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Sorting and image rescaling

I work on some of the software for an imaging sensor. One of the codes I
wrote was to generate JPEGS. In order to generate prettier JPEGS I wrote
my own bytescaling routine that crudely takes into account the
distribution of values within a band image. This routine relies on a
single line equivalent to

     sorted_values = SORT(band_image)

where SORT is the IDL intrinsic, and band image is a floating point two
dimensional array.  I basically use this line to find a set percentage
of minum and maximum outliers , and use the maximum and minimum of the
remaining inliers to do a linear rescaling to values from 0 to 255.

Some of our band images are on the order of 2500 by 10000. For such band
images this line can take over 30 seconds per band. This is a moderate
nuisance at the moment, but we are planning to update our calibration ,
and reprocess 1000s of multiband images with a new calibration.
Naturally we want to update the jpegs to reflect this new calibration.
It appears that this single line will extend reprocessing by a couple of
days. I don't like this. This yields the following questions:

1. Does anyone know a better general approach to such a rescaling that
avoids the need to sort the data, or sort more than a fraction of the

2. How does ENVI do its linear, gaussian, and uniform rescalings? They
seem to take about a second for these images, so they must be doing
something different from what I am doing.

3. Does IDL have a particularly inefficient SORT method for floats? Note
that for floats it is possible to sort in 0(N), using something like a
bucketsort, but more flexible sorting routines such as merge sort, heap
sort, and quick sort are of order 0(N ln(N)).