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Understanding, Optimising, and Extending Data Compression with Anisotropic Diffusion
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DOI：10.1007/s11263-014-0702-z.png)
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Abstract
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GaliA double dagger et al. (Journal of Mathematical Imaging and Vision 31:255-269, 2008) have shown that compression based on edge-enhancing anisotropic diffusion (EED) can outperform the quality of JPEG for medium to high compression ratios when the interpolation points are chosen as vertices of an adaptive triangulation. However, the reasons for the good performance of EED remained unclear, and they could not outperform the more advanced JPEG 2000. The goals of the present paper are threefold: Firstly, we investigate the compression qualities of various partial differential equations. This sheds light on the favourable properties of EED in the context of image compression. Secondly, we demonstrate that it is even possible to beat the quality of JPEG 2000 with EED if one uses specific subdivisions on rectangles and several important optimisations. These amendments include improved entropy coding, brightness and diffusivity optimisation, and interpolation swapping. Thirdly, we demonstrate how to extend our approach to 3-D and shape data. Experiments on classical test images and 3-D medical data illustrate the high potential of our approach.
Keywords:
Image compression
Edge-enhancing anisotropic diffusion (EED)
Partial differential equations (PDEs)
Subdivision
JPEG 2000
Kanizsa triangle
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