Editing Digital image processing (section)

===Image compression===
{{Main|Image compression}}

An important development in digital [[image compression]] technology was the [[discrete cosine transform]] (DCT), a [[lossy compression]] technique first proposed by [[N. Ahmed|Nasir Ahmed]] in 1972.<ref name="Ahmed">{{cite journal |last=Ahmed |first=Nasir |author-link=N. Ahmed |title=How I Came Up With the Discrete Cosine Transform |journal=[[Digital Signal Processing (journal)|Digital Signal Processing]] |date=January 1991 |volume=1 |issue=1 |pages=4–5 |doi=10.1016/1051-2004(91)90086-Z |bibcode=1991DSP.....1....4A |url=https://www.scribd.com/doc/52879771/DCT-History-How-I-Came-Up-with-the-Discrete-Cosine-Transform |access-date=10 October 2019 |archive-url=https://web.archive.org/web/20160610013109/https://www.scribd.com/doc/52879771/DCT-History-How-I-Came-Up-with-the-Discrete-Cosine-Transform |archive-date=10 June 2016 |url-status=live }}</ref> DCT compression became the basis for [[JPEG]], which was introduced by the [[Joint Photographic Experts Group]] in 1992.<ref name="t81">{{cite web |title=T.81 – Digital compression and coding of continuous-tone still images – requirements and guidelines |url=https://www.w3.org/Graphics/JPEG/itu-t81.pdf |publisher=[[CCITT]] |date=September 1992 |access-date=12 July 2019 |archive-url=https://web.archive.org/web/20190717052727/http://www.w3.org/Graphics/JPEG/itu-t81.pdf |archive-date=17 July 2019 |url-status=live }}</ref> JPEG compresses images down to much smaller file sizes, and has become the most widely used [[image file format]] on the [[Internet]].<ref>{{cite web |title=The JPEG image format explained |url=https://home.bt.com/tech-gadgets/photography/what-is-a-jpeg-11364206889349 |publisher=[[BT Group]] |first1= Joe |last1=Svetlik  |access-date=5 August 2019 |date=31 May 2018 |archive-url=https://web.archive.org/web/20190805194553/https://home.bt.com/tech-gadgets/photography/what-is-a-jpeg-11364206889349 |archive-date=5 August 2019 |url-status=dead }}</ref> Its highly efficient DCT compression algorithm was largely responsible for the wide proliferation of [[digital images]] and [[digital photo]]s,<ref name="Atlantic">{{cite web |date=24 September 2013 |title=What Is a JPEG? The Invisible Object You See Every Day |url=https://www.theatlantic.com/technology/archive/2013/09/what-is-a-jpeg-the-invisible-object-you-see-every-day/279954/ |first1=Paul |last1=Caplan |url-access=subscription |url-status=live |archive-url=https://web.archive.org/web/20191009054159/https://www.theatlantic.com/technology/archive/2013/09/what-is-a-jpeg-the-invisible-object-you-see-every-day/279954/ |archive-date=9 October 2019 |access-date=13 September 2019 |website=[[The Atlantic]]}}</ref> with several billion JPEG images produced every day {{as of|2015|lc=y}}.<ref>{{cite news |last1=Baraniuk |first1=Chris |title=JPeg lockdown: Restriction options sought by committee  |url=https://www.bbc.co.uk/news/technology-34538705 |access-date=13 September 2019 |publisher=[[BBC News]]|date=15 October 2015 |archive-url=https://web.archive.org/web/20191009193610/https://www.bbc.co.uk/news/technology-34538705 |archive-date=9 October 2019 |url-status=live }}</ref>

Medical imaging techniques produce very large amounts of data, especially from CT, MRI and PET modalities. As a result, storage and communications of electronic image data are prohibitive without the use of compression.<ref>{{Cite journal |last1=Nagornov |first1=Nikolay N. |last2=Lyakhov |first2=Pavel A. |last3=Valueva |first3=Maria V. |last4=Bergerman |first4=Maxim V. |date=2022 |title=RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients |s2cid-access=free |journal=IEEE Access |volume=10 |pages=19215–19231 |doi=10.1109/ACCESS.2022.3151361 |issn=2169-3536 |s2cid=246895876 |quote=Medical imaging systems produce increasingly accurate images with improved quality using higher spatial resolutions and color bit-depth. Such improvements increase the amount of information that needs to be stored, processed, and transmitted. |doi-access=free|bibcode=2022IEEEA..1019215N }}</ref><ref>{{Cite journal |last1=Dhouib |first1=D. |last2=Naït-Ali |first2=A. |last3=Olivier |first3=C. |last4=Naceur |first4=M.S. |date=June 2021 |title=ROI-Based Compression Strategy of 3D MRI Brain Datasets for Wireless Communications |url=https://linkinghub.elsevier.com/retrieve/pii/S1959031820300853 |journal=IRBM |language=en |volume=42 |issue=3 |pages=146–153 |doi=10.1016/j.irbm.2020.05.001 |s2cid=219437400 |quote=Because of the large amount of medical imaging data, the transmission process becomes complicated in telemedicine applications. Thus, in order to adapt the data bit streams to the constraints related to the limitation of the bandwidths a reduction of the size of the data by compression of the images is essential.}}</ref> [[JPEG 2000]] image compression is used by the [[DICOM]] standard for storage and transmission of medical images. The cost and feasibility of accessing large image data sets over low or various bandwidths are further addressed by use of another DICOM standard, called [[JPIP]], to enable efficient streaming of the [[JPEG 2000]] compressed image data.<ref>{{Cite journal |last1=Xin |first1=Gangtao |last2=Fan |first2=Pingyi |date=2021-06-11 |title=A lossless compression method for multi-component medical images based on big data mining |journal=Scientific Reports |language=en |volume=11 |issue=1 |pages=12372 |doi=10.1038/s41598-021-91920-x |issn=2045-2322|doi-access=free |pmid=34117350 |pmc=8196061 }}</ref>