The topics for Part 1 of 4.

Rendering systems can produce images that include the entire range of visible colors. Imaging hardware, however, can reproduce only a subset of these colors: the device gamut. An image can only be correctly displayed if all of its colors lie inside of the gamut of the target device. Current solutions to this problem are either to correct the scene colors by hand, or to apply gamut mapping techniques to the final image. We propose a methodology called device-directed rendering that performs scene color adjustments automatically. Device-directed rendering applies classic minimization techniques to a symbolic representation of the image that describes the relationship of the scene lights and surfaces to the pixel colors. This representation can then be evaluated to produce an image that is guaranteed to be in gamut. Although our primary application has been correcting out-of-gamut colors, this methodology can be generally applied to the problem of adjusting a scene description to accommodate constraints on the output image pixel values.

Description: Many two-dimensional graphics programs provide a user with a rectangular screen window for viewing a two-dimensional image. Common examples of this underlying “world” image include text, line, or shaded pictures, and plots of one- or two-dimensional functions. Typically the screen image cannot display the world image with a 1:1 ratio between screen units (pixels) and the smallest resolvable world units. Thus the screen image is typically scaled and panned across the world image. The scaling is often differential, i.e., different scaling factors are applied in X and Y. Suppose a user is viewing a function y = f(x) at a ratio of 1:1. Increasing the scale factor in X will bring more data into view, while retaining vertical amplitude; increasing the scale factor in Y will provide a finer view of the values of the function, while retaining the range plotted. We have developed a compact control device which allows a user to continuously adjust the aspect ratio of the world data presented to the window. Our model is based on the projection of the window on the untouched world data. If the screen window is narrow and tall in the world data, then the world data will be expanded horizontally and compressed vertically when displayed on the screen (note that the window itself never changes size on the screen). Accompanying the aspect ratio selection is a zoom multiplier, which can uniformly grow or shrink the screen window's image in the world. We also include variable-speed scrolling controls. Scrolling and uniform zooming are decoupled from differential scaling. The advantage of our technique is that the user need not independently scale X and Y while searching for the proper scaling of data; although both may be adjusted individually, and may also be adjusted simultaneously in a coupled, single-position device. Thus a single-button input device (such as a mouse) is all that is needed to control any aspect of the display. The technique has the additional advantage of being nonmodal, so the user need not remember any state during operation.

We introduce an adaptive, iterative technique for obtaining texture samples of arbitrary precision when synthesizing a computer-generated image. The technique is an improvement on the sum table texturing method. To motivate the technique we analyze the error properties of the sum-table method. Based on that analysis we propose using a combination of tables independently or together to obtain a better estimate, and analyze the error properties of such methods. We then propose a new technique for obtaining texture samples whose accuracy is a function of the texture and the image. As part of this technique we propose the use of an auxiliary table which contains local estimates of the texture variance. We show how the iteration of a given sample may be controlled by values from this table. We then analyze the error in this method, and present images which demonstrate the improvement.