Re: interpolation for a color image?

On May 26, 4:28 pm, da...@xxxxxxxxx (Dave Martindale) wrote:
Harris <xgeorg...@xxxxxxxxxxxxx> writes:
...

The problem I see with this is that you're effectively calculating the
higher-resolution image by recombining a luminance/intensity image
that was obtained by linear interpolation and colour information that
was upsampled by nearest-neighbour.  The latter is a terrible upsampling
method that doesn't actually interpolate anything.

But if the two pixels have the same intensity but different colour,
there will be an abrupt colour change half-way along a line between the
two original pixel locations.  So the colour component of the image will
have some nasty artifacts in it.

Now, you can argue that under certain viewing conditions, your eye's
colour resolution is bad enough that you can't see the colour artifacts,
so they don't matter.  True.  But you will get an image that looks
significantly worse up-close than interpolating in all 3 components
(whether that's in RGB or YCbCr).


In Harris's defense, it won't look "terrible" or "significantly worse"
but just bad enough to negate the advantage of the computation savings
which are much smaller than he imagines. The eye is relatively
insensitive to inaccuracies in the Cb and Cr channels and that is why
they are compressed more than Y in JPEG. I have used schemes similar
to Harris's for Xin Li (NEDI) enlargement. In NEDI, counterparts to
bilinear weights are calculated Wiener style which is very time
consuming. I calculate the weights based only on the Y channel and
apply them to all three channels, Y, Cb, Cr. It is good enough
considering the ~2/3 time savings. However, my default weights for
homogeneous Y are bilinear and not nearest neighbor as in Harris's
case.
.

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