NAME

       extendedopacity - theory of netpbm interpolation and extrapolation

DESCRIPTION

       This  page  is  a  copy of http://www.sgi.com/misc/grafica/interp/ on April 17, 2003, with
       some slight formatting changes, included in  the  Netpbm  documentation  for  convenience.
       Since at least June 11, 2005, the source page has been missing.

Image Processing By Interpolation and Extrapolation

       Paul Haeberli and Douglas Voorhies

   Introduction
       Interpolation  and extrapolation between two images offers a general, unifying approach to
       many common point and area image processing operations.  Brightness, contrast, saturation,
       tint,  and sharpness can all be controlled with one formula, separately or simultaneously.
       In several cases, there are also performance benefits.

       Linear interpolation is often used to blend two images.  Blend fractions (alpha) and (1  -
       alpha) are used in a weighted average of each component of each pixel:

             out = (1 - alpha)*in0 + alpha*in1

       Typically  alpha  is  a number in the range 0.0 to 1.0.  This is commonly used to linearly
       interpolate two images.  What is less often considered is that alpha may range beyond  the
       interval  0.0  to  1.0.   Values  above  one  subtract a portion of in0 while scaling in1.
       Values below 0.0 have the opposite effect.

       Extrapolation is particularly useful if a degenerate version of the image is used  as  the
       image  to  get  "away  from."   Extrapolating  away from a black-and-white image increases
       saturation.   Extrapolating  away  from  a  blurred  image   increases   sharpness.    The
       interpolation/extrapolation  formula  offers  one-parameter  control,  making display of a
       series of images, each differing in brightness, contrast, sharpness, color, or saturation,
       particularly easy to compute, and inviting hardware acceleration.

       In  the  following  examples,  a  single  alpha  value  is  used per image.  However other
       processing is possible, for example where alpha is a function of X and Y, or where a brush
       footprint controls alpha near the cursor.

   Changing Brightness
       To  control  image  brightness,  we  use  pure black as the degenerate (zero alpha) image.
       Interpolation darkens the image, and extrapolation brightens it.  In both cases,  brighter
       pixels are affected more.

       brightness

   Changing Contrast
       Contrast  can  be controlled using a constant gray image with the average image luminance.
       Interpolation reduces contrast and extrapolation  boosts  it.   Negative  alpha  generates
       inverted  images  with  varying  contrast.   In  all cases, the average image luminance is
       constant.

       contrast

       If middle gray or the average pixel color is used instead, contrast is again altered,  but
       with  middle  gray  or the average color left unaffected.  Shades and colors far away from
       the chosen value are most affected.

   Changing Saturation
       To alter saturation, pixel components must move towards or away from the pixel's luminance
       value.  By  using  a  black-and-white  image  as the degenerate version, saturation can be
       decreased  using  interpolation,  and  increased   using   extrapolation.    This   avoids
       computationally  more  expensive conversions to and from HSV space.  Repeated update in an
       interactive application is especially fast, since the luminance of each pixel need not  be
       recomputed.  Negative alpha preserves luminance but inverts the hue of the input image.

       saturation

   Sharpening an Image
       Any  convolution,  such as sharpening or blurring, can be adjusted by this approach.  If a
       blurred image is used as the degenerate image, interpolation attenuates  high  frequencies
       to  varying  degrees,  and  extrapolation  boosts  them,  sharpening  the image by unsharp
       masking.  Varying alpha acts as a  kernel  scale  factor,  so  a  series  of  convolutions
       differing  only in scale can be done easily, independent of the size of the kernel.  Since
       blurring, unlike sharpening, is often a separable operation, sharpening  by  extrapolation
       may be far more efficient for large kernels.

       sharpening

       Note  that  global  contrast  control,  local  contrast  control,  and  sharpening  form a
       continuum.  Global contrast pushes pixel components towards or away from the average image
       luminance.   Local contrast is similar, but uses local area luminance.  Unsharp masking is
       the extreme case, using only the color of nearby pixels.

   Combined Processing
       An unusual property of this interpolation/extrapolation approach  is  that  all  of  these
       image  parameters may be altered simultaneously.  Here sharpness, tint, and saturation are
       all altered.

       combined

   Conclusion
       Image  applications  frequently  need  to  produce  multiple   degrees   of   manipulation
       interactively.  Image applications frequently need to interactively manipulate an image by
       continuously changing a single parameter.  The best hardware mechanisms  employ  a  single
       "inner  loop"  to  achieve  a wide variety of effects.  Interpolation and extrapolation of
       images can be a unifying approach, providing a single function that  can  do  many  common
       image processing operations.

       Since  a degenerate image is sometimes easier to calculate, extrapolation may offer a more
       efficient method to achieve effects such as  sharpening  or  saturation.   Blending  is  a
       linear  operation,  and  so  it  must  be  performed  in  linear,  not gamma-warped space.
       Component range must also be monitored,  since  clamping,  especially  of  the  degenerate
       image, causes inaccuracy.

       These  image  manipulation  techniques  can  be used in paint programs to easily implement
       brushes that saturate, sharpen, lighten, darken, or modify contrast and color.   The  only
       major change needed is to work with alpha values outside the range 0.0 to 1.0.

       It  is  surprising  and  unfortunate  how many graphics software packages needlessly limit
       interpolant values to the range 0.0 to 1.0.  Application developers should allow users  to
       extrapolate parameters when practical.

   References
       For  a  slightly extended version of this article, see: P. Haeberli and D. Voorhies. Image
       Processing by Linear Interpolation and Extrapolation.   IRIS  Universe  Magazine  No.  28,
       Silicon Graphics, Aug, 1994.

DOCUMENT SOURCE

       This  manual page was generated by the Netpbm tool 'makeman' from HTML source.  The master
       documentation is at

              http://netpbm.sourceforge.net/doc/extendedopacity.html