half that size, depending on the type of file being compressed

  • Text compression techniques

    Visually “lossless” video compression is sometimes used when the goal is to reduce file and stream sizes by only a slight amount in order to keep picture quality identical to the original source.
    Lossless video compression typically achieves lower compression ratios of 10:1 to 20:1..

  • What is the limit of lossless compression?

    Typically, depending on the image, lossless compression ratios range from about 1.5:1 to 3:1.
    On the other hand, state-of-the-art lossy compression techniques give compression rations in excess of 20:1 with virtually no loss in visual fidelity..

Jun 17, 2011Half of all such strings can be compressed by at most 1 bit. 1/4 of all strings can be compressed by at most 2 bits. 1/8 of all such strings can 
Shannon established that there is a fundamental limit to lossless data compression. This limit, called the entropy rate, is denoted by H. The exact value of H depends on the information source --- more specifically, the statistical nature of the source.

Source Coding

Shannon in his famous article “A Mathematical Theory of Communication” introduces a further modification to the information transfer problem

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

Modern Approach to Information Theory

One of the most important aspects covered is to understand that source coding is a subproblem with respect to the problem of information

Does lossless compression preserve digitized data?

Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but can rarely do much better than 1:2 compression because of the intrinsic entropy of the data

Unfortunately, Wikipedia's article doesn't contain a reference or citation to support this claim

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

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