NAME
    Image::Density::TIFF

SYNOPSIS
      use Image::Density::TIFF;
      print "Density: %f\n", tiff_density("foo.tif"); # single-page
      print "Densities: ", join(", ", tiff_densities("bar.tif")), "\n"; # multi-page

DESCRIPTION
    A trivial density calculation would count the number of black pixels and
    divide by the total number of pixels. However, it would produce
    misleading results in the case where the image contains one or more
    target areas with scanned content and large blank areas in between
    (imagine a photocopy of a driver's license in the middle of a page).

    The metric implemented here estimates the density of data where there
    *is* data, and has a reasonable correllation with goodness as judged by
    humans. That is, if you let a human look at a set of images and judge
    quality, the desity values for those images as calculated here tend to
    correllate well with the human judgement (densities that are too high or
    too low represent "bad" images).

    This algorithm is intended for use on bitonal TIFF images.

  The calculation
    We omit the margins because there is likely to be noise there, such as
    black strips due to page skew. This does admit the possibility that we
    are skipping over something important, but the margin skipping here
    worked well on the test images.

    Leading and trailing white on a row are omitted from counting, as are
    runs of white at least as long as the margin width. This helps out when
    we have images with large blank areas, but decent density within the
    areas filled in, which is what we really care about.

AUTHOR
    Gregor N. Purdy <gregor@focusresearch.com>

COPYRIGHT
    Copyright (C) 2003-2007 Gregor N. Purdy. All rights reserved.

LICENSE
    This program is free software. Its use is subject to the same license as
    Perl.