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  2. Sloot Digital Coding System - Wikipedia

    en.wikipedia.org/wiki/Sloot_Digital_Coding_System

    The Sloot Digital Coding System is an alleged data sharing technique that its inventor claimed could store a complete digital movie file in 8 kilobytes of data — which, if true, would dramatically disprove Shannon's source coding theorem, a widely accepted principle of information theory that predicts how much data compression of a digital file is mathematically possible.

  3. List of archive formats - Wikipedia

    en.wikipedia.org/wiki/List_of_archive_formats

    An open source, patent- and royalty-free compression format. The compression algorithm is a Burrows–Wheeler transform followed by a move-to-front transform and finally Huffman coding. .F, .?XF [e] Freeze/melt [3] QNX4, Unix-like and DOS: Old compressor for QNX4 OS. The compression algorithm is a modified LZSS, with an adaptive Huffman coding ...

  4. 842 (compression algorithm) - Wikipedia

    en.wikipedia.org/wiki/842_(compression_algorithm)

    842, 8-4-2, or EFT is a data compression algorithm. It is a variation on Lempel–Ziv compression with a limited dictionary length. With typical data, 842 gives 80 to 90 percent of the compression of LZ77 with much faster throughput and less memory use. [1] Hardware implementations also provide minimal use of energy and minimal chip area.

  5. Data compression ratio - Wikipedia

    en.wikipedia.org/wiki/Data_compression_ratio

    For example, uncompressed songs in CD format have a data rate of 16 bits/channel x 2 channels x 44.1 kHz ≅ 1.4 Mbit/s, whereas AAC files on an iPod are typically compressed to 128 kbit/s, yielding a compression ratio of 10.9, for a data-rate saving of 0.91, or 91%.

  6. Lossless compression - Wikipedia

    en.wikipedia.org/wiki/Lossless_compression

    Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.

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