--- Austin Franklin <darkroom@ix.netcom.com> wrote:
> You can repeat it all you like, but what you say is not entirely
> accurate.
> The "data" is two dimensional. Each pixel has position (an XY
> coordinate)
> as one dimension and color information as the other.
I don't follow you. I didn't talk about dimensions but about number of
channels per pixel. And while it does not address my point at all there
are more then the two 'dimensions' you mentioned, i.e. time, etc.
> Interpolation requires the addition of new data points, like when a
> scanner
> that has an optical resolution of 1200 DPI gives you 2400 DPI.
Interpolation does not require new data points, it can produces them.
Also it does not necessary mean that there will be more pixels.
Interpolation can simply add missing color information. When you look
at the R, G, and B channel each on a 6MPixel grid many data points in
each channel will be missing. So it does not generate new pixels but
new data points within the channels to produce a true color pixel.
> That
> is
> interpolation of positional data. Interpolation means to "insert"
> between
> other elements.
Exactly, you insert the blue, green, and red data points where they are
missing on the 6Mpixel grid.
> Though the data points are not interpolated, the color value of each
> point
> MAY be arrived at by interpolation, if the algorithm uses
> interpolation. It
> is not necessary to use interpolation to arrive at the color
> information for
> each pixel. You could take the four color values, combine them and
> apply
> them to each of the four pixels...that isn't interpolating.
Hah, that approach produces so terrible image quality, especially along
edges, that I don't even consider it for anything where image quality
is of any importance. Or would you use a nearest neighbor approach to
size up your image? People don't even want linear interpolation but
bi-cubic interpolation, etc.
Robert
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