In this paper we develop a computational model of visual masking based on psychophysical data. The model predicts howthepresenceofonevisualpatternaectsthedetectabil- ity of another. The model allows us to choose texture pat- terns for computer graphics images that hide the eects 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 polygonal tes- selation of aflat-shaded curved surface. The model predicts how changes in the contrast, spatial frequency, and orien- tation of the texture pattern, or changes in the tesselation of the surface will alter the masking eect. The model is general and has uses in geometric modeling, realistic image synthesis, scientic visualization, image compression, and image-based rendering.