Spatial Domain Characterization and Control of Reconstruction Errors
Reconstruction is imperative whenever an image or a volume needs to be resampled as a result of an affine or perspective transformation, texture mapping, or volume rendering. We present a new method for the characterization and measurement of reconstruction error. Our method, based on spatial domain error analysis, uses approximation theory to develop error bounds. We provide, for the first time, an efficient way to guarantee an error bound at every point by varying the filter size. We go further to support position-adaptive and data-adaptive reconstruction which adjust filter size to the location of reconstruction and the data in its vicinity. We demonstrate the effectiveness of our methods with suitable 2D and 3D examples.
KeywordsComputer Graphic Truncation Error Reconstruction Error Neighborhood Size Spectral Energy
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