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Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. [1] By contrast, lossy compression permits reconstruction only of an approximation of ...
Data compression. In information theory, data compression, source coding, [1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation. [2] Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy.
Left side of the image is from a low-quality JPEG image, showing lossy artefacts; the right side is from a PNG image. In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used ...
The idea came from the need for their families to compress and send large documents via email. [4] The initial version of the site featured a standalone PDF compression tool. [5] Since then, the online platform has introduced over 16 PDF tools to convert, compress and edit PDF documents. [6]
The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm.
Definition. Data compression ratio is defined as the ratio between the uncompressed size and compressed size: [1][2][3][4][5] Thus, a representation that compresses a file's storage size from 10 MB to 2 MB has a compression ratio of 10/2 = 5, often notated as an explicit ratio, 5:1 (read "five" to "one"), or as an implicit ratio, 5/1.