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Technical Overview of ClearType Filtering
Abstract
ClearType
is a method for improving the quality of fonts on displays that contain
repeating patterns of colored sub-pixels. ClearType has several component
technologies. This page describes the filtering used by ClearType during the
conversion from an ideal font glyph to intensities of sub-pixels. The filtering
is optimal under a human visual system model. Approximations to the optimal
filtering yields a broad set of filters for patterned displays. This page
contains links to technical papers and a brief overview of ClearType. Papers and
other information about ClearType Filtering
Overview
LCDs do not really have full-color pixels. The pixels in an LCD are arrayed in vertical stripes with red sub-pixels displaced 1/3 of a pixel to the left of green and blue sub-pixels 1/3 of a pixel to the right. Traditional display algorithms ignored such physical properties of the display device and forfeited the resolution enhancement provided by ClearType. How does
ClearType filtering work? Unlike ad-hoc techniques, ClearType approaches the
problem mathematically. Here is the problem statement: you want to display an
image that contains any possible color at an infinite resolution. However, all
you can do is display an intensity of a single color for every sub-pixel. So,
let's choose the sub-pixel intensities so that the image that is produced by
the LCD is "close" to the ideal image with all colors at infinite
resolution. How do you define "close"? ClearType uses a model of the
human visual system to define what "close" means. The human visual
system can detect errors in image, but it is more sensitive to errors that have
a spatially large extent, rather than in the fine detail. Also, the human
visual system is more sensitive to detail errors in black/white brightness,
rather than in color. The
process of finding the best LCD sub-pixel values takes a red/green/blue input
image (at some resolution) and produces a red/green/blue output image (at the
sub-pixel output resolution). Given the human visual system model, the best
value for an LCD sub-pixel can be shown mathematically to depend linearly on
all of the input pixel colors in a neighborhood of the LCD sub-pixel. In other
words, all of the input colors influence all of the output colors. The
input-to-output mapping thus can be expressed as a family of nine linear
filters derived from an optimization problem: one for each combination of input
color and output color. The coefficients of these nine filters depend on the
human visual model and on the pattern of sub-pixel colors. Note that
conventional anti-aliasing ignores the display color pattern and applies the
same linear filter for each color component. ClearType, on the other hand, can
be thought of as including a display specific anti-aliasing technique. One
approximation to the optimal ClearType filters is to have the output sub-pixel
only depend on pixels of the same color in the input image. This approximation
consists of only three out of the original nine filters: a red filter, a green
filter, and a blue filter. For example, the red sub-pixels value is the result
of applying the red filter to the red channel of the input image. Now, if
you examine these three filters, you find something interesting: the filters
are similar to each other, but the red filter is displaced 1/3 of a pixel to
the left of the green filter, and the blue filter is displaced 1/3 of a pixel
to the right of the green filter. Thus, the ClearType filters can be viewed as
spatially displaced filters. These displaced filters take into account
the spatial arrangement of the sub-pixels, unlike conventional anti-aliasing.
These displaced filters yield a significant increase in apparent resolution In
summary, ClearType uses information about a display to produced crisper fonts.
ClearType is very general: it applies to any pattern of LCD pixels, not just
repeating RGB. It applies to any color font and any color background and any
width object (indeed, it can apply to an arbitrary image!). ClearType also can
accept fonts that are rasterized at a higher resolution than the sub-pixel
resolution (which yields fewer "jaggies"). This
page was written by John Platt of the CCSP Group of Microsoft Research. Last
updated: |
