@INPROCEEDINGS{1466Verhack2014,
	AUTHOR = {Ruben Verhack and Andreas Krutz  and Peter Lambert  and Rik Van de Walle and Thomas Sikora},
	TITLE = {[Top 10% Paper] LOSSY IMAGE CODING IN THE PIXEL DOMAIN USING A SPARSE STEERING KERNEL SYNTHESIS APPROACH},
	BOOKTITLE = {21th IEEE International Conference on Image Processing},
	YEAR = {2014},
	MONTH = oct,
	PAGES = {4807--4811},
	ADDRESS = {Paris,France},
	NOTE = {ISBN: 978-1-4799-5750-7},
	PDF = {http://elvera.nue.tu-berlin.de/files/1466Verhack2014.pdf},
	ABSTRACT = {Kernel 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.}
}