Sparse PDF maps for non-linear multi-resolution image operations

  • Authors:
  • Markus Hadwiger;Ronell Sicat;Johanna Beyer;Jens Krüger;Torsten Möller

  • Affiliations:
  • KAUST;KAUST;KAUST;IVDA, DFKI, Intel VCI;Simon Fraser University

  • Venue:
  • ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
  • Year:
  • 2012

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Abstract

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.