Please use this identifier to cite or link to this item:
doi:10.22028/D291-26365
Title: | Image compression with anisotropic diffusion |
Author(s): | Galic, Irena Weickert, Joachim Welk, Martin Bruhn, Andrés Belyaev, Alexander Seidel, Hans-Peter |
Language: | English |
Year of Publication: | 2008 |
DDC notations: | 510 Mathematics |
Publikation type: | Other |
Abstract: | 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 to this record: | urn:nbn:de:bsz:291-scidok-47339 hdl:20.500.11880/26421 http://dx.doi.org/10.22028/D291-26365 |
Series name: | Preprint / Fachrichtung Mathematik, Universität des Saarlandes |
Series volume: | 203 |
Date of registration: | 21-Mar-2012 |
Faculty: | MI - Fakultät für Mathematik und Informatik |
Department: | MI - Mathematik |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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File | Description | Size | Format | |
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preprint_203_08.pdf | 1,45 MB | Adobe PDF | View/Open |
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