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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.
In contrast, lossy compression (e.g. JPEG for images, or MP3 and Opus for audio) can achieve much higher compression ratios at the cost of a decrease in quality, such as Bluetooth audio streaming, as visual or audio compression artifacts from loss of important information are introduced.
Continuously varied JPEG compression (between Q=100 and Q=1) for an abdominal CT scan. JPEG (/ ˈ dʒ eɪ p ɛ ɡ / JAY-peg, short for Joint Photographic Experts Group and sometimes retroactively referred to as JPEG 1) [2] [3] is a commonly used method of lossy compression for digital images, particularly for those images produced by digital photography.
Beamr was founded by Sharon Carmel, an entrepreneur with a background in image and video processing. [4] Beamr’s clients include companies like Netflix , Viacom CBS, and Snapfish . Its technology is backed by 53 internationally granted patents and was awarded a Technology and Engineering Emmy in 2021 .
This category includes articles, which includes information on image compression methods and algorithms. For information on graphics file formats see Category:Graphics file formats . Subcategories
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
Quantization, involved in image processing, is a lossy compression technique achieved by compressing a range of values to a single quantum (discrete) value. When the number of discrete symbols in a given stream is reduced, the stream becomes more compressible.
JPEG-LS was developed with the aim of providing a low-complexity lossless and near-lossless image compression standard that could offer better compression efficiency than lossless JPEG. It was developed because at the time, the Huffman coding -based JPEG lossless standard and other standards were limited in their compression performance.
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