On 08/17/2010 01:01 AM, Soeren Sandmann wrote:
Bill Spitzak<spit...@gmail.com> writes:
> I'm not sure what's
> the difference in quality between those approaches. I could try
> prototyping both of them.
I suspect there is going to be a phasing problem with your approach if
the scale is slightly different that 1/integer. At some places the
kernel samples line up with the pixel centers and at others it is
between them and thus blurring or lowering the contrast. This will
probably look like moire patterns.
This is why we need both interpolation and low-pass filtering. When
the kernel samples don't line up with the pixel centers, then we apply
the interpolation filter. For example the bilinear filter. For
example:
X X X
o o o o o o o o o o o
o o o o o o o o o o o
X o o o o Xo o o o o X o
o o o o o o o o o o o
Here, the o's represent transformed destination subpixels and the X's
represent source pixels. If the image has a
pixman_image_set_filter()
of PIXMAN_FILTER_BILINEAR, then for each of the o's a bilinear
interpolation will take place, and then all those interpolation
results will be averaged together (or combined in a higher-quality way
if we add other resampling filters than box).
This is exactly the problem I am describing. A sample that is 1/2 way
between pixels A and B will get the value (A+B)/2. A sample that is
centered on pixel B will get the value B. This will cause moire patterns
because the first sample is from a blurred image and the second is from
a sharp image and the output image will vary between these as the phase
of the samples passes over the image.
Imagine the image is a checkerboard of black and white pixels. The 1/2
way sample will be .5 while the centered sample will be 1 or 0.
The example image of the house shows this clearly by the aliased
frequency banding in the gray stripe at the bottom.
Another, faster, but lower-quality, type of interpolation is NEAREST,
in which case each o will just take the value of the closest
X. Depending on the sample rate, this could indeed produce more
aliasing artifacts.
Actually nearest could improve the moire patterns but it adds a lot of
noise.
Another way to kill the moire patterns is to make all samples a 50/50
average of two pixels, ie blur the entire image uniformly. This is
somewhat what mipmapping does.
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