Sparse PDF Maps for Non-Linear Multi-Resolution Image Operations

Jens Krüger; Markus Hadwiger; Ronell Sicat; Johanna Beyer; Torsten Möller

In: Julie Dorsey (Hrsg.). ACM Transactions on Graphics (TOG), Vol. 31, Pages 133:1-133:12, ACM, 2012.


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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence