Difference between revisions of "Krita/Manual/ColorManagement"

You may have heard that Krita has something called color-management. Or maybe you just wondered what all these 'color model' and 'color profile' things you can find in the menus mean. Color management is pretty useful for people who work in digital imaging proffesionaly, and hopefully this page will explain why.

Basic info

If you've never worked with color management before, and have no clue what it is, then know that you've probably been working in the 8bit RGB colour space with the sRGB profile. This means you can choose for sRGB built-in or sRGB-elle-v2-srgbtrc.icc. With the new color space browser this profile is marked with (default) when using 8bit.

We'll go into what these terms mean in the theory, but if you're here only for trying to figure out which is the default, you now know it. Maybe, after reading this, you may feel like changing the default.

What is the problem?

To explain the point of colour management, you'd first need to learn which problem colour management tries to solve.

Let us imagine a kinder garden:

The class of 28 children is subdivided in groups of 7. Each group has their own table.

The teacher gives them a painting assignment: They need to paint a red triangle, a blue square, a green circle and put a yellow border around the three. The kids are very experienced with painting already, so the teacher can confidently leave the smarter ones to their own devices, and spent more time on those who need help.

The following results come from painting:

Even though all groups had the same assignment, each group's result looks different.

Group 1 had vermillion red, citron yellow and ultramarine blue to their disposal. This means their triangle looks nice and red, but their circle's green is muddy. This is because ultramarine is too dark of a blue to create nice greens with.

Group 2 had magenta red, citron yellow and cerulean blue. Magenta is a type of red that is closer to pink, opossed to vermillion, which is closer to orange. However, their green looks nice because cerulean is a much lighter blue.

Group 3 had vermillion red, citron yellow, edmerald green and cerulean blue. They didn't mix their green, and thus ended up with a purer colour.

Finally, group 4 has Vermillion red, citron yellow and cerulean blue. Their colours probably look like what you imagined.

Now, these are kindergarteners, so this isn't the largest problem in the world. However, imagine that something like this happened at a printing company? Imagine four printers printing the same magazine with wildly different results? That would be disastrous!

For this purpose, we invented colour management.

What is colour management?

Colour management is, dryly put, a set of systems that tries to have the same colour translate properly between colour devices.

It usually works by attempting to covert a colour to the reference colour space XYZ. XYZ is a coordinate system that has a spot for all colours that the avarage human eye can see.

From XYZ it can then be translated back into another device space, such as RGB(for screens), or CMYK(for printers).

Krita has two systems dedicated to colour management. On one hand we have lcms2, which deal with Icc-profiles, and on the other we have OCIO, which deal with LUT colour management.

To give a crude estimate, ICC profiles deal with keeping colours consistent over many interpretations of devices(screens, printers) by using a reference space, and LUT deals with manipulating the interpretation of said colours.

Within both we can identify the following color spaces:

Device spaces
Device spaces are those describing your monitor, and have to be made using a little device that is called "colorimeter". This device, in combination with the right software, measures the strongest red, green and blue your screen can produce, as well as the white, black and grey it produces. Using these and several other measurements it creates an icc profile unique to your screen. You set these in Krita's colour management tab.
By default we assume sRGB for screens, but it's very likely that your screen isn't exactly fitting sRGB, especially if you have a high quality screen, where it may be a bigger space instead. Device spaces are also why you should first consult with your printer what profile they expect. Many printing houses have their own device profiles for their printers, or may prefer doing color conversion themselves.
Working spaces
These are delivered alongside Krita for ICC, and downloadable from the OCIO website for OCIO. Working spaces are particularly nice to do colour calculations in, which programs like Krita do often. It's therefore recommended to have a working space profile for your image.
Aesthetic or Look spaces
These are special spaces that have been deformed to give a certain look to an image. Krita doesn't deliver Look profiles for ICC, nor does it yet support Look spaces for OCIO.

Color managed workflow

Knowing this about these spaces of course doesn't give you an idea how to use them, but it does make it easier to explain how to use them. So let us look at a typical color management workflow:

A typical example of a color managed workflow. We have input from scanners and cameras, which we convert to a working space that can be used between different editing software, and is converted to an output space for viewing on screen or printing.

In a traditional color managed workflow, we usually think in terms of real world colors being converted to computer colors and the other way around. So, for example photos from a camera or scanned in images. If you have a device space of such a device, we first assign said device space to the image, and then convert it to a working space.

We then do all our editing in the working space, and use the working space to communicate between editing programs. In Krita's case, due to it having two color management systems, we use ICC profiles between programs like Gimp 2.9+, Inkscape, Digikam and Scribus, and OCIO configuration between Blender and Natron.

You also store your working files in the working space, just like how you have the layers unmerged in the working file, or have it at a very high resolution.

Sometimes, we apply aesthetic or 'look' spaces to an image as part of the editing process. This is rather advanced, and probably not something to worry about in Krita's case.

Then, when we're done editing, we try to convert to an output space, which is another device space. This can be CMYK for printers or a special screen RGB profile. When you are dealing with professional printing houses, it is best to ask them about this step. They have a lot of experience with doing the best conversion, and may prefer to do the conversion from your working space to the device space of their printers.

Another form of output is the way your screen displays the color. Unlike regular output, this one is done all the time during editing: After all, you need to be able to see what you are doing, but your screen is still a device with a device space, so it does distort how the image looks. In this manner, you can see your screen as a set of binoculars you have to look through to see your image at all.

So what does this mean?

When we paint from scratch, we can see our screen profile as the input space, because we use it to determine what colors to pick. This somewhat simplifies the workflow, but makes the screen profile and viewing conditions more important.

Now, photographers and people who do a tricky discipline of VFX called 'color grading' will go completely mad over trying to get the colors they put in to come out 100% correctly, and will even count in factors like the time of day and the color they painted their walls. For example, if the wall behind your computer is pure red, your eyes will adjust to be less sensitive to red, which means that the colors they pick in the program could come out redder. We call these the viewing conditions.

Thankfully, artists have to worry a slight bit less about this. As illustrations are fully handmade, we are able to identify the important bits and make appropriate contrasts between colors. This means that even if our images turn out to be slightly redder than intended, it is less likely the whole image is ruined. If we look back at the kindergarten example above, we still understand what the image was supposed to look like, despite there being different colors on each image. Furthermore, because the colors in illustrations are deliberately picked, we can correct them more easily on a later date.

That said, for artists it is very useful to understand the working spaces. Different working spaces give different results with filters and mixing, and only some working spaces can be used for advanced technology like HDR.

Similarly, Krita, as a program intended to make images from scratch, doesn't really worry about assigning workspaces after having made the image. But because you are using the screen as a binocular to look at your image, and to pick colors, you can see your screen's device space as an input space to the image. Hence why profiling your monitor and giving the profile to Krita in the settings can help with preparing your work for print in the long run.

Overal, it is kinda useful to keep things like viewing conditions in the back of your mind. Many professional artists use a mid-grey color as their default canvas background because they find they create much more dynamic images due to having improved their viewing conditions. It is also why a lot of graphics programs, including Krita, come with a dark theme nowadays. (Though, of course this might also be because dark themes can be considered cool, who knows.)

We go over the type of pitfalls that are specific to artists painting from scratch in the viewing conditions section, but let's first take a look at our color management systems.

Icc profiles

An Icc profile is a set of coordinates describing the extremities of the device space within XYZ, and it is the color management data you use to communicate your working space to printers and applications that are designed for the print industry, such as GIMP, Scribus, Photoshop, Illustrator, Inkscape, Digikam, RawTheraphee, etc. You have two types of icc profiles:

Matrix Shaper profiles.
These are delivered alongside Krita. Matrix shaper profiles are made by setting parameters and interpolating between these to get the exact size of the colour space. Due to this, Krita's color space browser can give you a lot of information on these profiles. Such profiles are also preferable as working space.
cLUT profiles
These are fairly rare, and primarily used to describe printer profiles, such as CMYK. cLUT, or Color Look-up Table profiles store far more data than Matrix shaper profiles, so they can hold data of little particularities caused by, for example, unexpected results from mixing pigments. This is a far more organic approach to describing a color space, and thus terrible to do color maths in.

The interesting thing about icc profiles is that your working space can be larger than your device space. This is generally not bad. However, when converting, you do end up with the question of how to translate the working space values.

Perceptual
This just squishes the values of the working space into the space it's converted to. It's a nice method to see all possible values in this, but not so good if you want accurate colour reproduction. Use this if you want to see all colours in an image, or want to express all possible contrasts. Doesn't work with Matrix Shaper profiles, defaults to relative colorimetric.
Absolute Colorimetric.
The opposite to Perceptual, Absolute colorimetric will attempt to retain all the correct colours at whatever cost, which may result in awful looking colours. Recommended only for reproduction work. Doesn't work with Matrix Shaper profiles in Krita due to ICC v4 workflow standards.
Relative Colorimetric
A in between solution between perceptual and absolute, relative will try to fit whatever colours it can match between colour spaces. It does this by aligning the white and black points. It cuts off the rest to their respective borders. This is what all matrix shaper profiles default to during conversion, because the ICC v4 workflow specifies to only use Relative Colorimetric for matrix shaper profiles.
Saturation
Does anything to retain colourfulness, even hue will be sacrificed. Used in infographics. Doesn't work with Matrix Shaper profiles, defaults to relative colorimetric.

ICC profile version is the last thing to keep in mind when dealing with ICC profiles. Krita delivers both Version 2 and Version 4 profiles, with the later giving better results in doing color maths, but the former being more widely supported(as seen below in 'interoperability with other programs'. This is also why Krita defaults to V2, and we recommend using V2 when you aren't certain if the other programs you are using support V4.

LUT docker and hdr imaging

The LUT Docker is the second important bit of colour management in Krita, which allows you to modify the display filter.

As explained before, we can see our monitor as a telescope or binocular into the world of our picture. Which means it distorts our view of the image a little.

For example, white, on our monitor is full red, full green and full blue. But it's certainly different from white on our paper, or the colour of milk, white from the sun, or even the white of our cell-phone displays.

Black similarly, is brighter on a LCD display than a LED one, and incomparable with the black of a carefully sealed room.

This means that there's potentially blacker blacks than screen black, and white whites than screen white. However, for simplicity's sake we still assign the black-point and the white-point to certain values. From there, we can determine whether a white is whiter than the white point, or a black black than the black-point.

The LUT docker allows us to control this display-filter and modify the distortion. This is useful when we start modifying images that are made with scene referred values, such as HDR photos, or images coming out of a render engine.

So, for example, we can chose to scale whiter-than-screen-white to our screen-white so we can see the contrasts there.

The point of this is that you can take advantage of more lightness detail in an image. While you can't see the difference between screen white and whiter-than-screen-white(because you screen can't show the difference), graphics programs can certainly use it.

A common example is matching the lighting between a 3d model and a real world scene. Others are advanced photo retouching, with much more contrast information available to the user. In painting itself, this allows you to create an image where you can be flippant with the contrast, and allow yourself to go as bright as you'd like.

Like Icc, the LUT Docker allows you to create a profile of sorts for your device. In this case it's the 'lut', which stands for 'Look Up Table'. The look up table is a table of lightness measurements made with a colorimeter. It gives an indication of how bright your screen gets at a given value.

Linear and Gamma corrected colours.

Now, the situation we talked about above is what we would call 'linear'. Each step of brightness is the same value. Our eyes do not perceive linearly. Rather, we find it more easy to distinguish between darker greys than we do between lighter greys.

As humans are the ones using computers, we have made it so that computers will give more room to darker values in the coordinate system of the image. We call this 'gamma-encoding', because it is applying a gamma function to the TRC or transfer function of an image. The TRC in this case being the Tone Response Curve or Tone Reproduction Curve or Transfer function(because color management specialists hate themselves), which tells you computer or printer how much color corresponds to a certain value.

One of the most common issues people have with Krita's color management is the assigning of the right colorspace to the encoded TRC. Above, the center Pepper is the right one, where the encoded and assigned TRC are the same. To the left we have a Pepper encoded in sRGB, but assigned a linear profile, and to the right we have a Pepper encoded with a linear TRC and assigned a sRGB TRC. Image from Pepper & Carrot

The following table shows how there's a lot of space being used by lighter values in a linear space compared to the default sRGB trc of our modern computers and other TRCs available in our delivered profiles:

 Linear TRC sRGB TRC Lab L* TRC Rec 709 TRC Gamma 1.8 TRC Gamma 2.2 TRC

If you look at linear of rec 709 TRCs, you can see there's quite a jump between the darker shades and the lighter shades, while if we look at the Lab L* TRC or the sRGB TRC, which seem more evenly spaced. This is due to our eyes' sensitivity to darker values. This also means that if you do not have enough bit depth, an image in a linear space will look as if it has ugly banding. Hence why, when we make images for viewing on a screen, we always use something like the LAB L*, sRGB or Gamma 2.2 TRCs to encode the image with.

However, this modification to give more space to darker values does lead to wonky color maths when mixing the colors.

We can see this with the following experiment:

Red circle and blue circle over grey, half blurred. In a gamma-corrected environment, this gives an odd black border. In a linear environment, this gives us a nice gradation.

This also counts for Krita's colour smudge brush:

What is happening under the hood

Imagine we want to mix red and green.

First, we would need the color coordinates of red and green inside our colour space's color model. So, that'd be...

Color Red Green Blue
Red 1.0 0.0 0.0
Green 0.0 1.0 0.0

We then avarage these coordinates over three mixes:

Red Mix1 Mix2 Mix3 Green
Red 1.0 0.75 0.5 0.25 0.0
Green 0.0 0.25 0.5 0.75 1.0
Blue 0.0 0.0 0.0 0.0 0.0

But to figure out how these colours look on screen, we first put the indvidual values through the TRC of the color-space we're working with:

Then we fill in the values into the correct spot. Compare these to the values of the mixture table above!

Linear TRC sRGB TRC Lab L* TRC Rec 709 TRC Gamma = 1.8 TRC Gamma = 2.2 TRC
R G B R G B R G B R G B R G B R G B
Red 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0
Mix1 0.75 0.25 0.0 0.52 0.05 0.0 0.48 0.04 0.0 0.56 0.08 0.0 0.60 0.08 0.0 0.53 0.05 0.0
Mix2 0.5 0.5 0.0 0.21 0.21 0.0 0.18 0.18 0.0 0.27 0.27 0.0 0.29 0.29 0.0 0.22 0.22 0.0
Mix3 0.25 0.75 0.0 0.05 0.52 0.0 0.04 0.48 0.0 0.08 0.56 0.0 0.08 0.60 0.0 0.05 0.53 0.0
Green 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0

And this is why colour mixtures are lighter and softer in linear space. Linear space is more physically correct, but sRGB is more efficient in terms of space, so hence why many images have an sRGB TRC encoded into them. In case this still doesn't make sense: sRGB gives largely darker values than linear space for the same coordinates.

So different TRCs give different mixes between colors, in the following example, every set of gradients is in order a mix using linear trc, a mix using srgb trc and a mix using lab L* trc.

So, you might be asking, how do I tick this option? Is it in the settings somewhere? The answer is that we have several icc profiles that can be used for this kind of work:

• scRGB (linear)
• All 'elle'-profiles ending in '1.0', such as sRGB-elle-v2-1.0.icc.

In fact, in all the 'elle'-profiles, the last number indicates the gamma. 1.0 is linear, higher is gamma-corrected and 'srgbtrc' is a special gamma correction for the original sRGB profile.

If you use the color space browser, you can tell the TRC from the 'estimated gamma'(if it's 1.0, it's linear), or from the TRC widget in Krita 3.0, which looks exactly like the curve graphs above.

Even if you do not paint much, but are for example making textures for a videogame or rendering, using a linear space is very beneficial and will speed up the renderer a little, for it won't have to convert images on it's own.

The downside of linear space is of course that white seems very overpowered when mixing with black, because in a linear space, light greys get more room.

Space size

Using Krita's color space browser, you can see that there's many different space sizes.

How do these affect you image, and why would you use them?

The primary reason to use a large space is for two reasons:

1. Even though you can't see the colors, the computer program does understand them and can do color maths with it.
2. For exchanging between programs and devices: most CMYK profiles are a little bigger than our default sRGB in places, while in other places, it's smaller. To get the best conversion, having your image in a space that encompasses both you screen profile as your printer profile.
3. For archival purposes. In other words, maybe monitors of the future will have larger amounts of colors they can show(spoiler: they already do), and this allows you to be prepared for that.

Let's compare the following gradients in different spaces:

On the left we have an artefact-ridden color managed jpeg file with an ACES sRGBtrc v2 profile attached(or not, depending on mediawiki's mood, if not then you can see the exact different between the colors more clearly). This should give an approximation of the actual colors. On the right, we have a sRGB png that was converted in Krita from the base file.

Each of the gradients are gradients from the max of a given channel. As you can see, the mid-tone of the ACES color space is much brighter than the mid-tone of the RGB colorspace, and this is because the primaries are further apart.

What this means for us is that when we start mixing or applying filters, Krita can output values higher than visible, but also generate more correct mixes and gradients. In particular, when color correcting, the bigger space can help with giving more precise information.

If you have a display profile that uses a LUT, then you can use perceptual to give an indication of how your image will look.

Bigger spaces do have the downside they require more precision if you do not want to see banding, so make sure to have at the least 16bit per channel when choosing a bigger space.

Viewing conditions and White Points

We mentioned viewing conditions before, but what does this have to do with 'white points'?

A lot actually, rather, white points describe a type of viewing condition.

So, usually what we mean by viewing conditions is the lighting and decoration of the room that you are viewing the image in. Our eyes try to make sense of both the colors that you are looking at actively(the colors of the image) and the colors you aren't looking at actively(the colors of the room), which means that both sets of colors affect how the image looks.

This is for example, the reason why museum exhibitioners can get really angry at the interior decorators when the walls of the museum are painted bright red or blue, because this will drastically change the way how the painting's colors look. (Which, if we are talking about a painter known for their colors like Vermeer, could result in a really bad experience).

Left: Let's ruin Vermeer by putting a bright purple background that asks for more attention than the famous painting it self. Center: a much more neutral backdrop that an interior decorator would hate but brings out the colors. Right: The approximate color that this painting is displayed against in real life in the Maurits House, at the least, last time I was there. Image from wikipedia commons.

Lighting is the other component of the viewing condition which can have dramatic effects. Lighting in particular affects the way how all colors look. For example, if you were to paint an image of sunflowers and poppies, print that out, and shine a bright yellow light on it, the sunflowers would become indistinguishable from the white background, and the poppies would look gray. This is called metamerism, and it's generally something you want to avoid in your color management pipeline.

Examples where metamerism could become a problem is when you start matching colors from different sources together.

For example, if you are designing a print for a red t-shirt that's not bright red, but not super greyish red either. And you want to make sure the colors of the print match the color of the t-shirt, so you make a dummy background layer that is approximately that red, as correctly as you can observe it, and paint on layers above that dummy layer. When you are done, you hide this dummy layer and sent the image with a transparent background to the press.

But when you get the t-shit from the printer, you notice that all your colors look off, mismatched, and maybe too yellowish (and when did that T-Shirt become purple?).

This is where white points come in.

You probably observed the t-shirt in a white room where there were incandescent lamps shining, because as a true artist, you started your work in the middle of the night, as that is when the best art is made. However, incandescent lamps have a black body temperature of roughly 2300-2800K, which makes them give a yellowish light, officially called White Point A.

Your computer screen on the other hand, has a black body temperature of 6500K, also known as D65. Which is a far more blueish color of light than the lamps you are hanging.

What's worse, Printers print on the basis of using a white point of D50, the color of white paper under direct sunlight.

So, by eye-balling your t-shirt's color during the evening, you took it's red color as transformed by the yellowish light. Had you made your observation in diffuse sunlight of an overcast(which is also roughly D65), or made it in direct sunlight light and painted your picture with a profile set to D50, the color would have been much closer, and thus your design would not be as yellowish.

Applying a white balance filter will sort of match the colors to the tone as in the middle, but you would have had a much better design had you designed against the actual color to begin with

Now, you could technically quickly fix this by using a white balancing filter, like the ones in G'MIC, but because this error is caught at the end of the production process, you basically limited your use of possible colors when you were designing, which is a pity.

Another example where metamerism messes things up is with screen projections.

We have a presentation where we mark one type of item with red, another with yellow and yet another with purple. On a computer the difference between the colors are very obvious.

However, when we start projecting, the lights of the room aren't dimmed, which means that the tone scale of the colors becomes crunched, and yellow becomes near indistinguishable from white. Furthermore, because the light in the room is slightly yellowish, the purple is transformed into red, making it indistinguishable from the red. Meaning that the graphic is difficult to read.

In both cases, you can use Krita's color management a little to help you, but mostly, you just need to be aware of it, as Krita can hardly fix that you are looking at colors at night, or the fact that the presentation hall owner refuses to turn off the lights.

That said, unless you have a display profile that uses LUTs, such as an OCIO lut or a cLUT icc profile, white point won't matter much, due to weirdness in the icc v4 workflow which always converts matrix profiles with relative colorimetric, meaning the white points are matched up.

Bit depth.

Bit depth basically refers to the amount of working memory per pixel you reserve for an image.

Like how having a A2 paper in real life can allow for much more detail in the end drawing, it does take up more of your desk than a simple A4 paper.

However, this does not just refer to the size of the image, but also how much precision you need per colour.

To illustrate this, I'll briefly talk about something that is not even available in Krita:

Indexed Colour.

1bit
Only two colours in total, usually black and white.
4bit(16 colors)
16 colors in total, these are famous as many early games were presented in this color palette.
8bit
256 colors in total. 8bit images are commonly used in games to save on memory for textures and sprites.

However, this is not available in Krita. Krita instead works with channels, and counts how many colours per channel you need. This is called 'real colour'.

Real Colour.

4bit per channel(not supported by Krita)
Also known as Hi-color, or 16bit color total. A bit of an old system, and not used outside of specific displays.
8bit per channel
Also known as "True Color", "Millions of colors" or "24bit/32bit". The standard for many screens, and the lowest bit-depth Krita can handle.
16bit per channel.
One step up from 8bit, 16bit per channel allows for colors that can't be displayed by the screen. However, due to this, you are more likely to have smoother gradients. Sometimes known as "Deep Color".
16bit float
Similar to 16bit, but with more precision. Where 16bit only allows coordinates like [1, 4, 3], 16bit float has coordinates like [0.15, 0.70, 0.3759] . Required for HDR images.
32bit float
similar to 16bit float but with even higher precision. The native color depth of OpenColor IO, and thus faster than 16bit float in HDR images, if not heavier.

This is important if you have a working colour space that is larger than your device space: At the least, if you do not want colour banding.

And while you can attempt to create all your images a 32bit float, this will quickly take up your ram. Therefore, it's important to consider which bit depth you will use for what kind of image.

In summary

Krita has two modes of colour management:

• Icc works in terms of spaces relative to the CIEXYZ space, and requires an icc profile.
• OCIO works in terms of interpretation, and makes use of luts.
• both can be made with a colorimeter.

Krita does a lot of colour maths, often concerning the blending of colours. This colour maths works best in linear colour space, and linear colour space requires a bit depth of at the least 16bit to work correctly. The disadvantage is that linear space can be confusing to work in.

If you like painting, have a decent amount of ram, and are looking to start your baby-steps in taking advantage of Krita's colour management, try upgrading from having all your images in sRGB built-in to sRGB-v2-elle-g10.icc or rec2020-v2-elle-g10.icc at 16bit float. This'll give you better colour blending while opening up the possibility for you to start working in hdr!

Note
Some graphics cards, such as those of the Nvidia-brand actually have the best performance under 16bit float, because Nvidia cards convert to floating point internally. When it does not need to do that, it speeds up!

Note
No amount of color management in the world can make the image on your screen and the image out of the printer have 100% the same color.

Exporting

when you finished you image and are ready to export it, you can modify the color space to optimize it:

If you are preparing an image for the web:

• If you use 16bit color depth or higher, convert the image to 8bit color depth. This will make the image much much more smaller.
• If it's a gray-scale image, convert it to gray-scale.
• If it's a color image, keep it in the working space profile: Many web browsers these days support color profiles embedded into images. Firefox, for example, will try to convert your image to fit the color profile of the other's monitor(if they have one), that way, the image will look near exactly the same on your screen and other profiled monitors.

Note
In some versions of Firefox, the colours actually look strange: This is a bug in Firefox, which is because it's color management system is incomplete, save your png, jpg or tiff without an embedded profile to work around this this.

If you are preparing for print:

• You hopefully made the picture in a working space profile, if not convert it to something like adobe rgb.
• Check with the printer what kind of image they expect. Maybe they expect RGB color space, or perhaps they have their own profile.

Interaction with other applications

Blender

If you wish to use krita's OCIO functionality, and in particular in combination with Blender's color management, you can try to have it use Blender's OCIO config.

Blender's OCIO config is under <Blender-folder>/version number/datafiles/colormanagement. Set the LUT docker to use the OCIO engine, and select the config from the above path. This will give you blender's input and screen spaces, but not the looks, as those aren't supported in Krita yet.

Windows Photo Viewer

You might encounter some issues when using different applications together. One important thing to note is that the standard Windows Photo Viewer application does not handle modern ICC profiles. Krita uses version 4 profiles; Photo Viewer can only handle version 2 profiles. If you export to JPEG with an embedded profile, Photo Viewer will display your image much too dark.

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