Two Methods for Display of High Contrast Images

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 two 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

Earthquake

Do you remember the earthquake of \u2746? Do you remember how the chimney fell through the roof of the elementary school and down through both storeys of classrooms and would have killed us all if this had not been a Sunday morning? Do you remember how the post office, which was the only brick building in the entire valley, collapsed in a heap of rubble where it had stood for 23 years, and how we were thrilled to think afterwards that it looked exactly as if it might have been bombed from the air? And how the bells on the little Anglican church went chiming, and the electric poles whipped back and forth like fly-fishermen\u27s rods, and electric wires hooped low like skipping ropes and snapped tight and clearly sang, and how the earth came rolling up in waves and sent Cornelius Baxter\u27s car out of control and up onto Millie Weston\u27s porch

Interview

Jeanne Delbaere interviewed Jack Hodgins in Nanaimo, Vancouver Island, on 7 September 1987

Interview and Extract from Innocent Cities

Interview and Extract from Innocent Citie

Perception of human motion with different geometric models

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

Do Geometric Models Affect Judgments of Human Motion?

Human figures have been animated using a wide variety of geometric models including stick figures, polygonal models, and NURBS-based models with muscles, flexible skin, or clothing. This paper reports on experiments designed to ascertain whether 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 two motion sequences in each pair used the same geometric model. For each trial, the pairs of motion sequences were grouped into two sets where one set was rendered with a stick figure model and the other set was rendered with a polygonal model. Sensitivity measures for each trial indicate that for these sequences subjects were better able to discriminate motion variations with the polygonal model than with the stick figure model

Two Methods for Display of High Contrast Images

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 two 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 e ect when applied to small signals but compresses large signals to fit within an asymptotic limit. We demonstrate the effectiveness of these approaches by comparing processe

Two Methods for Display of High Contrast Images

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 two 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