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We present a new, single-pass nonlinear filter for edge-preserving smoothing and visual detail removal for N dimensional signals in computer graphics, image processing and computer vision applications. Built from two modified forms of Tomasi and Manduchi's bilateral filter, the new "trilateral" filter smoothes signals towards a sharply-bounded, piecewise-linear approximation. Unlike bilateral filters or anisotropic diffusion methods that smooth towards piecewise constant solutions, the trilateral filter provides stronger noise reduction and better outlier rejection in high-gradient regions, and it mimics the edge-limited smoothing behavior of shock-forming PDEs by region finding with a fast min-max stack. Yet the trilateral filter requires only one user-set parameter, filters an input signal in a single pass, and does not use an iterative solver as required by most PDE methods. Like the bilateral filter, the trilateral filter easily extends to N-dimensional signals, yet it also offers better performance for many visual applications including appearance-preserving contrast reduction problems for digital photography and denoising polygonal meshes.

Human figures have been animated using a variety of geometric models including stick figures, polygonal models, and NURBS-based models with muscles, flexible skin, or clothing. This paper reports on experimental results indicating that a viewer's perception of motion characteristics is affected by the geometric model used for rendering. Subjects were shown a series of paired motion sequences and asked if the two motions in each pair were the same or different. The motion sequences in each pair were rendered using the same geometric model. For the three types of motion variation tested, sensitivity scores indicate that subjects were better able to observe changes with the polygonal model than they were with the stick figure model.

High contrast images are common in night scenes and other scenes that include dark shadows and bright light sources. These scenes are difficult to display because their contrasts greatly exceed the range of most display devices for images. As a result, the image contrasts are compressed or truncated, obscuring subtle textures and details. Humans view and understand high contrast scenes easily, "adapting" their visual response to avoid compression or truncation with no apparent loss of detail. By imitating some of these visual adaptation processes, we developed methods for the improved display of high-contrast images. The first builds a display image from several layers of lighting and surface properties. Only the lighting layers are compressed, drastically reducing contrast while preserving much of the image detail. This method is practical only for synthetic images where the layers can be retained from the rendering process. The second method interactively adjusts the displayed image to preserve local contrasts in a small "foveal" neighborhood. Unlike the first method, this technique is usable on any image and includes a new tone reproduction operator. Both methods use a sigmoid function for contrast compression. This function has no effect when applied to small signals but compresses large signals to fit within an asymptotic limit. We demonstrate the effectiveness of these approaches by comparing processed and unprocessed images.