conference paper


Conference/Proceedings21th IEEE International Conference on Image Processing
Start date27.10.2014
End date30.10.2014
AddressParis,France
Pages4807-4811
Author(s)Ruben Verhack, Andreas Krutz , Peter Lambert , Rik Van de Walle, Thomas Sikora
TitleLOSSY IMAGE CODING IN THE PIXEL DOMAIN USING A SPARSE STEERING KERNEL SYNTHESIS APPROACH
AbstractKernel regression has been proven successful for image de-
noising, deblocking and reconstruction. These techniques lay
the foundation for new image coding opportunities. In this pa-
per, we introduce a novel compression scheme:
Sparse Steer-
ing Kernel Synthesis Coding
(SSKSC). This pre- and post-
processor for JPEG performs non-uniform sampling based
on the smoothness of an image, and reconstructs the miss-
ing pixels using adaptive kernel regression. At the same time,
the kernel regression reduces the blocking artifacts from the
JPEG coding. Crucial to this technique is that non-uniform
sampling is performed while maintaining only a small over-
head for signalization. Compared to JPEG, SSKSC achieves
a compression gain for low bits-per-pixel regions of 50% or
more for PSNR and SSIM. A PSNR gain is typically in the
0.0 - 0.5 bpp range, and an SSIM gain can mostly be achieved
in the 0.0 - 1.0 bpp range.
Key wordsimage coding; compression; adaptive sampling; kernel regression; sparse steering kernel synthesis
NoteTop 10% Paper
ISBN: 978-1-4799-5750-7
File1466Verhack2014.pdf

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