Editing Burrows–Wheeler transform (section)

===BWT for image compression===
The Burrows–Wheeler transformation has proved to be fundamental for [[image compression]] applications. For example,<ref name="Collin, P2019">{{cite book |vauthors=Collin P, Arnavut Z, Koc B |chapter=Lossless compression of medical images using Burrows–Wheeler Transformation with Inversion Coder |chapter-url=https://ieeexplore.ieee.org/document/7319012| title=2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)|journal=<!-- --> |date=2015 |volume=2015 |pages=2956–2959 |pmid=26736912 |doi=10.1109/EMBC.2015.7319012 |isbn=978-1-4244-9271-8 |s2cid=4460328 }}</ref> Showed a compression pipeline based on the application of the Burrows–Wheeler transformation followed by inversion, run-length, and arithmetic encoders. The pipeline developed in this case is known as Burrows–Wheeler transform with an inversion encoder (BWIC). The results shown by BWIC are shown to outperform the compression performance of well-known and widely used algorithms like [[Lossless JPEG]] and [[JPEG 2000]]. BWIC is shown to outperform those in terms of final compression size of radiography medical images on the order of 5.1% and 4.1% respectively. The improvements are achieved by combining BWIC and a pre-BWIC scan of the image in a vertical snake order fashion. More recently, additional works have shown the implementation of the Burrows–Wheeler Transform in conjunction with the known [[move-to-front transform]] (MTF) achieve near lossless compression of images. <ref name="Devadoss, CP2019">{{cite journal |vauthors=Devadoss CP, Sankaragomathi B |title=Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques |url=https://link.springer.com/article/10.1007/s10586-018-1801-3| journal=Cluster Computing| date=2019| volume=22 |pages=12929–12937 |doi=10.1007/s10586-018-1801-3|s2cid=33687086 }}</ref>