Today Roger N. Clark (change username to rnclark) spoke these
views with conviction for everyone's edification:

All Things Mopar wrote:
Today Roger N. Clark (change username to rnclark) spoke
these views with conviction for everyone's edification:


Enlarging and shrinking an image requires interpolation.
ALL interpolation algorithms have artifacts, at least all
I've seen. If you compare your original small image with
the largest one, you can see artifacts. I'll point out
one.


Resampling using any algorithm, interpolation or simple
pixel resize, can and does produce damage. But the term
"artifact" first came to prominence during the early
experiences with JPEG, after people first saw the damage
from over compression.

Artifacts in digital imaging is much older than jpeg. I
learned of it in the 1970s in graduate school. Yes digital
imaging was being done back then, pre CCDs. I was using a
vidicon, 256x256 if I remember correctly, system that
digitized with electronics filling 6 feet of rack space,
and a fifty pound camera head, cooled to dry ice
temperatures.

Being that I'm a visual sort of a guy, I don't know how to
use the analytical tools in PSP 9 to examine an image, and
I don't parlez vous PS CS. So, I'm very curious to know
how you can tell that your examples above were caused by
interpolation and not the more likely cause, over
compression at some point, and/or multiple
open/edit/re-save cycles.

Without knowing the exact techniques and algorithms used in
the processing, one can't be sure of where things were
introduced versus where they were exaggerated. In my
experience with scientific interpolation, including writing
imaaging interpolation algorithms, it is my experience that
there is no perfect interpolation algorithm for this type
of problem. What is the likely cause is that the
interpolation in the upsizing is imperfect (causing the
fundamental artifact) which the sharpening and other
processing steps enhanced. Some algorithms invent data
(e.g.fractals) which may look good in many cases, but is
scientifically incorrect, thus all the added "information"
is artifacts. Other algorithms try and do little
"inventing" linear, cubic spline and others, but these
cause other artifacts. One hopes the artifacts have
minimal detriment to the final image appearance.

Not exactly taking you on, but the above seems like spin to
me. A minute ago you were lecturing me on why artifacts are
created simply by interpolation, rather than by
overenthusastic JPEG compression. But you still haven't shown
anything quantitative to refute my definition of the term.
Could you perhaps post some examples that either support or
refute the various opinions expressed herein?

As to "looking good but being scientifically incorrect", this
is /exactly/ my point. Since normal people only look at and
print images, not examine them mathematically except while
post-processing their scans or digitals, the point you're
making is not only moot, but irrelevant.

I got into this thread only to point out that there are
methods, which you say you've personally implemented in
software, that can easily defy the "rules" of either upward or
downward resizing. I still like the bigger images but haven't
been able to track onto the techology used to create noise-
free versions, but I still sleep fine at night with only a
postage stamp picture of my father.

--
ATM, aka Jerry