perceptually based efficient rendering
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Using Perceptual Texture Masking for Efficient Image Synthesis
abstract:Texture mapping has become indispensable in image synthesis as an inexpensive source of rich visual detail. Less obvious, but just as useful, is its ability to mask image errors due to inaccuracies in geometry or lighting. This ability can be used to substantially accelerate rendering by eliminating computations when the resulting errors will be perceptually insignificant.Our new method precomputes the masking ability of textures using aspects of the JPEG image compression standard. This extra information is stored as error tolerance elevation factors in the texture's mip-map and interpolated at image generation time as part of the normal texture lookup process. Any algorithm which uses error tolerances or visibility thresholds can then take advantage of texture masking. Applications to adaptive shadow testing, irradiance caching, and path tracing are demonstrated. Unlike prior methods, our approach does not require that initial images be computed before masking can be exploited and incurs only negligible runtime computational overhead. Thus, it is much easier to integrate with existing rendering systems and yields computational savings even when only small amounts of texture masking are present.
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Spatio-Temporal Sensitivity and Visual Attention in Dynamic Environments
abstract:We present a method to accelerate global illumination computation in pre-rendered animations by taking advantage of limitations of the human visual system. A spatiotemporal error tolerance map, constructed from psychophysical data based on velocity dependent contrast sensitivity, is used to accelerate rendering. The error map is augmented by a model of visual attention in order to account for the tracking behavior of the eye. Perceptual acceleration combined with good sampling protocols provide a global illumination solution feasible for use in animation. Results indicate an order of magnitude improvement in computational speed.
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A Perceptually Based Physical Error Metric for Realistic Image Synthesis
abstract:We introduce a new concept for accelerating realistic image synthesis algorithms. At the core of this procedure is a novel physical error metric that correctly predicts the perceptual threshold for detecting artifacts in scene features. Built into this metric is a computational model of the human visual system’s loss of sensitivity at high background illumination levels, high spatial frequencies, and high contrast levels (visual masking). An important feature of our model is that it handles the luminance-dependent processing and spatiallydependent processing independently. This allows us to precompute the expensive spatially-dependent component, making our model extremely efficient. We illustrate the utility of our procedure with global illumination algorithms used for realistic image synthesis. The expense of global illumination computations is many orders of magnitude higher than the expense of direct illumination computations and can greatly benefit by applying our perceptually based technique. Results show our method preserves visual quality while achieving significant computational gains in areas of images with high frequency texture patterns, geometric details, and lighting variations.
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A Model of Visual Masking for Computer Graphics
abstract:In this paper we develop a computational model of visual masking based on psychophysical data. The model predicts how the presence of one visual pattern affects the detectability of another. The model allows us to choose texture patterns for computer graphics images that hide the effects of faceting, banding, aliasing, noise and other visual artifacts produced by sources of error in graphics algorithms. We demonstrate the utility of the model by choosing a texture pattern to mask faceting artifacts caused by tesselation of a flat-shaded curved surface. The model predicts how changes in the contrast, spatial frequency, and orientation of the texture pattern, or changes in the tesselation of the surface will alter the masking effect. The model is general and has uses in geometric modeling, realistic image synthesis, scientific visualization, image compression, and image-based rendering.
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| university of central florida : graphics group |
