Anti- aliasing: Most aliasing artifacts, when appear in a static image at a moderate resolution, are often tolerable, and in many cases, negligible. However, they can have a significant impact on our viewing experience when left untreated in a series if images that animate moving object. For example, a line being rotated around one of its endpoints becomes a rotating escalator with length- altering steps. A moving object with small parts or surface details may have some of those features intermittently change shape or even disappear. Although increasing image resolution is a straightforward way to decreases the size of many aliasing artifacts and alleviate their negative, we pay a heavy price in terms of system resource (going fron W X H to 2W X 2H means quadrupling the number of pixel) and the results are not always satisfactory. On the other hand, there are techniques that can greatly reduce aliasing artifacts and improve the appearance of image without increasing their resolution, these techniques are collectively referred to as anti- aliasing techniques are designed to treat a particular type of artifact. For instance, an outline font can be associated with a set of rules or hints to guide the adjustment and realignment that is necessary for its conversion into bitmaps of relatively low resolution, an example of such approach is called the True Type font.
Pre- filtering and post- filtering: Pre- filtering and post- filtering are two types of general- purpose anti- aliasing techniques. The concept of filtering originates from the field of signal processing, where true intensity values are continuous signals that consists if elements of various frequencies. Constant intensity values that correspond to a uniform region are at the low end of the spectrum. In order to lessen the jagged appearance of lines and other contours in the image space, we seek to smooth out sudden intensity changes, or in signal- processing terms, to filter out the high frequency components. A pre- filtering technique works on the true signal in the continuous space to derive proper values for individual pixels (filtering before sampling), whereas a post- filtering technique takes discrete samples of the continuous signal and uses the samples to computer pixel values (sampling before filtering).
Area Sampling: Area sampling is a pre- filtering technique in which we superimpose a pixel grid pattern onto the continuous object definition. For each pixel area that intersects the object, we calculate the percentage of overlap by the object. This percentage determines the proportion of the overall intensity value of the corresponding pixel that is due to the object's contribution. In other words, the higher the percentage of overlap, the greater influence the object has on the pixel's overall intensity value. In Figure (a) a mathematical line shown in dotted from is represented by a rectangular region that is one pixel wide. The percentage of overlap between the rectangle and each intersecting pixel is calculate analytically. Assuming that the background is black and the line is white, the percentage value can be used directly to set the intensity of the pixel [see figure (b)]. On the other hand, had the background been gray (0., 0.5, 0.5) and the line green (0, 1, 0) each blank pixel in the grid would have had the background gray value and each pixel filled with a fractional number f would have been assigned a value of [0.5 (1-f), 0.5 (1-f) +f, 0.5 (1-f)] a proportional blending of the background and object colors. Although the resultant discrete approximation of the line in Figure (c) takes on a blurry appearance, it no longer exhibits the sudden transition from an on pixel to an off pixel and vice versa, which is what we would get with an ordinary scan- conversion method. This trade- off is a characteristic of an anti- aliasing technique based on filtering. In this approach we subdivide each pixel into subpixels and check the position of sub pixel in relation to the object to be scan- converted. The object's contribution to a pixel's overall intensity value is proportional to the number of subpixel that are inside the area occupied by the object. Figure shows an example where we have a white object that is bounded by two slanted lines on a black background. We subdivide each pixel into nine (3*3) subpixels. The scene in mapped to the pixel values in Figure. The pixel at the upper right corner, for instance, is assigned 7.9 since seven of its nine subpixels are inside the object area. Had the object been red (1, 0, 0) and the background light yellow (0.5, 0.5, 0), the pixel would have been assigned Super sampling is often reagarded as a post- filtering technique since discrete samples are first taken and used to calculate pixel values. On the other hand, it can be viewed as an approximation to the area sampling method since we are simply using a finite number of values in each pixel area to approximation the accurate analytical result.
Lowpass filtering: This is a post- filtering technique in which we reassign each pixel a new value that is a weighted average of its original value and the original values of its neighbors. A lowpass filter in the form of a (2n + 1) * (2N + 1) grid, where n > 1, holds the weights for the computation. All weight values in a filter should sum to one. An example of a 3*3 filter is given in Figure (a).To compute a new value for a pixel, we align the filter with the pixel
Although increasing image resolution is a straightforward way to decreases the size of many aliasing artifacts their and alleviate their negative, we pay a heavy price in terms of system resource (going from W X H to 2W X 2H means quadrupling the number of pixels) and the results are not always satisfactory. On the other hand, there are technique that can greatly reduce aliasing artifacts and improve the appearance of images without increasing their resolution, These technique are collectively referred to as anti- aliasing technique are designed to treat a particular type of artifact. For instance, an outline font can be associated with a set of rules or hints to guide the adjustment and realignment that is necessary for its conversion into bitmaps of relatively low resolution, an example of such approach is called the True Type font.