Colour image compression with anisotropic diffusion

Schmaltz et al. (2009) have shown that for reasonably high compression rates, diffusion-based codecs can exceed the quality of transformation-based methods such as JPEG 2000. They store only data at a few optimised pixel locations and in-paint missing data with edge-enhancing anisotropic diffusion (EED). However, research on compression with diffusion methods has mainly focussed on grey-value images, and colour images have been compressed in a straightforward way using anisotropic diffusion in RGB space. So far, there is no sophisticated diffusion-based counterpart to the colour mode of JPEG 2000. To address this shortcoming we introduce an advanced colour compression codec that exploits properties of the human visual system in YCbCr space. Since details in the luma channel Y are perceptually relevant, we invest a large fraction of our bit budget in its encoding with high fidelity. For the chroma channels Cb and Cr, the stored information can be very sparse, if we guide the EED-based inpainting with the high quality diffusion tensor from the luma reconstruction. Experiments demonstrate that our novel codec outperforms JPEG 2000 and compression with RGB-diffusion, both visually and quantitatively.