Abstract: An electronic correction system and method for correcting optical anomalies, namely distortions, color non-convergence (excluding axial chromatic aberration) and luminance (or chrominance) non-uniformity. Each effect is modeled as a transformation in either spatial (positional) space or color space. Representing the effects as transformations of digital pixel data, allows the different anomalies to be resolved within a common framework, namely that of image ‘warping’. The anomaly, having been expressed as a pixel transformation, is then eliminated by electronically applying the inverse transformation. This process is equivalent to digitally manipulating or warping the image in position and/or color space and accordingly this can be achieved using commercially known warping circuits. In addition, the transformation can also contain a component to additionally perform any application specific image warping (e.g. scaling and geometric transformations).

Abstract: A short throw projection system and method for displaying a corrected optical image on a projection screen based on input image data that includes an electronic correction unit, an image projector and a reflection assembly. The electronic correction unit receives the input image data and generates pre-distorted image data. The image projector receives the pre-distorted image data from the electronic correction unit and projects a pre-distorted optical image that corresponds to the pre-distorted image data or a pre-distorted image compensated by the projection optic distortion. The optical reflection assembly is positioned in the optical path of the pre-distorted optical image to project an optical image on the projection screen. The reflection assembly can consist of various combinations of curved and planar mirrors as desired.

Abstract: An image transformation method for translating a non-linear 2D geometrical transformation into two separable 1D geometrical transformations first determines the inverse of the 2D geometrical transformation to form an inverse 2D geometrical transformation. Then the method converts the inverse 2D geometrical transformation into an analytical inverted 2D geometrical transformation and separates the analytical inverse 2D geometrical transformation into first and second 1D geometrical transformations. The method then represents said inverse 2D geometrical transformation and first and second 1D geometrical transformations as tensor spline surfaces and then compares an evaluation of said first and second 1D geometrical transformations at each pixel with an evaluation of the analytical inverse 2D geometrical transformation at each pixel. If the error evaluation does not meet a predetermined level of performance then the separation and transformation steps are repeated.