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COLOR MANAGEMENT: COLOR SPACE CONVERSION

2009年06月20日, 星期六 留下评论 Go to comments
I got this post bravia article from : http://www.cambridgeincolour.com/tutorials/color-space-conversion.htm

Color space conversion is what happens when the color management module (CMM)
translates color from one device’s space to another. 
Conversion may require approximations in order to preserve the
image’s most important color qualities.  Knowing how these
approximations work can help you control how the photo may change– hopefully maintaining the
intended look or mood.

LCD Printer
Input Device   Profile Connection Space   Output Device
Additive RGB Colors
RGB Profile
(RGB Space)
Subtractive CMYK Colors
CMYK Profile
(CMYK Space)

http://www.cambridgeincolour.com/fonts/MathSoftText.swfBACKGROUND: GAMUT MISMATCH & RENDERING INTENT

The translation stage attempts to create a best match between
devices– even when seemingly incompatible.  If the original device has
a larger color gamut than the final device, some of the those colors will be outside the
final device’s color space. 
These "out-of-gamut colors" occur with nearly every conversion
and are called a gamut mismatch.

RGB Color Space
CMYK Color Space
(Destination Space)

Each time a gamut mismatch occurs, the CMM uses the rendering
intent
to decide what qualities of the image it should
prioritize.  Common rendering intents include: absolute and relative
colorimetric, perceptual, and saturation.  Each of
these types maintains one property of color at the
expense of others (described below).

http://www.cambridgeincolour.com/fonts/MathSoftText.swfPERCEPTUAL & RELATIVE COLORIMETRIC INTENT

Perceptual and relative colorimetric rendering are probably
the most useful conversion types for digital photography.  Each
places a different priority on how they render colors within the
gamut mismatch region.  Relative colorimetric maintains a near
exact relationship between in gamut colors, even if this clips out
of gamut colors.  In contrast, perceptual rendering tries to
also preserve some relationship between out of gamut colors, even if
this results in inaccuracies for in gamut colors.  The
following example demonstrates an extreme case for an image within a
1-D black-magenta color space:

Original Image:
A = Wide Gamut Space
B = Narrow Gamut Space
       (Destination Space)
Relative Colorimetric Perceptual
A A
B B
Converted Image: Converted Image:

Note
how perceptual maintains smooth color gradations throughout by
compressing the entire tonal range, whereas relative colorimetric clips
out of gamut colors (at center of magenta globules and in the darkness
between them).  For 2D and 3D color spaces, relative colorimetric maps
these to the closest reproducible hue in the destination space.

Even though perceptual rendering compresses the entire gamut,
note how it remaps the central tones more precisely than those at
the edges of the gamut.  The exact conversion depends on what CMM is used for the conversion; Adobe ACE,
Microsoft ICM and Apple ColorSynch are some of the most common.

Another distinction is that perceptual does not destroy any color
information– it just redistributes it.  Relative colorimetric,
on the other hand, does destroy color information.  This means
that conversion using relative colorimetric intent is
irreversible, while perceptual can be reversed
.  This is
not to say that converting from space A to B and then back to A
again using perceptual will reproduce the original; this would
require careful use of tone curves to reverse the color compression
caused by the conversion.

http://www.cambridgeincolour.com/fonts/MathSoftText.swfABSOLUTE COLORIMETRIC INTENT

Absolute is similar
to relative colorimetric in that it preserves in gamut colors
and clips those out of gamut, but they differ in how each handles
the white point.  The white point is the location of the purest
and lightest white in a color space (also see discussion of
color temperature).  If one were to draw a
line between the white and black points, this would pass through the
most neutral colors.

 
3D Color Space   2D Cross-Section
(Two Spaces at 50% Luminance)

The location of this line often changes between color spaces, as
shown by the "+" on the top right.  Relative colorimetric skews
the colors within gamut so
that the white point of one space aligns with that of the
other, while absolute colorimetric preserves colors exactly
(without regard to changing white point).  To illustrate this, the example below shows two
theoretical spaces that have identical gamuts, but different white
points:

= White Point      
Color Space #1 Color Space #2   Absolute
Colorimetric
Relative
Colorimetric

Absolute colorimetric preserves the white point, while
relative colorimetric actually displaces the colors so that the old
white point aligns with the new one (while still retaining the
colors’ relative positions).  The exact preservation of colors may sound appealing, however
relative colorimetric adjusts the white point for a reason. 
Without this adjustment, absolute colorimetric results in
unsightly image color shifts, and is thus rarely of interest to
photographers

This color shift results because the white point of the color space usually needs to align with that of the light source
or paper tint used.  If one were printing to a color space for
paper with a bluish tint, absolute colorimetric would ignore this
tint change.  Relative colorimetric would compensate colors to
account for the fact that the whitest and lightest point has a tint
of blue.

http://www.cambridgeincolour.com/fonts/MathSoftText.swfSATURATION INTENT

Saturation rendering intent tries to preserve saturated colors,
and is most useful when trying to retain color purity in computer
graphics when converting into a larger color space.  If the original RGB device contained pure (fully
saturated) colors, then saturation intent ensures that those colors
will remain saturated in the new color space– even if this causes
the colors to become relatively more extreme.

Pie chart with fully saturated
cyan,
blue,
magenta and
red:

Saturation intent is not desirable for photos because it
does not attempt to maintain color realism.
  Maintaining color
saturation may come at the expense of changes in hue and lightness,
which is usually an unacceptable trade-off for photo
reproduction.  On the other hand, this is often acceptable for
computer graphics such as pie charts.

Another use for saturation intent is to avoid visible dithering
when printing computer graphics on inkjet printers.  Some
dithering may be unavoidable as inkjet printers never have an ink to
match every color, however saturation intent can minimize those
cases where dithering is sparse because the color is very close to
being pure.

Visible dithering due to lack of fully saturated colors:

http://www.cambridgeincolour.com/fonts/MathSoftText.swfPAY ATTENTION TO IMAGE CONTENT

One must take the range of image colors present into account;
just because an image is defined by a large color space does not
mean that it actually utilizes all of those extreme colors.  If
the destination color space fully encompasses the image’s colors
(despite being smaller than the original space), then relative
colorimetric will yield a more accurate result.

Example Image:

The above image barely utilizes the gamut of your computer
display device, which is actually typical of many photographic
images.  If one were to convert the above image into a
destination space which had less saturated reds and greens, this
would not place any image colors outside the destination space.  For such
cases, relative colorimetric would yield more accurate
results.  This is because perceptual intent compresses the
entire color gamut– regardless of whether these colors are actually
utilized.

http://www.cambridgeincolour.com/fonts/MathSoftText.swfSHADOW & HIGHLIGHT DETAIL IN 3D COLOR SPACES

Real-world photographs utilize three-dimensional color spaces,
even though up until now we have been primarily analyzing spaces in
one and two dimensions.  The most important consequence of
rendering intent on 3D color spaces is how it affects shadow and
highlight detail.

If the destination space can no longer reproduce subtle dark
tones and highlights, this detail may be clipped when using
relative/absolute colorimetric intent.  Perceptual intent
compresses these dark and light tones to fit within the new space,
however it does this at the cost of reducing overall contrast
(relative to what would have been produced with colorimetric
intent).

The conversion difference between perceptual and relative
colorimetric is similar to what was demonstrated earlier
with the magenta image.  The main difference is that
now the compression or clipping occurs in the vertical
dimension– for shadows and highlight colors.  Most
prints cannot produce the range of light to dark that we may
see on our computer display, so this aspect is of particular
importance when making a print of a digital photograph.

Using the "black point compensation" setting can help avoid
shadow clipping– even with absolute and relative colorimetric
intents.  This is available in the conversion properties of
nearly all software which supports color management (such as Adobe
Photoshop).

http://www.cambridgeincolour.com/fonts/MathSoftText.swfRECOMMENDATIONS

So which is the best rendering intent for digital photography? 
In general, perceptual and relative colorimetric are best suited for
photography because they aim to preserve the same visual appearance
as the original. 

The decision about when to use each of these depends on image
content and the intended purpose.  Images with intense colors
(such as bright sunsets or well-lit floral arrangements) will
preserve more of their color gradation in extreme colors using
perceptual intent.  On the other hand, this may come at the
expense of compressing or dulling more moderate colors. 
Images with more subtle tones (such as some portraits) often stand
to benefit more from the increased accuracy of relative colorimetric
(assuming no colors are placed within the gamut mismatch region). 
Perceptual intent is overall the safest bet for general and batch
use, unless you know specifics about each image.

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