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Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.
JPEG XR [4] (JPEG extended range [5]) is an image compression standard for continuous tone photographic images, based on the HD Photo (formerly Windows Media Photo) specifications that Microsoft originally developed and patented. [6]
Composite image showing JPG and PNG image compression. Left side of the image is from a JPEG image, showing lossy artefacts; the right side is from a PNG image. In the late 1980s, digital images became more common, and standards for lossless image compression emerged. In the early 1990s, lossy compression methods began to be widely used. [14]
JBIG2 is an image compression standard for bi-level images, developed by the Joint Bi-level Image Experts Group.It is suitable for both lossless and lossy compression. . According to a press release [1] from the Group, in its lossless mode JBIG2 typically generates files 3–5 times smaller than Fax Group 4 and 2–4 times smaller than JBIG, the previous bi-level compression standard released by
Lossless compression should be used to avoid accumulating stages of re-compression when editing images. Lossy compression algorithms preserve a representation of the original uncompressed image that may appear to be a perfect copy, but is not a perfect copy. Often lossy compression is able to achieve smaller file sizes than lossless compression.
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Brotli is a compression algorithm developed by Google for textual web content, and typically achieves higher compression ratios than other algorithms for this use case. .bz2 application/x-bzip2 bzip2: Unix-like An open source, patent- and royalty-free compression format.
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.