Histogram Question

Histogram Question

Post by Uni » Wed, 17 Apr 2002 12:25:19



Sometimes when I adjust the Low and/or High values of the Histogram, I
end up with a graph with steep, but very narrow, dropouts in it. Does
this mean there is something wrong with the image? How do you go about
setting the High and Low values of the Histogram and avoid this?

Thanks!

Uni

 
 
 

Histogram Question

Post by Ilya Razmano » Wed, 17 Apr 2002 21:32:49



> Sometimes when I adjust the Low and/or High values of the Histogram, I
> end up with a graph with steep, but very narrow, dropouts in it. Does
> this mean there is something wrong with the image? How do you go about
> setting the High and Low values of the Histogram and avoid this?

> Thanks!

> Uni

Guess you're trying to "expand" your image histogram - whole range (if you
use "levels") or, if you use "gamma", a part of it. Then some "gaps" are
unavoidable. Let's say you have a source with a range of 0..128 and try to
expand it to 0..255. The 0->0, 1->2, etc. There's no "1" value in the
resulting picture because there's no "0.5" value in the source.

The more you expand the range of your image - whole range or some particular
area of it - the more noticeable gaps you get.

I'm not sure if it can be fixed somehow, in theory. At least in practice, I
definitely never seen a solution.

Ilyich.
--------------------------------------------------------------------------
Ilya Razmanov (a.k.a. Ilyich the Toad)
http://photoshop.msk.ru/ - Photoshop plug-in filters
"No bucks - no yucks, Compadre" - Horace, the official Courley Motors
Smash-A-Torium merchandize salesman, Full Throttle
--------------------------------------------------------------------------

 
 
 

Histogram Question

Post by Uni » Wed, 17 Apr 2002 21:55:36




> > Sometimes when I adjust the Low and/or High values of the Histogram, I
> > end up with a graph with steep, but very narrow, dropouts in it. Does
> > this mean there is something wrong with the image? How do you go about
> > setting the High and Low values of the Histogram and avoid this?

> > Thanks!

> > Uni

> Guess you're trying to "expand" your image histogram - whole range (if you
> use "levels") or, if you use "gamma", a part of it. Then some "gaps" are
> unavoidable. Let's say you have a source with a range of 0..128 and try to
> expand it to 0..255. The 0->0, 1->2, etc. There's no "1" value in the
> resulting picture because there's no "0.5" value in the source.

> The more you expand the range of your image - whole range or some particular
> area of it - the more noticeable gaps you get.

> I'm not sure if it can be fixed somehow, in theory. At least in practice, I
> definitely never seen a solution.

Wow! Thanks, Ilya! :)

Anyway, I just edited another image and this same notching occurred in
the Histogram graph. I let it go and continued on with the image
tweaking. I looked back later at the Histogram and the notches had
disappeared.

I took a different approach to expand the graph by using the
Highlight/Midtone/Shadow filter. This expanded the graph w/o notching.
Whether this is appropriate, I'm not sure.

I have to rely heavily on graphs, because my monitor has such poor gamma
linearity. Shadow detail? Yeah, right, it looks all black to me! :-)

Thanks.

Uni

- Show quoted text -

Quote:

> Ilyich.
> --------------------------------------------------------------------------
> Ilya Razmanov (a.k.a. Ilyich the Toad)
> http://photoshop.msk.ru/ - Photoshop plug-in filters
> "No bucks - no yucks, Compadre" - Horace, the official Courley Motors
> Smash-A-Torium merchandize salesman, Full Throttle
> --------------------------------------------------------------------------

 
 
 

Histogram Question

Post by Ilya Razmano » Wed, 17 Apr 2002 22:19:18







Quote:> > > Sometimes when I adjust the Low and/or High values of the Histogram, I
> > > end up with a graph with steep, but very narrow, dropouts in it. Does
> > > this mean there is something wrong with the image? How do you go about
> > > setting the High and Low values of the Histogram and avoid this?

> > > Thanks!

> > > Uni

> > Guess you're trying to "expand" your image histogram - whole range (if
you
> > use "levels") or, if you use "gamma", a part of it. Then some "gaps" are
> > unavoidable. Let's say you have a source with a range of 0..128 and try
to
> > expand it to 0..255. The 0->0, 1->2, etc. There's no "1" value in the
> > resulting picture because there's no "0.5" value in the source.

> > The more you expand the range of your image - whole range or some
particular
> > area of it - the more noticeable gaps you get.

> > I'm not sure if it can be fixed somehow, in theory. At least in
practice, I
> > definitely never seen a solution.

> Wow! Thanks, Ilya! :)

> Anyway, I just edited another image and this same notching occurred in
> the Histogram graph. I let it go and continued on with the image
> tweaking. I looked back later at the Histogram and the notches had
> disappeared.

TIP: blur your image heavily enough and the histogram gaps will completely
disappear! ;-)))

Seriously, further editing may easily fill the gaps with some "interpolated"
or "somehow else calculated" colours.

Quote:> I took a different approach to expand the graph by using the
> Highlight/Midtone/Shadow filter. This expanded the graph w/o notching.
> Whether this is appropriate, I'm not sure.

Me neither. They may be operating in defferent colorspaces and so in the
space one of them produce gaps, the other is not (and vs.). Or maybe
something else. I was just explaining some very basic thing: if you have a
range of, say, 0..128 filled (fully, without a gaps) with integers, you
can't "rescale" it to 0..255, fully filled with integers without any gaps
between them. Some tricks like changing colorspace can "masquerade" it, in
histogram, but not definitely produce "visually better" results. The
non-linear effect (not just "High" and "Low" but also middle) may also
somewhat fill the gaps but, ultimately, you can't get everything out of
nothing - if your image has, say, only 30 steps of signal, you can't make
them fill 0..255 range without a gaps. The only way to do so would be apply
some sort of "filter", i.e., the thing that takes several pixels into
account, not just current one.

However, the fact some "banding" exists does not necessary mean it is
"visible".

Quote:> I have to rely heavily on graphs, because my monitor has such poor gamma
> linearity. Shadow detail? Yeah, right, it looks all black to me! :-)

Hmmm, aren't you using an LCD, by chance?

Ilyich.
--------------------------------------------------------------------------
Ilya Razmanov (a.k.a. Ilyich the Toad)
http://photoshop.msk.ru/ - Photoshop plug-in filters
"Stop him, Sam, he's gonna tell us a story!" - Max,
Sam and Max hit the road
--------------------------------------------------------------------------

 
 
 

Histogram Question

Post by Uni » Wed, 17 Apr 2002 22:37:44







> > > > Sometimes when I adjust the Low and/or High values of the Histogram, I
> > > > end up with a graph with steep, but very narrow, dropouts in it. Does
> > > > this mean there is something wrong with the image? How do you go about
> > > > setting the High and Low values of the Histogram and avoid this?

> > > > Thanks!

> > > > Uni

> > > Guess you're trying to "expand" your image histogram - whole range (if
> you
> > > use "levels") or, if you use "gamma", a part of it. Then some "gaps" are
> > > unavoidable. Let's say you have a source with a range of 0..128 and try
> to
> > > expand it to 0..255. The 0->0, 1->2, etc. There's no "1" value in the
> > > resulting picture because there's no "0.5" value in the source.

> > > The more you expand the range of your image - whole range or some
> particular
> > > area of it - the more noticeable gaps you get.

> > > I'm not sure if it can be fixed somehow, in theory. At least in
> practice, I
> > > definitely never seen a solution.

> > Wow! Thanks, Ilya! :)

> > Anyway, I just edited another image and this same notching occurred in
> > the Histogram graph. I let it go and continued on with the image
> > tweaking. I looked back later at the Histogram and the notches had
> > disappeared.

> TIP: blur your image heavily enough and the histogram gaps will completely
> disappear! ;-)))

You son of a gun, you! Yes, I did do some softening, so that's probably
what got rid of the notches.

- Show quoted text -

Quote:

> Seriously, further editing may easily fill the gaps with some "interpolated"
> or "somehow else calculated" colours.

> > I took a different approach to expand the graph by using the
> > Highlight/Midtone/Shadow filter. This expanded the graph w/o notching.
> > Whether this is appropriate, I'm not sure.

> Me neither. They may be operating in defferent colorspaces and so in the
> space one of them produce gaps, the other is not (and vs.). Or maybe
> something else. I was just explaining some very basic thing: if you have a
> range of, say, 0..128 filled (fully, without a gaps) with integers, you
> can't "rescale" it to 0..255, fully filled with integers without any gaps
> between them. Some tricks like changing colorspace can "masquerade" it, in
> histogram, but not definitely produce "visually better" results. The
> non-linear effect (not just "High" and "Low" but also middle) may also
> somewhat fill the gaps but, ultimately, you can't get everything out of
> nothing - if your image has, say, only 30 steps of signal, you can't make
> them fill 0..255 range without a gaps. The only way to do so would be apply
> some sort of "filter", i.e., the thing that takes several pixels into
> account, not just current one.

I understand now :)

Quote:

> However, the fact some "banding" exists does not necessary mean it is
> "visible".

That's what bothered me, if it could be visible. Actually, I think I'm
worrying over nothing :)

Quote:

> > I have to rely heavily on graphs, because my monitor has such poor gamma
> > linearity. Shadow detail? Yeah, right, it looks all black to me! :-)

> Hmmm, aren't you using an LCD, by chance?

No, one of these radioactive CRT's! :-)

Thanks, again.

Uni

- Show quoted text -

Quote:

> Ilyich.
> --------------------------------------------------------------------------
> Ilya Razmanov (a.k.a. Ilyich the Toad)
> http://photoshop.msk.ru/ - Photoshop plug-in filters
> "Stop him, Sam, he's gonna tell us a story!" - Max,
> Sam and Max hit the road
> --------------------------------------------------------------------------

 
 
 

Histogram Question

Post by Rick Simo » Wed, 17 Apr 2002 23:28:39



Quote:> Sometimes when I adjust the Low and/or High values of the
> Histogram, I end up with a graph with steep, but very narrow,
> dropouts in it. Does this mean there is something wrong with the
> image? How do you go about setting the High and Low values of the
> Histogram and avoid this?

 There's nothing inherently wrong with that, John. When working with
the Histogram, it's helpful to understand that the graph you are
looking at does not represent the image itself. Instead, it
represents the distribution of colors within the image.

 For instance, as Kris pointed out in a recent posting, lets say you
were to write a small program that simply rearranged all the pixels
in an image in a random manner. Not changed the color value of any of
them mind you, just moved them around within the image in a random
manner. You would end up with an image that looked like random noise.
If you were to look at the Histogram for that "randomized" image and
compare it to the Histogram for the original image though, you would
see that the two Histograms were exactly the same! A Histogram does
not deal with shapes or areas. It simply divides the pixels within
the image up into brightness levels from 0 to 255 and graphs them
out. It doesn't care where the pixels are within the image, it only
cares how bright each one is.

 As an example to illustrate what you are seeing with a Histogram
adjustment, let's say you are working on a grayscaled image (easiest
to explain) and using a Histogram to adjust Luminosity. Let's say
that this image has a large flat area with zero data that extends
inwards from the left edge of the Histogram for about 1/2". What does
that tell you? To me, it says that there are very few (or no) pixels
in the image that are pure black (0,0,0 - corresponds to the leftmost
edge of the Histogram). Furthermore, the further I go from the left
edge before I encounter data (or a rise in the graph), the more
shades of dark gray (1,1,1 or 2,2,2 or 3,3,3...) there are that are
not being used. By the time I've gone roughly 1/2" into the
Histogram, I'm up to 45,45,45. If the line in the Histogram is still
flat, that means that the image has no pixels in it that are
utilizing any of those dark gray colors, from pure black 0,0,0 to
dark gray 45,45,45.

 Let's say that our image also has a correspondingly large flat area
on the right side as well. That tells me that the image is also not
using pure white (255,255,255) or a number of shades of very light
gray. For the sake of simplicity, we'll say that the right side is
pretty much like the left side and that we can come inwards around 45
shades of gray before we find any rise in the Histogram. That means
that the image is also not using white or any shade of light gray
above 210,210,210.

 Overall then, we have an image that is only using 166 shades of gray
out of the total 256 shades that are available to us. As with any
process that reduces the numbers of colors available, this tends to
reduce the quality of the image. To make matters worse, this "color
reduction" did not make use of any sort of optimized algorithm to
reduce the colors while maintaining image quality. It simply chopped
off the upper and lower ends.

 Here's where the Histogram comes into play. By setting the lower and
upper "clip limits" to 45 and 210 respectively, we are telling the
Histogram tool that we want any pixels currently at 45,45,45 to
become 0,0,0 (pure black) and any pixels at 210,210,210 to become
pure white (255,255,255) when the Histogram Adjustment is applied.
All other pixels throughout the entire image will also have their
shade adjusted, correspondingly. What we are doing is, we are taking
all of the current color values within the image and "stretching them
out" so that they cover the entire gamut of available colors, from
pure black to pure white. In our example image, that equates to
taking 166 shades of gray, and spreading them across the 256 possible
shades.

 So how does the Histogram do that? It simply intersperses "empty"
shades in between "full" shades in order to spread things out. For
instance, let's say that in our original image, we had lots of pixels
that were at shades 127,127,127  128,128,128  and 129,129,129. They
would have made a solid appearing "bump" in the middle of the
Histogram. After applying our Histogram adjustment however, we might
now find that the pixels that were at 127,127,127 have now had their
color shifted downwards by one to 126,126,126 to help "fill in" those
empty first 45 shades of dark gray. The pixels that had been at
126,126,126 had also had their value reduced to 124,124,124 and the
pixels at 125,125,125 had their value reduced, etc. This continues
down the line until the available darker colors are spread across
that "empty" area that used to exist on the left side of the
Histogram. The pixels at 128,128,128 stayed where they were since
they were right in the middle. Similar to the darker shades, the
pixels at 129,129,129 and higher, had their colors changed upwards to
fill in those 45 shades of light gray that had been empty.

 When the Histogram "spreads out" the available shades to fill those
empty areas, it leaves gaps where none existed before. For instance,
in our example above the shades at 127, 128 and 129 were side by
side. After the Histogram adjustment however, they are now separated
by a single empty shade in between each one. Visually, what was once
a solid bar now appears as a series of three "spikes". The key to
remember here though, is that we are dealing with a graph of
brightness values, not the image itself. While you can see these
"spikes" and they appear obvious to you in the graph, when it comes
to looking at the image itself, it's an entirely different story.
What we have done is, we have gone from a difference between three
mid grays at 127,127,127 to 129,129,129 in the original image, to a
difference between three mid grays of 126,126,126 to 130,130,130. To
the human eye, such minor differences are extremely difficult to
detect when they appy to such small differences in shading.

 So, understanding this, as far as your original questions are
concerned, the "short answer" is:

 No. Having those spikes in the Histogram are nothing to be concerned
about (IMHO).

--
Rick Simon

 
 
 

Histogram Question

Post by Kris Zaklik » Thu, 18 Apr 2002 00:06:50


Rick Simon wrote:

> Uni <plgp...@usa.net> wrote in news:3CBB999F.71849F3D@usa.net:

> > Sometimes when I adjust the Low and/or High values of the
> > Histogram, I end up with a graph with steep, but very narrow,
> > dropouts in it. Does this mean there is something wrong with the
> > image? How do you go about setting the High and Low values of the
> > Histogram and avoid this?

>  There's nothing inherently wrong with that, John. When working with
> the Histogram, it's helpful to understand that the graph you are
> looking at does not represent the image itself. Instead, it
> represents the distribution of colors within the image.

>  For instance, as Kris pointed out in a recent posting, lets say you
> were to write a small program that simply rearranged all the pixels
> in an image in a random manner. Not changed the color value of any of
> them mind you, just moved them around within the image in a random
> manner. You would end up with an image that looked like random noise.
> If you were to look at the Histogram for that "randomized" image and
> compare it to the Histogram for the original image though, you would
> see that the two Histograms were exactly the same! A Histogram does
> not deal with shapes or areas. It simply divides the pixels within
> the image up into brightness levels from 0 to 255 and graphs them
> out. It doesn't care where the pixels are within the image, it only
> cares how bright each one is.

>  As an example to illustrate what you are seeing with a Histogram
> adjustment, let's say you are working on a grayscaled image (easiest
> to explain) and using a Histogram to adjust Luminosity. Let's say
> that this image has a large flat area with zero data that extends
> inwards from the left edge of the Histogram for about 1/2". What does
> that tell you? To me, it says that there are very few (or no) pixels
> in the image that are pure black (0,0,0 - corresponds to the leftmost
> edge of the Histogram). Furthermore, the further I go from the left
> edge before I encounter data (or a rise in the graph), the more
> shades of dark gray (1,1,1 or 2,2,2 or 3,3,3...) there are that are
> not being used. By the time I've gone roughly 1/2" into the
> Histogram, I'm up to 45,45,45. If the line in the Histogram is still
> flat, that means that the image has no pixels in it that are
> utilizing any of those dark gray colors, from pure black 0,0,0 to
> dark gray 45,45,45.

>  Let's say that our image also has a correspondingly large flat area
> on the right side as well. That tells me that the image is also not
> using pure white (255,255,255) or a number of shades of very light
> gray. For the sake of simplicity, we'll say that the right side is
> pretty much like the left side and that we can come inwards around 45
> shades of gray before we find any rise in the Histogram. That means
> that the image is also not using white or any shade of light gray
> above 210,210,210.

>  Overall then, we have an image that is only using 166 shades of gray
> out of the total 256 shades that are available to us. As with any
> process that reduces the numbers of colors available, this tends to
> reduce the quality of the image. To make matters worse, this "color
> reduction" did not make use of any sort of optimized algorithm to
> reduce the colors while maintaining image quality. It simply chopped
> off the upper and lower ends.

>  Here's where the Histogram comes into play. By setting the lower and
> upper "clip limits" to 45 and 210 respectively, we are telling the
> Histogram tool that we want any pixels currently at 45,45,45 to
> become 0,0,0 (pure black) and any pixels at 210,210,210 to become
> pure white (255,255,255) when the Histogram Adjustment is applied.
> All other pixels throughout the entire image will also have their
> shade adjusted, correspondingly. What we are doing is, we are taking
> all of the current color values within the image and "stretching them
> out" so that they cover the entire gamut of available colors, from
> pure black to pure white. In our example image, that equates to
> taking 166 shades of gray, and spreading them across the 256 possible
> shades.

>  So how does the Histogram do that? It simply intersperses "empty"
> shades in between "full" shades in order to spread things out. For
> instance, let's say that in our original image, we had lots of pixels
> that were at shades 127,127,127  128,128,128  and 129,129,129. They
> would have made a solid appearing "bump" in the middle of the
> Histogram. After applying our Histogram adjustment however, we might
> now find that the pixels that were at 127,127,127 have now had their
> color shifted downwards by one to 126,126,126 to help "fill in" those
> empty first 45 shades of dark gray. The pixels that had been at
> 126,126,126 had also had their value reduced to 124,124,124 and the
> pixels at 125,125,125 had their value reduced, etc. This continues
> down the line until the available darker colors are spread across
> that "empty" area that used to exist on the left side of the
> Histogram. The pixels at 128,128,128 stayed where they were since
> they were right in the middle. Similar to the darker shades, the
> pixels at 129,129,129 and higher, had their colors changed upwards to
> fill in those 45 shades of light gray that had been empty.

>  When the Histogram "spreads out" the available shades to fill those
> empty areas, it leaves gaps where none existed before. For instance,
> in our example above the shades at 127, 128 and 129 were side by
> side. After the Histogram adjustment however, they are now separated
> by a single empty shade in between each one. Visually, what was once
> a solid bar now appears as a series of three "spikes". The key to
> remember here though, is that we are dealing with a graph of
> brightness values, not the image itself. While you can see these
> "spikes" and they appear obvious to you in the graph, when it comes
> to looking at the image itself, it's an entirely different story.
> What we have done is, we have gone from a difference between three
> mid grays at 127,127,127 to 129,129,129 in the original image, to a
> difference between three mid grays of 126,126,126 to 130,130,130. To
> the human eye, such minor differences are extremely difficult to
> detect when they appy to such small differences in shading.

>  So, understanding this, as far as your original questions are
> concerned, the "short answer" is:

>  No. Having those spikes in the Histogram are nothing to be concerned
> about (IMHO).

Rick, this is your usual very fine explanation of course.
Permit me to add just two things. You said that the histogram
"doesn't care where the pixels are within the image, it only
cares how bright each one is". That's exactly true. However,
in real life different regions of the image often correspond
approximately to different portions of the histogram. For
instance the black dress or suit is in a different region
from the light face. This is what sometimes makes it possible
to threshold an image to extract objects approximately and
what connects the subject matter to the histogram. It is
also why I made the suggestion I did in response to Kerry's
"stupid question time, how did I do this effect?" post.
(But, of course, you knew that :) The other point is that
you can't use a histogram adjustment to create data. If
the histogram occupied only part of the 0 to 255 brightness
range stretching it will improve contrast and make the image
look less washed out. The price might, however, be visible
banding in some regions. Because the histogram doesn't
know or care where a pixel is in the image, it cannot
somehow insert a pixel of intermediate shade between the
steps of the banding since it doesn't know where to place
it in the image in a way that makes sense for the subject.
As was discussed elsewhere in the thread, any form of
blurring, because it is spatially oriented, will introduce
new brightness levels in the histogram and place them in
the correct part of the image - but your image looks
blurred :) As a result, in the case of moderate banding
your best bet may be Edge Preserving, which can be the
compromise that lets you get the best of both worlds - not
many steps in the histogram and not much blurring of detail.

> --
> Rick Simon
> rsi...@cris.com

--
----------------------------------------------------------------------
Kris Zaklika              Jasc Software, Inc.                   The
Product Ideas:            id...@jasc.com                        Power
Customer Service:         customer.serv...@jasc.com             To
Technical Support:        tech...@jasc.com                      Create
----------------------------------------------------------------------
 
 
 

Histogram Question

Post by Uni » Thu, 18 Apr 2002 01:51:09




> > Sometimes when I adjust the Low and/or High values of the
> > Histogram, I end up with a graph with steep, but very narrow,
> > dropouts in it. Does this mean there is something wrong with the
> > image? How do you go about setting the High and Low values of the
> > Histogram and avoid this?

>  There's nothing inherently wrong with that, John. When working with
> the Histogram, it's helpful to understand that the graph you are
> looking at does not represent the image itself. Instead, it
> represents the distribution of colors within the image.

>  For instance, as Kris pointed out in a recent posting, lets say you
> were to write a small program that simply rearranged all the pixels
> in an image in a random manner. Not changed the color value of any of
> them mind you, just moved them around within the image in a random
> manner. You would end up with an image that looked like random noise.
> If you were to look at the Histogram for that "randomized" image and
> compare it to the Histogram for the original image though, you would
> see that the two Histograms were exactly the same! A Histogram does
> not deal with shapes or areas. It simply divides the pixels within
> the image up into brightness levels from 0 to 255 and graphs them
> out. It doesn't care where the pixels are within the image, it only
> cares how bright each one is.

>  As an example to illustrate what you are seeing with a Histogram
> adjustment, let's say you are working on a grayscaled image (easiest
> to explain) and using a Histogram to adjust Luminosity. Let's say
> that this image has a large flat area with zero data that extends
> inwards from the left edge of the Histogram for about 1/2". What does
> that tell you? To me, it says that there are very few (or no) pixels
> in the image that are pure black (0,0,0 - corresponds to the leftmost
> edge of the Histogram). Furthermore, the further I go from the left
> edge before I encounter data (or a rise in the graph), the more
> shades of dark gray (1,1,1 or 2,2,2 or 3,3,3...) there are that are
> not being used. By the time I've gone roughly 1/2" into the
> Histogram, I'm up to 45,45,45. If the line in the Histogram is still
> flat, that means that the image has no pixels in it that are
> utilizing any of those dark gray colors, from pure black 0,0,0 to
> dark gray 45,45,45.

>  Let's say that our image also has a correspondingly large flat area
> on the right side as well. That tells me that the image is also not
> using pure white (255,255,255) or a number of shades of very light
> gray. For the sake of simplicity, we'll say that the right side is
> pretty much like the left side and that we can come inwards around 45
> shades of gray before we find any rise in the Histogram. That means
> that the image is also not using white or any shade of light gray
> above 210,210,210.

>  Overall then, we have an image that is only using 166 shades of gray
> out of the total 256 shades that are available to us. As with any
> process that reduces the numbers of colors available, this tends to
> reduce the quality of the image. To make matters worse, this "color
> reduction" did not make use of any sort of optimized algorithm to
> reduce the colors while maintaining image quality. It simply chopped
> off the upper and lower ends.

>  Here's where the Histogram comes into play. By setting the lower and
> upper "clip limits" to 45 and 210 respectively, we are telling the
> Histogram tool that we want any pixels currently at 45,45,45 to
> become 0,0,0 (pure black) and any pixels at 210,210,210 to become
> pure white (255,255,255) when the Histogram Adjustment is applied.
> All other pixels throughout the entire image will also have their
> shade adjusted, correspondingly. What we are doing is, we are taking
> all of the current color values within the image and "stretching them
> out" so that they cover the entire gamut of available colors, from
> pure black to pure white. In our example image, that equates to
> taking 166 shades of gray, and spreading them across the 256 possible
> shades.

>  So how does the Histogram do that? It simply intersperses "empty"
> shades in between "full" shades in order to spread things out. For
> instance, let's say that in our original image, we had lots of pixels
> that were at shades 127,127,127  128,128,128  and 129,129,129. They
> would have made a solid appearing "bump" in the middle of the
> Histogram. After applying our Histogram adjustment however, we might
> now find that the pixels that were at 127,127,127 have now had their
> color shifted downwards by one to 126,126,126 to help "fill in" those
> empty first 45 shades of dark gray. The pixels that had been at
> 126,126,126 had also had their value reduced to 124,124,124 and the
> pixels at 125,125,125 had their value reduced, etc. This continues
> down the line until the available darker colors are spread across
> that "empty" area that used to exist on the left side of the
> Histogram. The pixels at 128,128,128 stayed where they were since
> they were right in the middle. Similar to the darker shades, the
> pixels at 129,129,129 and higher, had their colors changed upwards to
> fill in those 45 shades of light gray that had been empty.

>  When the Histogram "spreads out" the available shades to fill those
> empty areas, it leaves gaps where none existed before. For instance,
> in our example above the shades at 127, 128 and 129 were side by
> side. After the Histogram adjustment however, they are now separated
> by a single empty shade in between each one. Visually, what was once
> a solid bar now appears as a series of three "spikes". The key to
> remember here though, is that we are dealing with a graph of
> brightness values, not the image itself. While you can see these
> "spikes" and they appear obvious to you in the graph, when it comes
> to looking at the image itself, it's an entirely different story.
> What we have done is, we have gone from a difference between three
> mid grays at 127,127,127 to 129,129,129 in the original image, to a
> difference between three mid grays of 126,126,126 to 130,130,130. To
> the human eye, such minor differences are extremely difficult to
> detect when they appy to such small differences in shading.

Whew! That was a lot to digest, Rick! :) Thank you!

I understand what you said. What bothered me though, was just a MINOR
adjustment of the Low end caused these gaps, or notches, to appear.

Some of the images I work with have a solid background color. This more
of less makes the Histogram graph worthless, even in its amplified
state. I generally end up guessing where the image should be adjusted.

One another note, I posted a couple of my scans on Webshots:
http://community.webshots.com/user/unidude2002

There's a "Dancer-1" and "Dancer-2" image there.
My pride is "Dancer-1", due to how small it was and what I had to do to
make it look somewhat photo realistic. A lot of manual softening and
redefining of the shadow detail went into it, due to low resolution
printing. No Automatic filters were used to correct the colors.
Actually, I frowned upon using the Automatic Color Balance filter, due
to what it does to the highlighted yellow colors in that image.

Since my monitor is the pits, I'd like to know what you think of them.
Be honest, please. I take criticism well.

Thank you, Rick, for all your help.

Regards,
John

P.S. If you do look at the Webshot images, the "Dancer-1" image should
not have the JPEG artifacts in the red outfits. Even though I delete,
then upload a replacement, that doesn't seem to replace the original
image.

- Show quoted text -

>  So, understanding this, as far as your original questions are
> concerned, the "short answer" is:

>  No. Having those spikes in the Histogram are nothing to be concerned
> about (IMHO).

> --
> Rick Simon


 
 
 

Histogram Question

Post by Rick Simo » Thu, 18 Apr 2002 02:23:15




Quote:

> The other point is that
> you can't use a histogram adjustment to create data. If
> the histogram occupied only part of the 0 to 255 brightness
> range stretching it will improve contrast and make the image
> look less washed out. The price might, however, be visible
> banding in some regions. Because the histogram doesn't
> know or care where a pixel is in the image, it cannot
> somehow insert a pixel of intermediate shade between the
> steps of the banding since it doesn't know where to place
> it in the image in a way that makes sense for the subject.

 Yep! I actually starting writing a bit about that in my earlier
posting but stopped and deleted it before posting the message. I had
also thought about mentioning more about using individual color
channels instead of just luminosity, but decided against it for the
same reason. Full explanations of that stuff would have necessitated
at least several more paragraphs and I figured the original was
already getting fairly long. There was already enough of my long-
winded material in there to put some people to sleep. <<smile>>

--
Rick Simon

 
 
 

Histogram Question

Post by Barry Sachai » Thu, 18 Apr 2002 02:51:33




>> Sometimes when I adjust the Low and/or High values of the
>> Histogram, I end up with a graph with steep, but very narrow,
>> dropouts in it. Does this mean there is something wrong with the
>> image? How do you go about setting the High and Low values of the
>> Histogram and avoid this?

> As an example to illustrate what you are seeing with a Histogram
>adjustment, let's say you are working on a grayscaled image (easiest
>to explain) and using a Histogram to adjust Luminosity. Let's say
>that this image has a large flat area with zero data that extends
>inwards from the left edge of the Histogram for about 1/2". What does

HALLELUJAH, NOW I GET IT!   Rick, thanks for the detailed explanation
and one that clears up a lot of stuff for me.

I just hope you're right.  <VBG>

Seriously, your post was a tremendous help to me.

 
 
 

Histogram Question

Post by Barbara J. Bradl » Thu, 18 Apr 2002 02:57:29






>> The other point is that
>> you can't use a histogram adjustment to create data. If
>> the histogram occupied only part of the 0 to 255 brightness
>> range stretching it will improve contrast and make the image
>> look less washed out. The price might, however, be visible
>> banding in some regions. Because the histogram doesn't
>> know or care where a pixel is in the image, it cannot
>> somehow insert a pixel of intermediate shade between the
>> steps of the banding since it doesn't know where to place
>> it in the image in a way that makes sense for the subject.

> Yep! I actually starting writing a bit about that in my earlier
>posting but stopped and deleted it before posting the message. I had
>also thought about mentioning more about using individual color
>channels instead of just luminosity, but decided against it for the
>same reason. Full explanations of that stuff would have necessitated
>at least several more paragraphs and I figured the original was
>already getting fairly long. There was already enough of my long-
>winded material in there to put some people to sleep. <<smile>>

>--
>Rick Simon

       Thanks to both of you for all the great info.  I save for future
reference since I can't remember all of it on the first read.
            Barb
 
 
 

Histogram Question

Post by Rick Simo » Thu, 18 Apr 2002 03:38:23



Quote:

> I understand what you said. What bothered me though, was just a
> MINOR adjustment of the Low end caused these gaps, or notches, to
> appear.

 It's all in perceptions. Let's say you have a 10' tall privacy fence
along one side of a yard. Let's also say it's comprised of 256
vertical boards, each one painted black. Now, back off 1/4 mile (1/2
km for our metric friends) and look at the fence. You can see the
fence, you may even notice that it appears to have a vertical texture
of some type, but pick out the individual boards? Not likely, unless
you have the eyes of an eagle.

 Now, without moving anything else, have someone remove every 25th
board from that fence and replace it with a white board. That means
that 10 white "gaps" will suddenly appear in that fence. I guarantee
that you'll be able to see them quite well. They'll really stand out.
What you would be seeing is the equivalent to a minor adjustment to
the Histogram by moving the Low end up from 0 to 9. Those 10 blank
"slots" are then spread out across the rest of the "fence" if you
will. The fact that they show up so well is merely due to the way we
perceive contrast, rather than any deficiency or problem with the
histogram.

Quote:> Some of the images I work with have a solid background color. This
> more of less makes the Histogram graph worthless, even in its
> amplified state. I generally end up guessing where the image
> should be adjusted.

 Why so? Simply select the background, invert the selection and apply
the histogram to the selected area. Of course, as with all
filters/modifications applied to selections, you may want to feather
the edges a bit, or at the very least, watch the edges of the
selected area closely in case the filter/effect you are applying has
a negative impact on it.

 In addition, the histogram adjustment tool comes with some excellent
preview panes which allow you to see the effect on the image. You can
use them to zoom in and pan around the image to observe the exact
effects the tool will have, before actually applying it.

 Note though, that the preview panes do not display selections
properly if you are using the above mentioned selection method. In
either case though, you also always have the option of observing the
image itself directly if you have enough room on your monitor to
shove the dialog box off to the side.

Quote:> There's a "Dancer-1" and "Dancer-2" image there.
> My pride is "Dancer-1", due to how small it was and what I had to
> do to make it look somewhat photo realistic. A lot of manual
> softening and redefining of the shadow detail went into it, due to
> low resolution printing. No Automatic filters were used to correct
> the colors. Actually, I frowned upon using the Automatic Color
> Balance filter, due to what it does to the highlighted yellow
> colors in that image.

 I'm not sure what you're asking me to comment on, but at first
glance the Dancer-1 image appears to be quite nice. The colors appear
to be well balanced with good saturation levels. As far as visible
evidence of retouch work, edges are the thing that often give that
away, however in this image those edges also have some .jpg
artifacting so it is difficult to tell one way or the other. The only
other things I noted off the top of my head were what appeared to be
a brush stoke on the left side of the image and something slightly
odd looking about a couple of the dancers costumes.

 If you look at the two young ladies on the far left (one seated one
standing), there appears to have been a vertical retouch tool brush
stroke run down the leg of the young lady standing up, which also cut
across the arm of the young lady sitting down. It leaves an odd, two-
toned appearance to her lower arm.

 There also appears to be something a little odd about the costume of
the same young lady standing up. Along the leftmost edge, down her
side, there appears to be a strip of lighter material which should be
shaded. Almost as if you had run a retouch tool down her side to
provide some shadowing but missed a thin strip right at the edge.
Might be a lighting effect, though the difference between the
adjacent shaded and lit up areas is a bit sharper than I would have
expected. Note a similar effect on the far right side of the young
lady standing up on the right side. She also has a lighter strip on
the outer edge of her costume, but the contrast appears less sharp.
In her case, it appears more like a lighting effect (IMHO).

 The last thing that didn't look quite right to me is the shading on
the costume of the young lady sitting on the pedestal. While all of
the others appear to be wearing a satin like material that provides a
nice, reflective sheen, hers does not. Perhaps it's just that the
material of her costume is made of a different material (velour
perhaps?). Or perhaps it's just an oddity of lighting, but it just
does not look quite right. More blotchy than it should (again IMHO).

--
Rick Simon

 
 
 

Histogram Question

Post by Uni » Thu, 18 Apr 2002 05:42:09


Rick, I'm a little short on time, and I'll address your other comments
later. However, I just wanted to give you a BIG THANK YOU for the
selective type Histogram tip!!! It works!! :)

John



> > I understand what you said. What bothered me though, was just a
> > MINOR adjustment of the Low end caused these gaps, or notches, to
> > appear.

>  It's all in perceptions. Let's say you have a 10' tall privacy fence
> along one side of a yard. Let's also say it's comprised of 256
> vertical boards, each one painted black. Now, back off 1/4 mile (1/2
> km for our metric friends) and look at the fence. You can see the
> fence, you may even notice that it appears to have a vertical texture
> of some type, but pick out the individual boards? Not likely, unless
> you have the eyes of an eagle.

>  Now, without moving anything else, have someone remove every 25th
> board from that fence and replace it with a white board. That means
> that 10 white "gaps" will suddenly appear in that fence. I guarantee
> that you'll be able to see them quite well. They'll really stand out.
> What you would be seeing is the equivalent to a minor adjustment to
> the Histogram by moving the Low end up from 0 to 9. Those 10 blank
> "slots" are then spread out across the rest of the "fence" if you
> will. The fact that they show up so well is merely due to the way we
> perceive contrast, rather than any deficiency or problem with the
> histogram.

> > Some of the images I work with have a solid background color. This
> > more of less makes the Histogram graph worthless, even in its
> > amplified state. I generally end up guessing where the image
> > should be adjusted.

>  Why so? Simply select the background, invert the selection and apply
> the histogram to the selected area. Of course, as with all
> filters/modifications applied to selections, you may want to feather
> the edges a bit, or at the very least, watch the edges of the
> selected area closely in case the filter/effect you are applying has
> a negative impact on it.

>  In addition, the histogram adjustment tool comes with some excellent
> preview panes which allow you to see the effect on the image. You can
> use them to zoom in and pan around the image to observe the exact
> effects the tool will have, before actually applying it.

>  Note though, that the preview panes do not display selections
> properly if you are using the above mentioned selection method. In
> either case though, you also always have the option of observing the
> image itself directly if you have enough room on your monitor to
> shove the dialog box off to the side.

> > There's a "Dancer-1" and "Dancer-2" image there.
> > My pride is "Dancer-1", due to how small it was and what I had to
> > do to make it look somewhat photo realistic. A lot of manual
> > softening and redefining of the shadow detail went into it, due to
> > low resolution printing. No Automatic filters were used to correct
> > the colors. Actually, I frowned upon using the Automatic Color
> > Balance filter, due to what it does to the highlighted yellow
> > colors in that image.

>  I'm not sure what you're asking me to comment on, but at first
> glance the Dancer-1 image appears to be quite nice. The colors appear
> to be well balanced with good saturation levels. As far as visible
> evidence of retouch work, edges are the thing that often give that
> away, however in this image those edges also have some .jpg
> artifacting so it is difficult to tell one way or the other. The only
> other things I noted off the top of my head were what appeared to be
> a brush stoke on the left side of the image and something slightly
> odd looking about a couple of the dancers costumes.

>  If you look at the two young ladies on the far left (one seated one
> standing), there appears to have been a vertical retouch tool brush
> stroke run down the leg of the young lady standing up, which also cut
> across the arm of the young lady sitting down. It leaves an odd, two-
> toned appearance to her lower arm.

>  There also appears to be something a little odd about the costume of
> the same young lady standing up. Along the leftmost edge, down her
> side, there appears to be a strip of lighter material which should be
> shaded. Almost as if you had run a retouch tool down her side to
> provide some shadowing but missed a thin strip right at the edge.
> Might be a lighting effect, though the difference between the
> adjacent shaded and lit up areas is a bit sharper than I would have
> expected. Note a similar effect on the far right side of the young
> lady standing up on the right side. She also has a lighter strip on
> the outer edge of her costume, but the contrast appears less sharp.
> In her case, it appears more like a lighting effect (IMHO).

>  The last thing that didn't look quite right to me is the shading on
> the costume of the young lady sitting on the pedestal. While all of
> the others appear to be wearing a satin like material that provides a
> nice, reflective sheen, hers does not. Perhaps it's just that the
> material of her costume is made of a different material (velour
> perhaps?). Or perhaps it's just an oddity of lighting, but it just
> does not look quite right. More blotchy than it should (again IMHO).

> --
> Rick Simon


 
 
 

Histogram Question

Post by Fugitiv » Thu, 18 Apr 2002 06:43:44





>> > Sometimes when I adjust the Low and/or High values of the
>> > Histogram, I end up with a graph with steep, but very narrow,
>> > dropouts in it. Does this mean there is something wrong with the
>> > image? How do you go about setting the High and Low values of the
>> > Histogram and avoid this?

>>  There's nothing inherently wrong with that, John. When working with
>> the Histogram, it's helpful to understand that the graph you are
>> looking at does not represent the image itself. Instead, it
>> represents the distribution of colors within the image.

>>  For instance, as Kris pointed out in a recent posting, lets say you
>> were to write a small program that simply rearranged all the pixels
>> in an image in a random manner. Not changed the color value of any of
>> them mind you, just moved them around within the image in a random
>> manner. You would end up with an image that looked like random noise.
>> If you were to look at the Histogram for that "randomized" image and
>> compare it to the Histogram for the original image though, you would
>> see that the two Histograms were exactly the same! A Histogram does
>> not deal with shapes or areas. It simply divides the pixels within
>> the image up into brightness levels from 0 to 255 and graphs them
>> out. It doesn't care where the pixels are within the image, it only
>> cares how bright each one is.

>>  As an example to illustrate what you are seeing with a Histogram
>> adjustment, let's say you are working on a grayscaled image (easiest
>> to explain) and using a Histogram to adjust Luminosity. Let's say
>> that this image has a large flat area with zero data that extends
>> inwards from the left edge of the Histogram for about 1/2". What does
>> that tell you? To me, it says that there are very few (or no) pixels
>> in the image that are pure black (0,0,0 - corresponds to the leftmost
>> edge of the Histogram). Furthermore, the further I go from the left
>> edge before I encounter data (or a rise in the graph), the more
>> shades of dark gray (1,1,1 or 2,2,2 or 3,3,3...) there are that are
>> not being used. By the time I've gone roughly 1/2" into the
>> Histogram, I'm up to 45,45,45. If the line in the Histogram is still
>> flat, that means that the image has no pixels in it that are
>> utilizing any of those dark gray colors, from pure black 0,0,0 to
>> dark gray 45,45,45.

>>  Let's say that our image also has a correspondingly large flat area
>> on the right side as well. That tells me that the image is also not
>> using pure white (255,255,255) or a number of shades of very light
>> gray. For the sake of simplicity, we'll say that the right side is
>> pretty much like the left side and that we can come inwards around 45
>> shades of gray before we find any rise in the Histogram. That means
>> that the image is also not using white or any shade of light gray
>> above 210,210,210.

>>  Overall then, we have an image that is only using 166 shades of gray
>> out of the total 256 shades that are available to us. As with any
>> process that reduces the numbers of colors available, this tends to
>> reduce the quality of the image. To make matters worse, this "color
>> reduction" did not make use of any sort of optimized algorithm to
>> reduce the colors while maintaining image quality. It simply chopped
>> off the upper and lower ends.

>>  Here's where the Histogram comes into play. By setting the lower and
>> upper "clip limits" to 45 and 210 respectively, we are telling the
>> Histogram tool that we want any pixels currently at 45,45,45 to
>> become 0,0,0 (pure black) and any pixels at 210,210,210 to become
>> pure white (255,255,255) when the Histogram Adjustment is applied.
>> All other pixels throughout the entire image will also have their
>> shade adjusted, correspondingly. What we are doing is, we are taking
>> all of the current color values within the image and "stretching them
>> out" so that they cover the entire gamut of available colors, from
>> pure black to pure white. In our example image, that equates to
>> taking 166 shades of gray, and spreading them across the 256 possible
>> shades.

>>  So how does the Histogram do that? It simply intersperses "empty"
>> shades in between "full" shades in order to spread things out. For
>> instance, let's say that in our original image, we had lots of pixels
>> that were at shades 127,127,127  128,128,128  and 129,129,129. They
>> would have made a solid appearing "bump" in the middle of the
>> Histogram. After applying our Histogram adjustment however, we might
>> now find that the pixels that were at 127,127,127 have now had their
>> color shifted downwards by one to 126,126,126 to help "fill in" those
>> empty first 45 shades of dark gray. The pixels that had been at
>> 126,126,126 had also had their value reduced to 124,124,124 and the
>> pixels at 125,125,125 had their value reduced, etc. This continues
>> down the line until the available darker colors are spread across
>> that "empty" area that used to exist on the left side of the
>> Histogram. The pixels at 128,128,128 stayed where they were since
>> they were right in the middle. Similar to the darker shades, the
>> pixels at 129,129,129 and higher, had their colors changed upwards to
>> fill in those 45 shades of light gray that had been empty.

>>  When the Histogram "spreads out" the available shades to fill those
>> empty areas, it leaves gaps where none existed before. For instance,
>> in our example above the shades at 127, 128 and 129 were side by
>> side. After the Histogram adjustment however, they are now separated
>> by a single empty shade in between each one. Visually, what was once
>> a solid bar now appears as a series of three "spikes". The key to
>> remember here though, is that we are dealing with a graph of
>> brightness values, not the image itself. While you can see these
>> "spikes" and they appear obvious to you in the graph, when it comes
>> to looking at the image itself, it's an entirely different story.
>> What we have done is, we have gone from a difference between three
>> mid grays at 127,127,127 to 129,129,129 in the original image, to a
>> difference between three mid grays of 126,126,126 to 130,130,130. To
>> the human eye, such minor differences are extremely difficult to
>> detect when they appy to such small differences in shading.

>Whew! That was a lot to digest, Rick! :) Thank you!

>I understand what you said. What bothered me though, was just a MINOR
>adjustment of the Low end caused these gaps, or notches, to appear.

>Some of the images I work with have a solid background color. This more
>of less makes the Histogram graph worthless, even in its amplified
>state. I generally end up guessing where the image should be adjusted.

>One another note, I posted a couple of my scans on Webshots:
>http://community.webshots.com/user/unidude2002

>There's a "Dancer-1" and "Dancer-2" image there.
>My pride is "Dancer-1", due to how small it was and what I had to do to
>make it look somewhat photo realistic. A lot of manual softening and
>redefining of the shadow detail went into it, due to low resolution
>printing. No Automatic filters were used to correct the colors.
>Actually, I frowned upon using the Automatic Color Balance filter, due
>to what it does to the highlighted yellow colors in that image.

>Since my monitor is the pits, I'd like to know what you think of them.
>Be honest, please. I take criticism well.

>Thank you, Rick, for all your help.

>Regards,
>John

>P.S. If you do look at the Webshot images, the "Dancer-1" image should
>not have the JPEG artifacts in the red outfits. Even though I delete,
>then upload a replacement, that doesn't seem to replace the original
>image.

In my webshots, the uploaded file does not replace but adds to the
dirc. One has to delete the file themselves. Yes, it will tolerate 2
files, side by side with the same name. Try this, rename it.

Greg

a few things....< http://community.webshots.com/user/fugitive02
It is better to be high-spirited even though one makes more mistakes, than to be narrow-minded and all to prudent.
V.Van Gogh

 
 
 

Histogram Question

Post by Uni » Thu, 18 Apr 2002 09:34:39


Fugitive wrote:

> On Tue, 16 Apr 2002 12:51:09 -0400, Uni <plgp...@usa.net> wrote:

> >Rick Simon wrote:

> >> Uni <plgp...@usa.net> wrote in news:3CBB999F.71849F3D@usa.net:

> >> > Sometimes when I adjust the Low and/or High values of the
> >> > Histogram, I end up with a graph with steep, but very narrow,
> >> > dropouts in it. Does this mean there is something wrong with the
> >> > image? How do you go about setting the High and Low values of the
> >> > Histogram and avoid this?

> >>  There's nothing inherently wrong with that, John. When working with
> >> the Histogram, it's helpful to understand that the graph you are
> >> looking at does not represent the image itself. Instead, it
> >> represents the distribution of colors within the image.

> >>  For instance, as Kris pointed out in a recent posting, lets say you
> >> were to write a small program that simply rearranged all the pixels
> >> in an image in a random manner. Not changed the color value of any of
> >> them mind you, just moved them around within the image in a random
> >> manner. You would end up with an image that looked like random noise.
> >> If you were to look at the Histogram for that "randomized" image and
> >> compare it to the Histogram for the original image though, you would
> >> see that the two Histograms were exactly the same! A Histogram does
> >> not deal with shapes or areas. It simply divides the pixels within
> >> the image up into brightness levels from 0 to 255 and graphs them
> >> out. It doesn't care where the pixels are within the image, it only
> >> cares how bright each one is.

> >>  As an example to illustrate what you are seeing with a Histogram
> >> adjustment, let's say you are working on a grayscaled image (easiest
> >> to explain) and using a Histogram to adjust Luminosity. Let's say
> >> that this image has a large flat area with zero data that extends
> >> inwards from the left edge of the Histogram for about 1/2". What does
> >> that tell you? To me, it says that there are very few (or no) pixels
> >> in the image that are pure black (0,0,0 - corresponds to the leftmost
> >> edge of the Histogram). Furthermore, the further I go from the left
> >> edge before I encounter data (or a rise in the graph), the more
> >> shades of dark gray (1,1,1 or 2,2,2 or 3,3,3...) there are that are
> >> not being used. By the time I've gone roughly 1/2" into the
> >> Histogram, I'm up to 45,45,45. If the line in the Histogram is still
> >> flat, that means that the image has no pixels in it that are
> >> utilizing any of those dark gray colors, from pure black 0,0,0 to
> >> dark gray 45,45,45.

> >>  Let's say that our image also has a correspondingly large flat area
> >> on the right side as well. That tells me that the image is also not
> >> using pure white (255,255,255) or a number of shades of very light
> >> gray. For the sake of simplicity, we'll say that the right side is
> >> pretty much like the left side and that we can come inwards around 45
> >> shades of gray before we find any rise in the Histogram. That means
> >> that the image is also not using white or any shade of light gray
> >> above 210,210,210.

> >>  Overall then, we have an image that is only using 166 shades of gray
> >> out of the total 256 shades that are available to us. As with any
> >> process that reduces the numbers of colors available, this tends to
> >> reduce the quality of the image. To make matters worse, this "color
> >> reduction" did not make use of any sort of optimized algorithm to
> >> reduce the colors while maintaining image quality. It simply chopped
> >> off the upper and lower ends.

> >>  Here's where the Histogram comes into play. By setting the lower and
> >> upper "clip limits" to 45 and 210 respectively, we are telling the
> >> Histogram tool that we want any pixels currently at 45,45,45 to
> >> become 0,0,0 (pure black) and any pixels at 210,210,210 to become
> >> pure white (255,255,255) when the Histogram Adjustment is applied.
> >> All other pixels throughout the entire image will also have their
> >> shade adjusted, correspondingly. What we are doing is, we are taking
> >> all of the current color values within the image and "stretching them
> >> out" so that they cover the entire gamut of available colors, from
> >> pure black to pure white. In our example image, that equates to
> >> taking 166 shades of gray, and spreading them across the 256 possible
> >> shades.

> >>  So how does the Histogram do that? It simply intersperses "empty"
> >> shades in between "full" shades in order to spread things out. For
> >> instance, let's say that in our original image, we had lots of pixels
> >> that were at shades 127,127,127  128,128,128  and 129,129,129. They
> >> would have made a solid appearing "bump" in the middle of the
> >> Histogram. After applying our Histogram adjustment however, we might
> >> now find that the pixels that were at 127,127,127 have now had their
> >> color shifted downwards by one to 126,126,126 to help "fill in" those
> >> empty first 45 shades of dark gray. The pixels that had been at
> >> 126,126,126 had also had their value reduced to 124,124,124 and the
> >> pixels at 125,125,125 had their value reduced, etc. This continues
> >> down the line until the available darker colors are spread across
> >> that "empty" area that used to exist on the left side of the
> >> Histogram. The pixels at 128,128,128 stayed where they were since
> >> they were right in the middle. Similar to the darker shades, the
> >> pixels at 129,129,129 and higher, had their colors changed upwards to
> >> fill in those 45 shades of light gray that had been empty.

> >>  When the Histogram "spreads out" the available shades to fill those
> >> empty areas, it leaves gaps where none existed before. For instance,
> >> in our example above the shades at 127, 128 and 129 were side by
> >> side. After the Histogram adjustment however, they are now separated
> >> by a single empty shade in between each one. Visually, what was once
> >> a solid bar now appears as a series of three "spikes". The key to
> >> remember here though, is that we are dealing with a graph of
> >> brightness values, not the image itself. While you can see these
> >> "spikes" and they appear obvious to you in the graph, when it comes
> >> to looking at the image itself, it's an entirely different story.
> >> What we have done is, we have gone from a difference between three
> >> mid grays at 127,127,127 to 129,129,129 in the original image, to a
> >> difference between three mid grays of 126,126,126 to 130,130,130. To
> >> the human eye, such minor differences are extremely difficult to
> >> detect when they appy to such small differences in shading.

> >Whew! That was a lot to digest, Rick! :) Thank you!

> >I understand what you said. What bothered me though, was just a MINOR
> >adjustment of the Low end caused these gaps, or notches, to appear.

> >Some of the images I work with have a solid background color. This more
> >of less makes the Histogram graph worthless, even in its amplified
> >state. I generally end up guessing where the image should be adjusted.

> >One another note, I posted a couple of my scans on Webshots:
> >http://community.webshots.com/user/unidude2002

> >There's a "Dancer-1" and "Dancer-2" image there.
> >My pride is "Dancer-1", due to how small it was and what I had to do to
> >make it look somewhat photo realistic. A lot of manual softening and
> >redefining of the shadow detail went into it, due to low resolution
> >printing. No Automatic filters were used to correct the colors.
> >Actually, I frowned upon using the Automatic Color Balance filter, due
> >to what it does to the highlighted yellow colors in that image.

> >Since my monitor is the pits, I'd like to know what you think of them.
> >Be honest, please. I take criticism well.

> >Thank you, Rick, for all your help.

> >Regards,
> >John

> >P.S. If you do look at the Webshot images, the "Dancer-1" image should
> >not have the JPEG artifacts in the red outfits. Even though I delete,
> >then upload a replacement, that doesn't seem to replace the original
> >image.

> In my webshots, the uploaded file does not replace but adds to the
> dirc. One has to delete the file themselves. Yes, it will tolerate 2
> files, side by side with the same name. Try this, rename it.

Thanks, Greg! I will try that.

Uni

- Show quoted text -

> Greg

> a few things....< http://community.webshots.com/user/fugitive02
> It is better to be high-spirited even though one makes more mistakes, than to be narrow-minded and all to prudent.
> V.Van Gogh

 
 
 

1. OpenGL Convolution & Histograms question

I've got a quick question concerning the convolution & histogram
extensions under the SGI implementation of OpenGL.  I'm using RE3
hardware under IRIX 6.2.  The routines don't seem much faster (if at
all) than the pure CPU counterparts that I coded up.  Does anyone know
if these two extensions actually use the graphics hardware or are they
implemented using the CPU instead.

Thanks in advance.

--
 Regards,
   Loren Knutson

2. 6.5b?

3. PSP 6.0 Histogram question

4. What are effective resolutions of HDTV and film movies?

5. Histogram questions

6. New IntelliEye Mouse and PSP

7. Histogram question

8. Publication opportunities - Journals/Issues ...

9. Scanner/Histogram Question

10. HISTOGRAM Question

11. histogram question

12. OpenGL Convolution & Histograms question