<div class="csl-bib-body">
<div class="csl-entry">Feischl, M., & Hackl, H. (2024). Adaptive image compression via optimal mesh refinement. <i>Computational Methods in Applied Mathematics</i>, <i>24</i>(2), 325–343. https://doi.org/10.1515/cmam-2023-0097</div>
</div>
-
dc.identifier.issn
1609-4840
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/209306
-
dc.description.abstract
The JPEG algorithm is a defacto standard for image compression. We investigate whether adaptive mesh refinement can be used to optimize the compression ratio and propose a new adaptive image compression algorithm. We prove that it produces a quasi-optimal subdivision grid for a given error norm with high probability. This subdivision can be stored with very little overhead and thus leads to an efficient compression algorithm. We demonstrate experimentally, that the new algorithm can achieve better compression ratios than standard JPEG compression with no visible loss of quality on many images. The mathematical core of this work shows that Binev’s optimal tree approximation algorithm is applicable to image compression with high probability, when we assume small additive Gaussian noise on the pixels of the image.
en
dc.language.iso
en
-
dc.publisher
WALTER DE GRUYTER GMBH
-
dc.relation.ispartof
Computational Methods in Applied Mathematics
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Adaptive Mesh Refinement
en
dc.subject
Image Compression
en
dc.subject
JPEG
en
dc.subject
Optimality
en
dc.title
Adaptive image compression via optimal mesh refinement