LightWave 2024 Render Improvements

One of the most commonly used filters in graphics is the box filter (and, in fact, when filtering and reconstruction aren't addressed explicitly, the box filter is the de facto result). The box filter equally weights all samples within a square region of the image. Although computationally efficient, it's just about the worst filter possible.

The triangle filter gives slightly better results than the box: the weight falls off linearly from the filter center over the square extent of the filter.

Unlike the box and triangle filters, the Gaussian filter gives a reasonably good result in practice. This filter applies a Gaussian bump that is centered at the pixel and radially symmetric around it. The Gaussian's value at the end of its extent is subtracted from the filter value, in order to make the filter go to 0 at its limit. The Gaussian does tend to cause slight blurring of the final image compared to some of the other filters, but this blurring can actually help mask any remaining aliasing in the image.

Filter design is notoriously difficult, mixing mathematical analysis and perceptual experiments. Mitchell and Netravali (1988) have developed a family of parameterized filter functions in order to be able to explore this space in a systematic manner. After analyzing test subjects' subjective responses to images filtered with a variety of parameter values, they developed a filter that tends to do a good job of trading off between ringing (phantom edges next to actual edges in the image) and blurring (excessively blurred results)—two common artifacts from poor reconstruction filters.

Finally, the Lanczos Sinc is a filter based on the sinc function. In practice, the sinc filter is often multiplied by another function that goes to 0 after some distance. This gives a filter function with finite extent, which is necessary for an implementation with reasonable performance.