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Determining Color Difference Using the CIELAB Model?
Colour Blind asks: "I am working for a not-for-profit organization developing a website for kids. I am attempting to develop a method for testing if two colours (as defined by R, G, and B values [0-255]) are adequately different to be visible on top of each other. So far I have tried many things but this is the one that, by all accounts, should work: I have converted from RGB to (CIE)XYZ using a 3x3 matrix transformation. From here I have used three more equations to convert to CIELAB colour. I have then calculated the distance between the two colours in question in CIELAB colour space. The results are not correct: there are pairs of colours that are quite far from visible that yield the same difference as colours that are plainly acceptable for visibility. Any suggestions?"
There is a large body of research on perceptually uniform color spaces. Last I checked no one had an accurate model, though many have tried. Look on citeseer or something, or google for that matter. Best of luck!
Music speeds up when you yawn, but does not change pitch.
Ask Google
Ryan T. Sammartino
"Ancora imparo"
A friend of mine wrote a paper on this topic:
Limitations of Colour Management.
In addition, it sounds like you're hoping to test whether things are sufficiently perceptually different on people's monitors. The sad news is that the variation between different monitors, between LCDs and CRTs, between different brightness and contrast settings, between different phosphor technologies, differences in how long the monitor has been warmed up, and differences in the aging of the phosphors mean that no two monitors will actually produce the same color from the same R, G and B equivalents, and you'll get different distinguishabilities for different colors on different monitors.
As a nature photographer, I have to jump through hoops, including hardware sensors for detecting the output of my monitor, to get anything like reproducable color out of my own equipment. It's just a difficult problem, I'm afraid.
I'm a nature photographer.
I think the key in picking colours for any website is that they have have a difference of at least X in brightness (the V in Hue-Saturation-Value unless I'm sadly mistaken - I'm not an expert in this area), where you should be able to determine X experimentally. Any decent color picker (such as those in Gimp or Photoshop) will allow you to jump between RGB or HSV. The reason I think this is the way to go is that a decently large percentage of the population (at any age) is colour blind, so while you or I may easily be able to see the difference between a blue and green, or a green and red, at the same brightness, some people (particularly males), just can't.
That should take care of you for making the site usable. At that point, the choice of which light or dark colors you use for what becomes purely stylistic (again, I'm just a stupid computer scientist - I'm sure someone with a stronger HUI, marketing, or fine arts background might have a stronger opinion on what colors are used for what).
-"Zow"
Have you been able to demonstrate that the small difference seen between two contrasting colors is due to a flaw in the CIELAB model? If so, perhaps you can publish a paper on the subject. Otherwise, what evidence do you have that your program is actually doing the right thing? For example, CIELAB appears to use polar coordinates. Are you sure that you are treating 0 and 2*PI as being the same value?
Also, you have not demonstrated a need for such an evaluation function to exist. Is this because the site designers have a problem being able to choose readable colors? Perhaps if these people cannot select a good color scheme, you need new designers.
Note, IAANR (I am a neurobiology researcher), but I deal mainly with ion channels so take this with an appropriately sized grain of salt:
Your ability to tell the difference between two colors (or light intensities to bring back the classic experiments) depends on how you approach the limit of perception (or differentiation in this case). Classically, if you start where you can not perceive a difference between to intensities, and increase the difference, your threshold for difference will be lower than had you started when you could tell the difference between the two intensities and gradually decreased this difference. It seems that color differences should follow the same rules as light intensities. Also keep in mind that we're more sensitive to differences in shades of blue, IIRC, due to overlaps in the sensitivity ranges of long, medium and short cones. So, these are probably among the causes for you observations.
There's nothing on this page having anything to do with color. Googling for "HolyColor" didn't turn up anything either.
"It take 9 months to bear a child, no matter how many women you assign to the job."
Automated colour scheme generation. Let's face it, most people (myself included) can't generate a decent colour scheme to save themselves. Having a program create scheme suggestions automatically is extremely useful (blatant plug: check my sig for my own attempt at doing this) for non-artists.
:)
There are a load of tricks for generating colours which usually work well together; however, the hue rotations trick sometimes generates totally unreadable combinations (mainly with bright colours). Having an additional check would be extremely useful.
Anyway, it could be that you're good at graphics, in which case good for you. Just give the rest of us time to catch up
Edwin Land (yes, the founder of Polaroid) did work in human color perception, where he showed that two colors could used to create an apparent full color image.
The important things are our visual expectations, as well as the relative intensity of parts of the scene. I can remember a demo from Land where two projectors sufficed to give a full color scene. If part of the image was abstracted, it appeared to be black and white ! This implies that a combination of two colors can, under certain circumstances, appear to be the same as a different combination of three colors. I would suspect that this effect would have to be considered in the vision tests described in the original posts.
A Michigan State U. report on the Land work is available, as is a lot of more recent work, such as this paper by Kobus Barnard.
There are a number of critical factors in this process that you haven't told us. The issues of display devices, gamma, and implementation details all play an important role in your ability to visibly distinguish between two colors.
What sort of monitor are you using? Have you correctly callibrated the display? What software are you using to display the colors? How does this software deal with display gamma? Other important details include the brightness of the surrounding environment, other windows and such on the screen which can distract the eye and interfere with your visual processing of the colors.
If you haven't already read the books and web pages by Charles Poynton, they cover all the details. Color issues always seem simple, but actually this is an extremely complex and subtle issue. Also, people's ability to visually distinguish color varies quite a bit. A surprisingly large percentage of the population is color blind to at least some portion of the spectrum. Also, display devices vary widely in their ability to correctly display different colors.
Anyway, to sum it all up, I'd be really surprised if you can use any sort of theory to predict whether you can visually distinguish between different colors. Even with correctly callibrated equipment, and experienced researchers, I doubt that your problem is easily answered!
Best of luck,
Daniel Wexler
www.flarg.com
Another issue that should be considered is that approximately 5% of humans are red-green color blind. There are other forms that are more rare, but in designing web-sites it is common enough that it should be taken into consideration.
See http://www.visibone.com/colorblind/ for useful color information specific to web-site design.
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Not only do the differences between individual monitors - make, model, batch, age, time used, etc - affect the state of the phosphor and so the displayed colour, so do brightness, contrast and ambient lighting. That's not even taking LCD panels into consideration.
Then you've got the fact that not only does everyone perceive colour in a different way, but some people are colour-blind! I myself am unable to distinguish between the standard yellow & green used in 4-colour mode on the BBC Micro from so long ago - the last census stats I saw indicated that 8% of the population have some form of colour-blindness.
I think you really need to re-think your web-site design - after all, what's wrong with black on white?
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- The CIE color space: A pretty decent introduction to what the CIE color space is
- Color FAQ: I haven't read through this, but it seems to be a more extensive coverage of color and how it's much more than RGB, HSV, or CYMK.
The short version is that all the different primary color systems--RGB (red-green-blue), CYMK (cyan-yellow-magenta-black), HSV (hue-saturation-value)--can represent some, but not all, of the colors visible to the human eye. Even specifying colors by the wavelength of the light emitted or reflected covers only a small subset of colors--in fact an even smaller subset than any of the primary color representations. The CIE system identifies colors by an XYZ coordinate system, where X, Y, and Z are artificial primary colors that span the full range of colors visible to the human eye."It take 9 months to bear a child, no matter how many women you assign to the job."
Oh, just to make things even more difficult, two colors which are quite perceptually distinct may still make poor colors for text legibility. Try reading bright red text on a bright green, equal-intensity background. (Even if you're not red-green color-blind.) I suspect without evidence that text legiblity is more strongly related to luminosity differences than to perceptual difference metrics.
I'm a nature photographer.
Are you doing it pixel by pixel? Sometimes pixels are not discrete color units, in which case you might want to reconsider your algorithm. For example, if you have a mosaic effect from newspaper you've scanned in, pixels are going to mirror the little newspaper specks of color introduced by the printing process. Maybe you want to have some sort of averaging method?
Porison and Wandrell adapted CIELAB color models to account for the quirks of monitors. You need to have information on how far away people sit from the monitor, the resolution, the size, etc., but it's actually quite good. Here's a MATLAB implementation by Zhang at Stanford.
One problem I had when I was working with this is that the pixels were not lining up correctly. Try overlaying the images and the CIELAB error to make sure your results are sensible.
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I am an expert in electricity. My father held the chair of applied electricity at the state prision.
I did work on this in college for a physics of light and color class. His experiment worked best when he used cyan and red filters for the projectors/cameras. Cyan is the light equivalent of combining blue and green. So in effect, you get RGB, with only taking two black and white samples of a scene.
This is also how 2-chip DLP works. 1-chip DLP uses a color wheel containing RGB, and alterately projects an image of each color, the chip is, in essence, the black and white sample. 2-chip DLP uses a cyan filter on one of the projector's chips, and a red filter on the other, in effect, reproducing Land's experiment! 3-chip DLP uses a chip for each of RGB.
My sentiments exactly. Nothing is better than the human eye for differentiating colors.
Damnit, Jim, I'm an anarchist, not a F@#$!^& doctor!
This is an excellent question, as male engineers have the tendancy to be Red/Green colorblind.
o rmodels/main.html
2 1. htm
In order to help those of us who can't match our shirts with our pants, various "color models" have been developed over the past 340 years (although some pre-date plato), including RGB (cube), HSV (tetrahedron), CIE, YIQ, PANTONE (spherical/spiral), etc. Although this question is not exactly technical in nature, the underlying problem is about the mathmatics of converting the various geometries of the color spaces with an emphasis on web usability and accessibility... gdb with or without a serial cable is of little use here.
Check out: http://www.adobe.com/support/techguides/color/col
And for the math:
http://academic.mu.edu/phys/matthysd/web226/L02
You're trying to approach a simple task with far too complex an array of mathematics and programming skill.
:P) use this in work every day.
You want to select colors that contrast with each other, yes? Humans are more sensitive to value than anything else. Painters and photographers and designers (well, not so many designers
Forget about saturation and hue -- if the value of the adjacent colors is 20-30% different, you can be pretty sure that most human beings will be able to see the contrast between them. Note that i'm talking about value in the sense of 0 being black and 100 being white. So you can easily make 3 or 4 colors simultaneously contrast with each other.
Feel free to pick hue and saturation at random, you'll have a pair of colors that contrast and go together. Doing more than 2-3 colors is harder to make the colors work together without someone with design skill stepping in...
Recursive: Adj. See Recursive.
I would recommend making sure you are accounting for the gamma of your monitor in your matricies. That is, your transforms should be, R'G'B'->RGB->XYZ->L*a*b* and then compare. You can also try making comparitions in L*u'v' space, which is also supposed to be "perceptually uniform". You should also generate some gradients in L*a*b* space and see if they match some you might find on the web.
That said, both of these colour spaces are really only approximations, and I think they'll be weighted towards uniformity in pure colours. Maybe a colour that pulls from a wider range of the spectrum like orange or gold might be smaller than the green or blue areas. I'm going to try and generate some test images to verify this either way.
-- MarkusQ
Yeah so this was interesting as there have been tetrachromats discovered in other primates (monkeys other than humans), so it was reasoned that it might be possible to find tetrachromacy in humans.
The advantage that hyperdimensional color perception has over traditional trichromacy it a better ability to discriminate hues or different colors. Therefore a tetrachromat could be considered to be at a certain advantage when it comes to color discrimination. This obviously has not been important to our evolution but it is for some species as birds and turtles see a world we can only imagine with some birds seeing from ultraviolet into the visible spectrum and turtles seeing a world rich with color. For instance, if you were to imagine a turtle sitting in a pond with the water as still as glass and the sun setting on the horizon making everything (the sky, land and water) red and orange and yellow, the turtle sitting in the water would be able to pick out a frog sitting on a log with discrimination that we could never hope to approach.
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PS. Yes, you are trolling and I fell for it. Well I was bored :).
I co-wrote one of the most popular Ray-Tracing programs out there, and learned this from my travels.
Your problem is coming from an effect called Metamerism. This is a phenomenon that causes us to perceive 2 colors as the same when they are not.
The whole problem is caused by our moronic RGB model of light. It's not that simple, in reality. It's like thinking of the audio spectrum as being divided into Treble, Midrange and Bass, and all tones (frequencies) expressed as a "quantity" of bass, treble, and midrange. Stupid, hmm? Well, our RGB model has caused the same stupidity in the optical spectrum.
The visual spectrum is continuous, just like the audio and RF spectrums. A given light source (color) is almost never a single line color on a spectral scale, unless it's a monochromatic laser.
The different spectral peaks for a given light or color sample will be assimilated by your eyes and brain as a given color. There are MANY combinations of spectral peaks that can APPEAR to be the exact same color, yet a measurement system such as HSV or CIE or even RGB will see them as very different. This is called Metamerism.
Even worse, the effect is also compounded by a given color sample looking different under various spectral distributions of illumination (i.e. different colors of light)
For more research on this effect, consult the people that devised the CIELAB scale, Hunter Labs. I learned about this effect in a book written by them! Unfortunately, this book was lent to me by an old associate years ago, and I don't remember the details, like the exact name.
-- You are in a maze of little, twisty passages, all different... --
An older approach is the Munsell system. His system, which he began in 1898 with the creation of his color sphere, or tree, saw its full expression with his publication, A Color Notation, in 1905. It is not mathematically based, but rather each step corresponds to an actual equal perception step.
Even though there are surprisingly large discrepancies between CIE L*a*b* and isotropic observation-based color spaces, such as Munsell, a good bet is to convert your LAB into Munsell and go from there.
You have to make sure your RGB values are in light-linear space (gamma = 1). The default, when you read them from a bitmap or a screen or something, is that they are not (gamma = 2.4 or thereabouts). So if you read colors out of a bitmap and put them through your 3x3 matrix, you won't actually get the right CIEXYZ colors. So then the final step (XYZ to LAB) is pretty meaningless.
So before you convert a pixel to XYZ, do this:
[1] Make sure each component is in the range from 0 to 1 (so if it's from an 8-bit-per-channel image source, divide each channel by 255).
[2] Raise each channel to the power GAMMA, where you define GAMMA to be something like 2.4.
[3] Now push the colors through the 3x3 matrix you came up with (which of course requires you to know what your illuminant and RGB phosphors are like... I use illuminant D65 and the standard phosphor responses and get good results).
I have source code I can send you. jon@bolt-action.com. Also, search on the web for Poynton's "FAQ about Color and Gamma".
-N.
First off, if you just want to make sure the colors are visible on top of each other, you could calculate the luminance of each color using .30*R + .59*G + .11*B and make sure that those numbers differ signifigantly. Some other rules of thumb are here, under color rules.
As far as color discrimability, you might want to look for info on MacAdam's ellipses of just noticeable color differences. There's a picutre on this page which shows the main idea: how different a color has to be in order to notice the difference depends on what color it is. Humans can discern more shades of green than red or blue.
That's the Optical Society of America Uniform Color Space. You can find out more here. Cartesian distance in this space corresponds to perceptual difference, more or less.. ht m
e ci fication.pdf
http://www.colorsystem.com/projekte/engl/49osae
Although the space presented is a bunch of discrete points, there exists formulae to relate the three coordinates (L,j,g) to CIE x,y,z. The corrected formulae are tucked away in this paper on page 18.
http://color.psych.upenn.edu/brainard/papers/sp
The space has the property that perceptual difference roughly corresponds to Cartesian distance between points for differences more than 20x just-noticable.
As a sidenote, I'd like to point out that the color yellow can cause sick people to become more ill. Just as red/orange makes someone hungry, bright yellows make someone sick. it can even induce epileptic shock. True! That's why hospitals are always lavender and purple.
Ok, you mentioned R, G and B, but what about the other one ?
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XFree86 includes support for this colorspace in Xlib.
There are several things that you need to know:
1) People are generally MUCH less sensitive
to differences in BLUE than in RED and
somewhat less sensitive to RED than to GREEN.
2) Gamma correction is poorly implemented across
the web - that results in great differences
in the percieved colours for the brightest
and dimmest R, G or B values. This is hard
to cope with.
3) Don't forget colourblind people! This can
result in people finding it hard to distinguish
various colour values depending on the nature
of their disability.
4) Women see subtle differences between greenish
blues MUCH better than men.
5) The CIE cromaticity diagram includes a bunch
of colours that a CRT cannot reproduce.
6) How distinguishable two colours are depends
critically on the backgrounds against which
they are presented and how close they are to
each other in space and time.
7) In the real world, colours can be pure,
single frequences of light - or complex
chords with many, many frequencies. A CRT
can only display light of three frequencies,
so most pure colours and even most mixtures
of colours can't possibly be accurately
depicted. Fortunately, human eyes can
only *measure* the light intensity at
three basic frequencies - so CRT's appear
to work acceptably. However, the frequencies
of light generated by the phosphors in a CRT
or the LCD's in a flatpanel are not the
same exact frequencies that the human eye
detects. That results in a lot of strange
non-linearities.
8) The colours produced by a particular RGB
triplet will be different on CRT, LCD,
printer ink, etc. That can make a huge
difference in readability.
CONCLUSION:
~~~~~~~~~~~
You have a LOT of research to do!
Try using the color sum, R+B+G for radius and the ratio of colors for angle. The outside would be white, the center black and a mean radius would have R+B+G = 256 and so contain the pure colors. Limits of acceptable difference could be set to accomodate for any crappy old monitor.
If no one else has come up with this rather obvious aproach to digital color, I hearby delcare first art and grant anyone and everyone the right to use this basic IDEA without further consultation. You'll have to do some work to make that idea practical, but the basic idea seems to have been working for designers, graphic artists, archetects and plain old painters for a long time.
DMCA, Hollings, Palladium. What might have sounded like paranoia is now common sense.
...since Psych 101 covered that blue and red make the most psychologically disturbing color combination, despite their difference in colorspace. I think it could have something with them being on completely different ends of the visible spectrum.
This does remind me of the info on mutant tetrachromat females. And also for the different types of normal color blindness. A color-blind friend from work pointed out this page on the types to point out the different effects. And a different co-worker happens to be an extremely rare type, perhaps 'monochromat?'
Anyway, what you use should consider distance in colorspace, and also position in the spectrum, with the effects of the different types of colorblindness taken into consideration also.
One problem might be that color perception depends on the spatial structure of the scene as well as the pure CIELAB coordinates. At least one research group has taken a stab at including spatial information into a model of image quality. Take a look atb .h tml
http://white.stanford.edu/~brian/scielab/sciela
vischeck.com also have an interesting take on simulating color deficiencies (although not the perceptual differences between 'regular' colors). 'Color blind' might be interested in this.
You can try to work these ellipses into your formulae, and people like Parra in France and Parry Moon in Camridge (MA) had tried to distort these color ellipses back into circles in trying to find a
transform of these color spaces into a perceptually uniform color space. But the key thing is that the monitors will differ, the monitors' settings will differ, (the phosphors don't really differ that much between CRTs, but the primaries for CRTs are very different from that of LCDs and that of projectors using LCDs or DLP/DMDs), and most importantly the viewers' photopigments will also differ. Along with the well known ~10% of males that are dichromats, a large number of the population are anomalous trichromats. The actual numbers are still being tallied, and it is still in a very conjectural stage whether most people have multiple copies of the L-opsin gene or multiple copies of the M-opsin gene. The Nathans are on one side of this and the Neitzes are on the other side of this. Which is which, I keep forgetting.
But a quick summary is:
the majority of human subjects are most sensitive to changes in the red (+L-M) or green direction (-L+M), with changes of 0.3% being perceptible for targets of 2-10 degrees in size, mediumly sensitive to changes in intensity (+L+M[+ maybe S]) {there are HUGE arguments and PhD theses brewing over this} for brighter and -L-M[ - maybe S] for darker), and least sensitive to changes that affect the S-cones (+S, kind of a violet change, -S, kind of a greeny-chartreuse change). This, along with the MacAdam Discrimination Ellipse, are for a target color being compared to the immediately adjacent surround color. This does not hold true for nonadjacent and distant targets being compared or for targets subtending visual angles much larger than 5 degrees or much less than 1-2 degrees. There are some funky changes that occur when you get to targets of less than 1/2 degrees in size and when you start to talk about colors that you don't look at foveally or centrally.
That may answer your question. And it may even lead you towards a workable plan on equi-perceptual space. (work with CIE xyz and integrate MacAdams' discrimination data). But realize that MacAdams' work, like much of visual psychophysics is based on less than 5 (count 'em five) subjects. Most visual psychophysics papers today usually have the authors and their post-doc slaves as the only subjects. But actually coming up with that will take data collection on the scale of a PhD thesis, and working within the CIE color space, even from 1931, is probably close enough for most work, even if some of your side-by-side colors are twice as far in color space than they need to be at minimum.
Arrange the primary and secondary colors in a circle. Since you're in the digital realm they would be Red, Green, Blue; and Cyan, Magenta, Yellow. Put Red at the top, then every 60 degrees mark off another color. It should read like this from the top going aroumd clockwise: R, Y, G, C, B, M. These are the hues. Now if you measure the degrees between 2 different hues (the shortest distance), you should have a good indication of their contrast. That is, Red on Cyan is a lot higher contrast than Red on Red-with-a-little-yellow-in-it.
Of course, there're two more variables: Saturation and Value. Imagine in the center of the circle there's a dot of neutral grey, and a gradient from that grey out to the colors. That is, a dot on the edge at 120 deg. would measure as Green at it's highest saturation point, and as you move to the center of the circle, it would get duller and duller until it reached grey. The same for all the other colors. This way you can measure a color's saturation.
Now for the most important aspect: Value. Value is a measure of how light or dark a color is of you took away all the color information (ie, converted it to greyscale). One of the first things you learn in art school is that a difference of Value is higher contrast than a difference of Hue or Saturation. Black on White is the highest contrast you can get, and Red-on-Green and Red-on-Grey fall somewhere in the middle. So you now have to extrude to color model in 3d space so it looks like a cylinder. The top disc should be all white and the bottom disc should be all black. Now you can find out the difference in contrast of two different colors by locating them on the model and measuring their relative distances in 3d space. The tricky part: how tall do you make the model? I'd recommend about twice as high as it is wide. This would mean that White on Black is twice as contrasty as Green on Magenta.
Now here's the really tricky part: the original color wheel you made in the beginning isn't just a flat disc in the center of the cylinder- it's all floppy. The Cyan and Yellow edges should be close to the top, since they're very bright (close to white), and the Red and Blue ends should be nearer to the bottom, since they're darker.
Photoshop does a pretty good job of representing everything except the last paragraph. If you go to the color picker and click on the H toggle button (HSB), you'll see that the rainbow strip represents the circumfrence of the original circle, and the x-axis in the grid represents Saturation while the y-axis represents Value (Brightness). Where it falls short is it says that Cyan at it's highest Saturation is no brighter than Blue (fully saturated)! Of course, it's obvious that Cyan (with a perceptual brightness of (I'd say) around 95, is much brighter than Blue (which I'd guess had a perceptual brightness of 30 or so). But there are good reasons why Adobe chose to do the HSB color measurements this way.
Hope this helps. I don't know how you would program it, but it's good for picturing it in your head.
Josh
c-hack.com |
OK, I have read and reread the original post a few times now and have read most of the highest moderated comments and I still have a question: why? Why are you trying to determine this? Are you designing a website? If so, why do you need a mathematical model? I work closely with a designer with many interactive (read: web) site designs under her belt and I can assure you, she uses no mathematics in making very amazing designs.
So, pray tell - what do you need this for? Especially considering that if you are working on a website, you should really consider limiting your colorspace to the 216 (or so) web-safe colors. It's not so much to support people with 8-bit color (though many such machines still exist) but more to provide a more uniform experience across multiple platforms (read: video cards, monitors, gamma corrections, etc.)
Also, don't forget that a not-for-profit must conform to S.508 accessibility guidelines (you're familiar with that as a not-for-profit web developer, right?).
If you are already converting to CIELAB, try using the L component alone, not the full Delta E. That yields very good results. Probably color-blind people will have less problems with the generated colors, too, since this is about perceived brightness, not hue (but I'm not too sure about this).
I just made a quick Perl hack to test this. It generates 500 pairs of random colors, and outputs them sorted by "distance". It does so converting to LAB and then computing "distance" as abs(L1-L2). Check the output here, mail me if you want the script.
~shiny
WILL HACK FOR $$$
have you?
autopr0n is like, down and stuff.
But while using our RGB monitors and TV the colors would seem flat to him, like RG (without blue) picture would be flat to me. This could be quite inconvenient, however still less than a greyscale pictures to us.
True, but up till just recently, evolution had nothing to do with RGB monitors and TV. And if they do end up having an influence on evolution I am sure that TV influence would not be for the better. Besides, there is a world outside of the RGB gun. Yes?
Seriously though, RGB monitors and the technology that they use is pushing 80 years old. Adaptive technologies and composite individual pixels will make these issues less of a problem.
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This is probably exactly your problem, but even so, I feel your research is far to subjective without adding quite a bit of statistical research (which has already been done, so don't completely reinvent the wheel). The biggest problem is that any mathematical computations about modelling light are done in radiometric space, where light is considered pure energy of varying frequency (Joules, Watts, and other simple(?) units). In reality, we see on a different, completely NONLINEAR scale, known as the photometric scale (Talbots, lumens, etc. (very complex)). Converting between the two involves the use of what are called Spectral Luminous Efficiency Curves. These are the conversion factors that take into account that the eye has widely varying response to different frequencies of light. Not only are they highly non-linear, the only reason they appear to be smooth is that they are a statistical average of many, many different individuals' efficiency curves.
In other words, if your data isn't lining up, the fundamental problem is that the response of individuals' eyes are rarely comparable. Then again, there's all kinds of psycho-visual effects that can screw up this kind of research without even taking this into account.
Sorry you chose to pick such an incredibly complex subject to research, but good luck in your results!
~Loren
~shiny
WILL HACK FOR $$$
Anyway, my solution was to resort to empirical testing, and stash the result in lookup tables. Since I was only interested in the "web safe" color pallette, the number of colors I had to deal with was easily manageable. I wouldn't be suprised if this is the right way of doing it even with a larger color space (you could record data for a coarse mesh and then try and interpolate the results for the colors inbetween your test cases...)
Color spaces are great for development of the displays and printers - there you have to get down to as few basic parameters as possible. They are useless for designing output of printers and displays..
<^>_<(ô ô)>_<^>
From your description, it sounds like you are converting RGB colors into XYZ using only a linear matrix multiplication. This isn't correct - you also need to take gamma into account. If you want to follow a standard, try the sRGB colorspace. Otherwise, it might be good enough to simply raise the raw RGB values to the power of 2.4 or so before the matrix multiplication.
CIELAB is reasonably accurate for evaluating color differences, but research in color spaces that more accurately reflect perception is ongoing - a good recent paper is this one. Also, the Argyll color management system implements most of the color goodies you might want, including CIECAM97 (which is widely considered to be an improvement over CIELab).
It's amazing to me how little (and poorly) color theory is taught, in spite of color being one of the more universal human experiences. My guess is that this is largely to do with the cross-disciplinary nature of color. It's not merely a branch of physics, psychophysiology, pigment chemistry, math, or art, but overlaps all of them.
Try the gamut changes and see if that helps.
LILO boot: linux init=/usr/bin/emacs
You can try to work these ellipses into your formulae, and people like Parra in France and Parry Moon in Camridge (MA) had tried to distort these color ellipses back into circles in trying to find a transform of these color spaces into a perceptually uniform color space. But the key thing is that the monitors will differ, the monitors' settings will differ, (the phosphors don't really differ that much between CRTs, but the primaries for CRTs are very different from that of LCDs and that of projectors using LCDs or DLP/DMDs), and most importantly the viewers' photopigments will also differ. Along with the well known ~10% of males that are dichromats, a large number of the population are anomalous trichromats. The actual numbers are still being tallied, and it is still in a very conjectural stage whether most people have multiple copies of the L-opsin gene or multiple copies of the M-opsin gene. The Nathans are on one side of this and the Neitzes are on the other side of this. Which is which, I keep forgetting.
But a quick summary is:
the majority of human subjects are most sensitive to changes in the red (+L-M) or green direction (-L+M), with changes of 0.3% being perceptible for targets of 2-10 degrees in size, mediumly sensitive to changes in intensity (+L+M[+ maybe S]) {there are HUGE arguments and PhD theses brewing over this} for brighter and -L-M[ - maybe S] for darker), and least sensitive to changes that affect the S-cones (+S, kind of a violet change, -S, kind of a greeny-chartreuse change). This, along with the MacAdam Discrimination Ellipse, are for a target color being compared to the immediately adjacent surround color. This does not hold true for nonadjacent and distant targets being compared or for targets subtending visual angles much larger than 5 degrees or much less than 1-2 degrees. There are some funky changes that occur when you get to targets of less than 1/2 degrees in size and when you start to talk about colors that you don't look at foveally or centrally.
That may answer your question. And it may even lead you towards a workable plan on equi-perceptual space. (work with CIE xyz and integrate MacAdams' discrimination data). But realize that MacAdams' work, like much of visual psychophysics is based on less than 5 (count 'em five) subjects. Most visual psychophysics papers today usually have the authors and their post-doc slaves as the only subjects. But actually coming up with that will take data collection on the scale of a PhD thesis, and working within the CIE color space, even from 1931, is probably close enough for most work, even if some of your side-by-side colors are twice as far in color space than they need to be at minimum.
Even in 1990 when I got my first PC clone, it was yellow text time!
GTRacer
- I need an Apple ][ video cable...
Defending IP by destroying access to it? That makes sense, RIAA/MPAA. Go to the corner until you can play nice!
I wrote an image recognition system for the lumber industry a few years back.
:-)
People (graders) using neon chalk would write on boards (The marks would designate the board quality, and where to cut the bad pieces off.) The boards and chalk would go under a housing with UV light, which had a photosensitive trigger. The trigger would signal the computer, to capture the image. The computer would analyze the image, and send out appropiate bits to a PLC which controlled the saws and sorting.
As you have found out, RGB does *not* uniquely identify colors. We worked around that problem in 2 ways:
1) carefully choosing our chalk color.
2) I then converted the colors over to HSB and used a relative error of Hue to determine if 2 colors were "close enough."
It wasn't perfect, but it was close enough and extremely fast.
I doubt HSB will be sufficient for your domain, but see if you can "change the problem" to make it more computer friendly
It's valid that the issue of true tetrachromacy is in question. Genetically one could be demonstrated to have more than three photopigments, if one could examine the pigments (there are ways to do this noninvasively) they could determine whether or not one had more than three photopigments, but the ultimate proof would lie in both behavioral studies and the examination of the retinal circuitry. (I do the latter for a living).
I used hyperdimensional in the mathematical sense for a reason. In that typically we regard any thing higher than three space as hyperdimensional. For instance, there are organisms that see in five to seven space and one could appreciate that it would be possible to deconvolve their respective individual perception spaces into readily separable and quantifiable dimensions of analysis.
Visit Jonesblog and say hello.
- If Value of both is close to 0, then No - because they are both dark
- If Saturation of both is close to 0, and the difference of both colors' Value is close to 0, then No - because they are both similar shades of grey
- If the 3D distance between the colors is close to 0, then No - they are the same color
Then you just need to tweak the 4 thresholds to get it right. And you might need 3 tweakable weights for computing the 3D distance.