Bitte benutzen Sie diese Referenz, um auf diese Ressource zu verweisen: doi:10.22028/D291-26365
Titel: Image compression with anisotropic diffusion
Verfasser: Galic, Irena
Weickert, Joachim
Welk, Martin
Bruhn, Andrés
Belyaev, Alexander
Seidel, Hans-Peter
Sprache: Englisch
Erscheinungsjahr: 2008
DDC-Sachgruppe: 510 Mathematik
Dokumentart : Preprint (Vorabdruck)
Kurzfassung: Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel framework for image compression that makes use of the interpolation qualities of edge-enhancing diffusion. Although this anisotropic diffusion equation with a diffusion tensor was originally proposed for image denoising, we show that it outperforms many other PDEs when sparse scattered data must be interpolated. To exploit this property for image compression, we consider an adaptive triangulation method for removing less significant pixels from the image. The remaining points serve as scattered interpolation data for the diffusion process. They can be coded in a compact way that reflects the B-tree structure of the triangulation. We supplement the coding step with a number of amendments such as error threshold adaptation, diffusion-based point selection, and specific quantisation strategies. Our experiments illustrate the usefulness of each of these modifications. They demonstrate that for high compression rates, our PDE-based approach does not only give far better results than the widely-used JPEG standard, but can even come close to the quality of the highly optimised JPEG2000 codec.
Link zu diesem Datensatz: urn:nbn:de:bsz:291-scidok-47339
Schriftenreihe: Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Band: 203
SciDok-Publikation: 21-Mär-2012
Fakultät: Fakultät 6 - Naturwissenschaftlich-Technische Fakultät I
Fachrichtung: MI - Mathematik
Fakultät / Institution:MI - Fakultät für Mathematik und Informatik

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
preprint_203_08.pdf1,45 MBAdobe PDFÖffnen/Anzeigen

Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt.