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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.
Block Truncation Coding (BTC) is a type of lossy image compression technique for greyscale images. It divides the original images into blocks and then uses a quantizer to reduce the number of grey levels in each block whilst maintaining the same mean and standard deviation .
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
This is an accepted version of this page This is the latest accepted revision, reviewed on 31 December 2024. Lossy compression method for reducing the size of digital images For other uses, see JPEG (disambiguation). "JPG" and "Jpg" redirect here. For other uses, see JPG (disambiguation). JPEG A photo of a European wildcat with the compression rate, and associated losses, decreasing from left ...
Composite image showing JPG and PNG image compression. Left side of the image is from a low-quality JPEG image, showing lossy artefacts; the right side is from a PNG image. In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data ...
It implements a JPEG codec (encoding and decoding) alongside various utilities for handling JPEG data. It is written in C and distributed as free software together with its source code under the terms of a custom permissive (BSD-like) free software license, which demands attribution. The original variant is maintained and published by the ...
Set partitioning in hierarchical trees (SPIHT) [1] is an image compression algorithm that exploits the inherent similarities across the subbands in a wavelet decomposition of an image. The algorithm was developed by Brazilian engineer Amir Said with William A. Pearlman in 1996. [1]
It is a simple and efficient baseline algorithm which consists of two independent and distinct stages called modeling and encoding. 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.