I'm coming from a Direct3D 9 background, and recently switched my custom game engine over to Direct3D12. After some research, it looked like using one of the *_SRGB formats was the way to go, because it corrected the gamma level.
Immediately, I noticed that everything nearly doubled in brightness, which was unexpected. When correcting a curve, I would expect some values to be brighter and some to be darker, but everything just appears brighter. However, I just accepted it and moved on. But now I'm noticing some other strange issues, and I'm not sure what's going on. Maybe someone could help me understand what I'm missing?
When I draw a primitive with a color value in either HLSL or C++, such as color(128,128,128,255) or float4(0.5,0.5,0.5,1.0), the resulting color I see on the screen is actually RGB 188,188,188. Is this to be expected? I'm reading the values of these colors in Adobe Photoshop 2022, which is in SRGB mode. Should the values not match up if both applications are using SRGB?
128 to 188 is really strange, but 0.5 to 0.73 is even stranger. How do I manually construct a color that comes out the way I constructed it? For example, one might use 0.5 to scale by "half brightness", but 0.73 is definitely not half brightness. It's almost white.
If our textures are painted on a PC, such as in Substance Painter or Photoshop, what is the point of converting all of these colors? If the artist can see the same color space that will be used to render, why tell the display to show us something else?
Before I switched to sRGB, I modeled in Blender, and my textures always looked the same between Blender and my game engine. If I start using sRGB, I'm worried that will not be the case. How are artists making that work?
Images that I've seen that were gamma corrected are often brighter and washed out. And images that were not gamma correct are usually dark and rich. Does gamma correction cause some type of saturation loss in darker color?
I appreciate any guidance. I've done research on this topic, but most of the information goes on endlessly about linear color space. Linear is nice, because it makes math easier, but half of the stuff we deal with in a 3D app is non-linear. At this point, I'm not sure its worth it.
1&2: Gamma correction is designed to convert between light intensity as it exists in the real world, i.e. the amount of photons that hit a camera sensor, and how human beings perceive light. So if a light source is emitting 50% less photons, we see it as as around 74% of the light (individual curves may vary) so 128 should become 188.
3&4: The point of linear color is to allow us to process images in a space where an increase in the number of photons is linearly related to the increase in the intensity values. Then the linear colors are gamma corrected before presenting them to the user. When you work in those programs, you are looking at gamma corrected images.
Basically, people don't look at linear color spaces. They look wrong to us. They only exist to allow the computer to do some processing. If you have shaders that do work in linear color space, saving your images in a linear format so that they don't have to remove the gamma, do the processing, and then reapply the gamma can have performance benefits.
The problem may be that you are gamma correcting images that are already gamma corrected. If the images look right to you, they may be gamma corrected, if they look dark, with the lighter areas seemingly emphasized, they may be linear. If you are adding colors/images that look right to you, before gamma is applied, you will have to put the colors/images through inverse gamma correction.
How Applications Display/Convert Color Spaces: (Edit)
Photoshop interprets what the numbers in an image mean through the currently applied color space. It is possible to both "assign" a color space, which changes how photoshop interprets the numbers, and "convert" a color space, which changes the numbers so that they look the same (or as close as possible) when interpreted through the new color space.
This first image is in the sRGB color space. I've painted a gray dot with the values of (127,127,127).
In this second image I have converted the image to a linear color space. It looks almost the same, because photoshop always applies gamma correction so that it looks right to you, but the first dot now has the value (54,54,54). I've added a second dot with the values (127,127,127) in this color space.
In this third image, I have assigned the sRGB color space. Now photoshop thinks the numbers are in the sRGB color space, so it thinks the image already has gamma correction, and is showing us something like the way linear color space looks.
For the final image, I did everything the opposite direction, drawing a dot with a value of (127,127,127), then converting back. The last dot now has a value of (187,187,187)
Related
Anyone who frequently does UI likely knows that for a given color hsl(H, 100%, 50%) (syntax is CSS) not all values of H will produce a color suitable to be placed under arbitrarily black or white text. The specific fact I'm noting is that certain colors (green) appear especially bright and other (blue) appear especially dark.
Well suppose I would like a user to be able to enter a color hue and have the color always appear with a consistent brightness so that one of either white or black text is guaranteed to always be readable on top of it. I would like all colors to also maintain the most vivid level of saturation they can given the constraint on brightness.
Here is a quick example of what I've tried so far. I start with a grid of squared like this rendered using a bunch of html div elements. Essentially these are hue values roughly from 0 to 360 along the horizontal axis and lightness values from roughly 0% to 100% along the vertical axis. All saturation value are set to 100%.
Using a JS library library called chroma.js, I now process all colors using the color.luminance function, whose definition seems to be to do what I'm looking for. I just passed the lightness of the hsl value in as the parameter to the function. I don't know for sure that this is the best way to accomplish my goal though since I'm not familiar with all the terminology at play here. Please note that my choice to use this library is by no means a constraint on how I want to go about this. It just represents my attempt at solving the problem.
The colors certainly now have a more consistent lightness, but the spectrum now seems particularly vivid around the orange to cyan area and particularly dull everywhere else. Also the colors seems to drop very quickly away from black at the top.
Hopefully this example helps a bit to express what I'm trying to accomplish here. Does any know what they best way to go about this is?
I found the solution! Check out HSLuv. It balances out all the hues in the spectrum so that at any given saturation and lightness, all hues will have the exact same perceived brightness to the human eye.
This solved my problem because now I can just set my text color to white (for example) and then as long as the text is readable against a certain HSLuv lightness it is guaranteed that it will be readable against any hue and saturation used in combination with that lightness. Magic.
I'm writing a vertex decimator that needs to interpolate vertex colors on a mesh. I'm reading Level of Detail for 3D Graphics for domain material. In the color interpolation secion, the book goes on to suggest using the CIE-Luv* color space to perform perceptual linear interpolation of colors.
The translation equations to and from the CIE XYZ color space are provided. I am able to implement the equations it provides, but Wikipedia leaves out numeric values of the following variables: u'n, v'n, and Yn.
The article say these values depend on a "specified white point" and its "luminance". It suggests u'n = 0.2009 and v'n = 0.4610 when using 2° observer and standard illuminant C. If I am using these, what would Yn be? I do not know enough physics to figure this out, and I have been unable to search for an answer on Google.
In the end, my question boils down to: What are satisfactory/appropriate values I can use for u'n, v'n, and Yn?
Also, I'm assuming I simply linearly interpolate piecewise each component of CIE-Luv* (L*, u*, and v*) when interpolating values in this color space. Is this correct?
These three values are left out its because they depend on the colorspace of the specific device (e.g. display, printer or camera). Since computer screens use an RGB colorspace where perceived grey are R=B=G, you can assume that the values are not device dependant. I can't remember the values of by heart, so I'll edit them in later.
The human eye perceives luminance/intensity logarithmically, however, a linear interpolation is close enough, especially since you don't know what the actual min and max screen levels are.
The human eye perceives the color angle linearly, however, you need to take into account that the angle id's cyclic, therefore, the interpolation of the min and max angles should equal min (or max) and not the half way point. E.g. average of purple and red should be purple.
I think that the perception of saturation is also logarithmic, however, can be approximated by a linear interpolation.
Edit:
It seems like most sites use the sRGB to XYZ formulas.
http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html
http://www.easyrgb.com/index.php?X=MATH&H=02#text2
http://colormine.org/convert/rgb-to-xyz
What is the basic property of a linear RGB space and what is the fundamental property of a non-linear one? When talking about the values inside each channel in those 8 (or more) bits, what changes?
In OpenGL, colors are 3+1 values, and with this i mean RGB+alpha, with 8 bit reserved to each channel, and this is the part that i get clearly.
But when it comes to gamma correction i don't get what the effect of working in a non-linear RGB space is.
Since i know how to use a curve in a graphic software for photo-editing, my explanation is that in a linear RGB space you take the values as they are, with no manipulation and no math function attached, instead when it's non-linear each channel usually evolves following a classic power function behaviour.
Even if i take this explanation as the real one, i still don't get what a real linear space is, because after computation all non-linear RGB spaces becomes linear and most important of all i don't get the part where a non-linear color space is more suitable for the human eye because in the end all RGB spaces are linear for what i understand.
Let's say you're working with RGB colors: each color is represented with three intensities or brightnesses. You've got to choose between "linear RGB" and "sRGB". For now, we'll simplify things by ignoring the three different intensities, and assume you just have one intensity: that is, you're only dealing with shades of gray.
In a linear color-space, the relationship between the numbers you store and the intensities they represent is linear. Practically, this means that if you double the number, you double the intensity (the lightness of the gray). If you want to add two intensities together (because you're computing an intensity based on the contributions of two light sources, or because you're adding a transparent object on top of an opaque object), you can do this by just adding the two numbers together. If you're doing any kind of 2D blending or 3D shading, or almost any image processing, then you want your intensities in a linear color-space, so you can just add, subtract, multiply, and divide numbers to have the same effect on the intensities. Most color-processing and rendering algorithms only give correct results with linear RGB, unless you add extra weights to everything.
That sounds really easy, but there's a problem. The human eye's sensitivity to light is finer at low intensities than high intensities. That's to say, if you make a list of all the intensities you can distinguish, there are more dark ones than light ones. To put it another way, you can tell dark shades of gray apart better than you can with light shades of gray. In particular, if you're using 8 bits to represent your intensity, and you do this in a linear color-space, you'll end up with too many light shades, and not enough dark shades. You get banding in your dark areas, while in your light areas, you're wasting bits on different shades of near-white that the user can't tell apart.
To avoid this problem, and make the best use of those 8 bits, we tend to use sRGB. The sRGB standard tells you a curve to use, to make your colors non-linear. The curve is shallower at the bottom, so you can have more dark grays, and steeper at the top, so you have fewer light grays. If you double the number, you more than double the intensity. This means that if you add sRGB colors together, you end up with a result that is lighter than it should be. These days, most monitors interpret their input colors as sRGB. So, when you're putting a color on the screen, or storing it in an 8-bit-per-channel texture, store it as sRGB, so you make the best use of those 8 bits.
You'll notice we now have a problem: we want our colors processed in linear space, but stored in sRGB. This means you end up doing sRGB-to-linear conversion on read, and linear-to-sRGB conversion on write. As we've already said that linear 8-bit intensities don't have enough darks, this would cause problems, so there's one more practical rule: don't use 8-bit linear colors if you can avoid it. It's becoming conventional to follow the rule that 8-bit colors are always sRGB, so you do your sRGB-to-linear conversion at the same time as widening your intensity from 8 to 16 bits, or from integer to floating-point; similarly, when you've finished your floating-point processing, you narrow to 8 bits at the same time as converting to sRGB. If you follow these rules, you never have to worry about gamma correction.
When you're reading an sRGB image, and you want linear intensities, apply this formula to each intensity:
float s = read_channel();
float linear;
if (s <= 0.04045) linear = s / 12.92;
else linear = pow((s + 0.055) / 1.055, 2.4);
Going the other way, when you want to write an image as sRGB, apply this formula to each linear intensity:
float linear = do_processing();
float s;
if (linear <= 0.0031308) s = linear * 12.92;
else s = 1.055 * pow(linear, 1.0/2.4) - 0.055; ( Edited: The previous version is -0.55 )
In both cases, the floating-point s value ranges from 0 to 1, so if you're reading 8-bit integers you want to divide by 255 first, and if you're writing 8-bit integers you want to multiply by 255 last, the same way you usually would. That's all you need to know to work with sRGB.
Up to now, I've dealt with one intensity only, but there are cleverer things to do with colors. The human eye can tell different brightnesses apart better than different tints (more technically, it has better luminance resolution than chrominance), so you can make even better use of your 24 bits by storing the brightness separately from the tint. This is what YUV, YCrCb, etc. representations try to do. The Y channel is the overall lightness of the color, and uses more bits (or has more spatial resolution) than the other two channels. This way, you don't (always) need to apply a curve like you do with RGB intensities. YUV is a linear color-space, so if you double the number in the Y channel, you double the lightness of the color, but you can't add or multiply YUV colors together like you can with RGB colors, so it's not used for image processing, only for storage and transmission.
I think that answers your question, so I'll end with a quick historical note. Before sRGB, old CRTs used to have a non-linearity built into them. If you doubled the voltage for a pixel, you would more than double the intensity. How much more was different for each monitor, and this parameter was called the gamma. This behavior was useful because it meant you could get more darks than lights, but it also meant you couldn't tell how bright your colors would be on the user's CRT, unless you calibrated it first. Gamma correction means transforming the colors you start with (probably linear) and transforming them for the gamma of the user's CRT. OpenGL comes from this era, which is why its sRGB behavior is sometimes a little confusing. But GPU vendors now tend to work with the convention I described above: that when you're storing an 8-bit intensity in a texture or framebuffer, it's sRGB, and when you're processing colors, it's linear. For example, an OpenGL ES 3.0, each framebuffer and texture has an "sRGB flag" you can turn on to enable automatic conversion when reading and writing. You don't need to explicitly do sRGB conversion or gamma correction at all.
I am not a "human color detection expert", but I've met similar thing on the YUV->RGB conversion. There are different weights for R/G/B channels, so if you change the source color by x, RGB values change different quantity.
As said, I'm not an expert, anyway, I think, if you want to do some color-correct transformation, you should do it in YUV space, then convert it to RGB (or do the mathematically equivalent operation on RGB, beware of data loss). Also, I'm not sure that YUV is the best native representation of colors, but video cameras provide that format, that's where I've met the issue.
Here is the magic YUV->RGB formula with secret numbers included: http://www.fourcc.org/fccyvrgb.php
I'm trying to create a spectral image with a constant grey-scale value for every row. I've written some fantastically slow code that basically tries 1000 different variation between black and white for a given hue and it finds the one whose grey-scale value most closely approximates the target value, resulting in the following image:
On my laptop screen (HP) there is a very noticeable 'dip' near the blue peak, where blue pixels near the bottom of the image appear much brighter than the neighbouring purple and cyan pixels. On my second screen (Acer, which has far superior colour display) the dip is smaller, but still there.
I use the following function to compute the grey-scale approximation of a colour:
Math.Abs(targetGrey - (0.2989 * R + 0.5870 * G + 0.1140 * B))
when I convert the image to grey-scale using Paint.NET, I get a perfect black to white gradient, so that part of the code at least works.
So, question: Is this purely an artefact of the display qualities of my screens? Or can the above mentioned grey-scale algorithm be improved upon to give a visually more consistent result?
EDIT: The problem seems to be mostly monitor calibration. Not, I repeat not, a problem with the code.
I'm wondering if its more to do with the way our eyes interpret the colors, rather than screen artifacts.
That said... I am using a very-high quality screen (Dell Ultrasharp, IPS) that has incredible color reproduction and I'm not sure what you mean by "dip" in the blue peak. So either I'm just not noticing it, or my screen doesn't show the same picture and it more color-accurate.
The output looks correct given the greyscale conversion you have used (which I believe is the standard one for sRGB colour spaces).
However - there are lots of tradeoffs in colour models and one of these is that you can get results which aren't visually quite what you want. In your case, the fact that there is a very low blue weight means that a greater amount of blue is needed to get any given greyscale value, hence the blue seems to start lower, at least in terms of how the human eye perceives it.
If your objective is to get a visually appealing spectral image, then I'd suggest altering your function to make the R,G,B weights more equal, and see if you like what you get.
The luminence of pixels on a computer screen is not usually linearly related to the digital RGB triplet values of a pixel. The nonlinear response of early CRTs required a compensating nonlinear encoding and we continue to use such encodings today.
Usually we produce images on a computer screen and consume them there as well, so it all works fine. But when we antialias, the nonlinearity — called gamma — means that we can't just add an alpha value of 0.5 to a 50% covered pixel and expect it to look right. An alpha value of 0.5 is only 0.5^2.2=22% as bright as an alpha of 1.0 with a typical gamma of 2.2.
Is there any widely established best practice for antialiasing gamma compensation? Do you have a pet method you use from day to day? Has anyone seen any studies of the results and human perceptions of the quality of the graphic output with different techniques?
I've thought of doing standard X^(1/2.2) compensation but that is pretty computationally intense. Maybe I can make it faster with a 256 entry lookup table, though.
Lookup tables are used quite often for work like that. They're small and fast.
But whether look-up or some formula, if the end result is an image file, and the format permits, it's best to save a color profile or at least the gamma value in the file for later viewing, rather than try adjusting RGB values yourself.
The reason: for typical byte-valued R, G, B channels, you have 256 unique values in each channel at each pixel. That's almost good enough to look good to the human eye (I wish "byte" had been defined as nine bits!) Any kind of math, aside from trivial value inversion, would map many-to-one for some of those values. The output won't have 256 values to pick from for each pixel for R, G, or B, but far fewer. That can lead to contouring, jaggies, color noise and other badness.
Precision issues aside, if any kind of decent quality is desired, all composting, mixing, blending, color correction, fake lens flare addition, chroma-keying and whatever, should be done in linear RGB space, where the values of R, G and B are in proportion to physical light intensity. The image math mimics physical light math. But where ultimate speed is vital, there are ways to cheat.
Jim Blinns - "Dirty Pixels" book outlines a fast and good compositing calculation by using 16 bit math plus lookup tables to accurately go back and forward to linear color space. This guy worked on NASAs visualisations, he knows his stuff.
I'm trying to answer, though mainly for reference now, to the actual questions:
First, there are the recommendations from ITU (http://www.itu.int/rec/T-REC-H.272-200701-I/en) which can be applied to programming (but you have to know your stuff).
In Jim Blinn's "Notation, Notation, Notation", Chapter 9, has a very detailed mathematical and perceptual error analysis, although he only covers compositing (many other graphics tasks are affected too).
The notation he establishes can also be used to derive a way of dealing with gamma, or to check if a given way of doing so is actually correct. Very handy, my pet method (mainly as I discovered it independently but later found his book).
When generating images, one typically works in a linear color space (like linear RGB or one of the CIE color spaces) and then converts to a non-linear RGB space at the end. That conversion can be accelerated in hardware or via lookup tables or even through tricky math. (See the other answers' references.)
When performing an alpha blend (e.g., render this icon onto this background), this kind of precision is often elided in favor of speed. The results are computed directly in the non-linear RGB-space by lerping with the alpha as the parameter. This is not "correct", but it's good enough in most cases. Especially for things like icons on desktops.
If you're trying to do more correct blending, you treat it like an original render. Work in linear space (which may require an initial conversion) and then convert to your non-linear display space at the end.
A lot of graphics nowadays use sRGB as the non-linear display color space. If I recall correctly, sRGB is very similar to a gamma of 2.2, but there are adjustments made to values at the low end.