🚀 go-pugleaf

In article <yi.714929332@fife.ecn.purdue.edu> yi@fife.ecn.purdue.edu (Juneho Yi) writes:
>sjreeves@eng.auburn.edu (Stan Reeves) writes:

[ stuff on objective measures for restoration quality ]

>>I'm assuming that the original poster is interested in objective measures
>>that have some correlation to the assessment a human observer would make.
>>Does this measure have any relationship to subjective evaluation?  What is
>>the point of normalizing with respect to surface area?
>
>By normalizing the volume by the surface area, the value of the measure 
>can be compared for different size of images.

This is usually done simply by dividing by the number of pixels in the
image.  Is there any advantage of dividing by surface area?  By surface
area, are you thinking of an image as a 2-D contour map?  If you just
mean height*width, then that's the same as dividing by the number of
pixels.

Also, what about "volume"?  This sounds like mean absolute error to me.

>The Mean-Square error is kinda L2 metric.
>Apparently, if MSE is used as a measure, where the slope is high,
>the difference value is emphasized more than in flat regions.
>I am saying that MSE measure is ** not invariant ** w.r.t. a coordinate system.
>The original poster was looking for methods to compare various algorithms on
>image restoration, wasn`t it ?

When you speak of a coordinate system, are you thinking of an image as a
signal in two dimensions or are you thinking of each pixel as a dimension
so that we have a signal in an MxN-dimensional vector space for an MxN 
image?  If you mean the second, then MSE *is invariant* with respect to
a coordinate transformation.  It also seems pretty clear that it's invariant
in the sense of a 2-D coordinate transformation.  Can you clarify?



--
Stan Reeves
Auburn University, Department of Electrical Engineering, Auburn, AL  36849
INTERNET: sjreeves@eng.auburn.edu