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[Top 10% Paper] LOSSY IMAGE CODING IN THE PIXEL DOMAIN USING A SPARSE STEERING KERNEL SYNTHESIS APPROACH
Citation key 1466Verhack2014
Author Ruben Verhack and Andreas Krutz and Peter Lambert and Rik Van de Walle and Thomas Sikora
Title of Book 21th IEEE International Conference on Image Processing
Pages 4807–4811
Year 2014
Address Paris,France
Month oct
Note ISBN: 978-1-4799-5750-7
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.
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