Lossy image coding in the pixel domain using a sparse steering kernel synthesis approach

TLDR: This paper introduces a novel compression scheme: Sparse Steering Kernel Synthesis Coding (SSKSC), which performs non-uniform sampling based on the smoothness of an image, and reconstructs the missing pixels using adaptive kernel regression.

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 paper, we introduce a novel compression scheme: Sparse Steering Kernel Synthesis Coding (SSKSC). This pre- and postprocessor for JPEG performs non-uniform sampling based on the smoothness of an image, and reconstructs the missing 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 overhead 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.