Editing Image compression (section)

== Lossy and lossless image compression ==
Image compression may be [[lossy compression|lossy]] or [[lossless compression|lossless]].  Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, [[clip art]], or comics. Lossy compression methods, especially when used at low [[bit rate]]s, introduce [[compression artifact]]s.  Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless.

Methods for [[lossy compression]]:

* [[Transform coding]] – This is the most commonly used method.
** [[Discrete Cosine Transform]] (DCT) – The most widely used form of lossy compression. It is a type of [[List of Fourier-related transforms|Fourier-related transform]], and was originally developed by [[N. Ahmed|Nasir Ahmed]], T. Natarajan and [[K. R. Rao]] in 1974.<ref>{{cite journal|doi=10.1109/T-C.1974.223784|url=http://dasan.sejong.ac.kr/~dihan/dip/p5_DCT.pdf | title=Discrete Cosine Transform|date=1974 |archive-url=https://web.archive.org/web/20111125071212/http://dasan.sejong.ac.kr/~dihan/dip/p5_DCT.pdf |archive-date=2011-11-25 |last1=Ahmed |first1=N. |last2=Natarajan |first2=T. |last3=Rao |first3=K.R. |journal=IEEE Transactions on Computers |pages=90–93 |s2cid=149806273 }}</ref> The DCT is sometimes referred to as "DCT-II" in the context of a family of discrete cosine transforms (see [[discrete cosine transform]]). It is generally the most efficient form of image compression.
*** DCT is used in [[JPEG]], the most popular lossy format, and the more recent [[HEIF]].
** The more recently developed [[wavelet transform]] is also used extensively, followed by [[Quantization (image processing)|quantization]] and [[entropy coding]].
* [[Color quantization]] - Reducing the [[color space encoding|color space]] to a few "representative" colors in the image. The selected colors are specified in the color [[palette (computing)|palette]] in the header of the compressed image. Each pixel just references the index of a color in the color palette. This method can be combined with [[dithering]] to avoid [[posterization]].
** Whole-image palette, typically 256 colors, used in GIF and PNG file formats.
** block palette, typically 2 or 4 colors for each block of 4x4 pixels, used in [[Block Truncation Coding|BTC]], [[Color Cell Compression|CCC]], [[S2TC]], and [[S3 Texture Compression|S3TC]].
* [[Chroma subsampling]]. This takes advantage of the fact that the human eye perceives spatial changes of brightness more sharply than those of color, by averaging or dropping some of the chrominance information in the image.
* [[Fractal compression]].
* More recently, methods based on [[Machine Learning]] were applied, using [[Multilayer perceptron]]s, [[Convolutional neural network]]s, [[Generative adversarial network]]s<ref>{{cite web |author1=Gilad David Maayan |title=AI-Based Image Compression: The State of the Art |url=https://towardsdatascience.com/ai-based-image-compression-the-state-of-the-art-fb5aa6042bfa |website=Towards Data Science |access-date=6 April 2023 |date=Nov 24, 2021}}</ref> and [[Diffusion model]]s.<ref>{{Cite web |last=Bühlmann |first=Matthias |date=2022-09-28 |title=Stable Diffusion Based Image Compression |url=https://pub.towardsai.net/stable-diffusion-based-image-compresssion-6f1f0a399202 |access-date=2022-11-02 |website=Medium |language=en}}</ref> Implementations are available in [[OpenCV]], [[TensorFlow]], [[MATLAB]]'s Image Processing Toolbox (IPT), and the [[High-Fidelity Generative Image Compression]] (HiFiC) open source project.<ref>{{cite web |title=High-Fidelity Generative Image Compression |url=https://hific.github.io/ |access-date=6 April 2023}}</ref>

Methods for [[lossless compression]]:

* [[Run-length encoding]] – used in default method in [[PCX]] and as one of possible in [[BMP file format|BMP]], [[.tga|TGA]], [[TIFF]]
* Predictive coding – used in [[DPCM]]
* [[Entropy encoding]] – the two most common entropy encoding techniques are [[arithmetic coding]] and [[Huffman coding]]
* Adaptive dictionary algorithms such as [[LZW]] – used in [[Graphics Interchange Format|GIF]] and [[TIFF]]
* [[DEFLATE]] – used in [[Portable Network Graphics|PNG]], [[Multiple-image Network Graphics|MNG]], and [[TIFF]]
* [[Chain code]]s