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  2. Stretched exponential function - Wikipedia

    en.wikipedia.org/wiki/Stretched_exponential_function

    With a stretching exponent β between 0 and 1, the graph of log f versus t is characteristically stretched, hence the name of the function. The compressed exponential function (with β > 1) has less practical importance, with the notable exception of β = 2, which gives the normal distribution.

  3. Zopfli - Wikipedia

    en.wikipedia.org/wiki/Zopfli

    Zopfli is a data compression library that performs Deflate, gzip and zlib data encoding. [2] It achieves higher compression ratios than mainstream Deflate and zlib implementations at the cost of being slower. [3] Google first released Zopfli in February 2013 under the terms of Apache License 2.0. [4]

  4. Graph Fourier transform - Wikipedia

    en.wikipedia.org/wiki/Graph_Fourier_transform

    Analogously to the classical Fourier transform, the eigenvalues represent frequencies and eigenvectors form what is known as a graph Fourier basis. The Graph Fourier transform is important in spectral graph theory. It is widely applied in the recent study of graph structured learning algorithms, such as the widely employed convolutional networks.

  5. Deflate - Wikipedia

    en.wikipedia.org/wiki/DEFLATE

    Within compressed blocks, if a duplicate series of bytes is spotted (a repeated string), then a back-reference is inserted, linking to the previous location of that identical string instead. An encoded match to an earlier string consists of an 8-bit length (3–258 bytes) and a 15-bit distance (1–32,768 bytes) to the beginning of the duplicate.

  6. Transformation matrix - Wikipedia

    en.wikipedia.org/wiki/Transformation_matrix

    (Note that if k > 1, then this really is a "stretch"; if k < 1, it is technically a "compression", but we still call it a stretch. Also, if k = 1, then the transformation is an identity, i.e. it has no effect.) The matrix associated with a stretch by a factor k along the x-axis is given by: []

  7. Lossless compression - Wikipedia

    en.wikipedia.org/wiki/Lossless_compression

    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 ]

  8. S3 Texture Compression - Wikipedia

    en.wikipedia.org/wiki/S3_Texture_Compression

    S3 Texture Compression (S3TC) (sometimes also called DXTn, DXTC, or BCn) is a group of related lossy texture compression algorithms originally developed by Iourcha et al. of S3 Graphics, Ltd. [1] [2] for use in their Savage 3D computer graphics accelerator.

  9. Data compression ratio - Wikipedia

    en.wikipedia.org/wiki/Data_compression_ratio

    Thus, a representation that compresses the storage size of a file from 10 MB to 2 MB 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: