<title>Antialiasing-warped imagery using lookup-table-based methods for adaptive resampling</title>

Abstract

In a previous paper1, the authors demonstrated how image warping could be implemented using a pair of two-dimensional (2-D) spatial look-up tables. In that paper, the focus was on generating the tables, not on antialiasing the results. In this paper, the authors present a method for generating resampling kernels from the spatial look-up tables for the purpose of reducing aliasing. The method generates resampling kernels based on the reduction in spatial frequency content that must take place in order to produce an antialiased warped result. Since the reduction in spatial frequency required for each output pixel can be unique, the method is adaptive.

Building a large-scale high-resolution tiled rear-projected passive stereo display system based on commodity components

“…A small mismatch of tile edges will then result in only a small variation across the overlapping region. The method we implemented was based on [15,16] -essentially a Gaussian falloff is applied to a fixed-width border around the edge of the pre-warped image. This was incorporated into Chromium (discussed below) using alpha blending of a precomputed fading map with the rendered image.…”

Generalized image warping using enhanced lookup tables

“…Inverse mapping avoids this disadvantage by mapping each output pixel to a location on the input image, then using interpolation [8] to determine the final output pixel once and for all. This approach requires writing the output pixel value only once and ensures that each output pixel is assigned a value.…”

Texture mapping 3D models of real-world scenes

“…Some of the more sophisticated techniques that have been used to anti-alias texture-mapped images are cited here for completeness. They include the following: trilinear interpolation on hierarchical resolution texture patterns (called pyramids [Tanimoto and Pavlidis 1975;Burt 1981] and MIP maps [Williams 1983]), elliptically weighted averaging (EWA) [Greene and Heckbert 1986], spatially variant filtering [Fournier and Fiume 1988], summed area tables [Crow 1984;Glassner 1986], repeated integration [Heckbert 1986a], clamping [Norton et al 1982], super-resolution sampling, stochastic and jittered sampling [Cook 1986;Dippe 1985], adaptive sampling [Painter and Sloane 1989], A-buffering [Carpenter 1984], accumulation-buffering [Haeberli and Akeley 1990], and spatial transformation lookup tables [Walterman and Weinhaus 1991]. Heckbert [1986b] has presented a review and comparison of many of these anti-aliasing techniques.…”