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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.
Guetzli optimizes the quantization step of encoding to achieve compression efficiency. It constructs custom quantization tables for each file, decides on color subsampling, [4] and quantizes adjacent DCT coefficients to zero, balancing benefits in the run-length encoding of coefficients and preservation of perceived image fidelity.
JBIG is an early lossless image compression standard from the Joint Bi-level Image Experts Group, standardized as ISO/IEC standard 11544 and as ITU-T recommendation T.82 in March 1993. [1] It is widely implemented in fax machines. Now that the newer bi-level image compression standard JBIG2 has been released, JBIG is also known as JBIG1.
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
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
Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but it does not generally achieve compression ratio much better than 2:1 because of the intrinsic entropy of the data. Compression algorithms which provide higher ratios either incur very large overheads or work only for specific data ...
The founders later talked to Irving Reed at the University of Southern California, who had an idea for an improved image compression algorithm, and started implementing such an algorithm; this became the ART image file format. [1] The company was acquired by AOL on February 1, 1996, for approximately 1.6 million shares of stock. [2]
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.