CPU, SMP and GPU implementations of Nohalo level 1, a fast co-convex antialiasing image resampler

This article introduces Nohalo level 1 (“Nohalo”), the simplest member of a family of image resamplers which straighten diagonal interfaces without adding noticeable nonlinear artifacts. Nohalo is interpolatory, co-monotone, coconvex, antialiasing, local average preserving, continuous, and exact on linears. Like many edge-enhancing methods, Nohalo has two main stages: first, nonlinear interpolation is used to create a double-density version of the original image; this doubledensity image is then resampled with bilinear interpolation. Nohalo is especially suited for GPU computing because the nonlinear slopes can be computed once and stored in a low bit-depth texture without rounding error, because the final bilinear stage can be performed in hardware, and because monotonicity allows full use of the texture’s dynamic range. Demand-driven implementations for CPUs and SMPs are more complex, and require extra work to fix bottlenecks. Efficient implementations of the minmod functio...

• Type: Conference
• Year: 2009
• Where: C3S2E
• Authors: Nicolas Robidoux, Minglun Gong, John Cupitt, Adam Turcotte, Kirk Martinez
• Keywords: C3S2E 2009, Graphics Library, Noticeable Nonlinear Artifacts, Software Engineering, final Bilinear Stage