Methodology

Many value tools exist, but not a single one solves all problems correctly, and there are certain problems no other tool solves at all. This page is how Value Lab approaches them.

The importance of value

Have you ever heard a great painting must unconditionally have great values, even if the colors are off? We are wired to extract information from lightness, while mostly discarding color.

Here are 2 versions of the same photo, one where only value remains, and the other where only color remains. The difference is so profound that you could credibly state value truly carries a painting.

Reference — The middle photo removes all color, leaving only values. The right photo sets a fixed middle gray while keeping hue and saturation information.

Value only — The middle photo removes all color, leaving only values. The right photo sets a fixed middle gray while keeping hue and saturation information.

Value perception

In true comic fashion, our eye is a terrible light meter.

We actually have no mechanism which tells us the amount of physical light coming off an object. The brain instead computes the ratios between surfaces and throws the absolute number away. Because of this, when we judge value (perceived lightness), the ruler our eye measures with keeps changing on us:

The visual system constantly recalibrates to the average luminance of the scene (light adaptation). .
The brain decides what is “white,” and judges all other values relative to that (anchoring). .
Perceived lightness shifts based on surrounding values (simultaneous contrast). .

Squinting cuts through some of this bias, but is a crude approximation. Dim lighting is another technique which actually works well by switching off our color vision so only value remains, but you can’t paint in the dark.

On top of all this, there’s another problem. Equal jumps on the value scale don’t equate to equal amounts of physical light in the real world. For example, a value that looks 50% gray is only reflecting about 20% of the light.

This problem is especially vexing when it comes to transforming color photos into accurate value studies.

CIELAB

Value Lab is built on CIELAB, a color space designed to correct this exact issue, where equal steps on the scale read as equal steps to our eye. Munsell solved this over a century ago with a book of painted chips you match by eye. CIELAB is the same idea written as a formula, so every color gets an exact number instead of an approximation.

The linear dashed line on this chart represents the naive value guess, 0.25 light = 25% gray, which greatly underestimates the darks. In actuality, 0.25 light is nearly 60% gray, which is what the curve of the CIELAB L* channel corrects for. Darks are given more room on the scale, since a little extra light goes a long way in the shadows but barely registers near white.

CIELAB describes any color with three numbers. L* is lightness, running from 0 (black) to 100 (white), and it closely matches what painters call value. a* and b* are the two color axes: magenta against green, and blue against yellow. When both are zero, the color is a neutral gray.

Color saturation (chroma)

There are two more biases specifically related to color saturation, in addition to the biases above.

Depending on how you’re painting, the source of the values you’re reading and the destination of the values you’re putting down will differ:

painting from life is real life → real life
painting from a lit screen is screen → real life
painting digitally is screen → screen or real life → screen

This concept is important because in all scenarios above you face one saturation-based bias always working against you. Only screen → real life has two. Even if these pitfalls don’t remain entirely in your conscious mind, simply being aware of them can improve your ability to judge value.

The bias that affects all scenarios above happens right at your eye. As colors become more saturated, our brain starts to confuse saturation with value, and we see them lighter than they really are. The reason it applies to every scenario is that light is light; it doesn’t matter if it’s light from a screen or light bouncing off a real life subject. Once it hits your eye, it’s the same light, and it’s biased the same amount. Each hue has a different innate level of bias through its saturation range (Helmholtz–Kohlrausch effect). .

While you can memorize different tricks like “saturated reds and violets are darker than they appear,” the true solution is to build an intuition for the amount of bias of different colors. In other words, you recalibrate your eye by repeated exposure to the correct answer.

This may not come as a surprise, but Value Lab is the perfect tool to do this. In fact, this holds true for all biases mentioned on this page. Don’t panic if it seems complex! Your mind can work all of this out by just intently studying the value relationships in the app, and the rest will fall into place.

The first study method is to load any photo, and flip between Color and Black & White mode repeatedly. Try to observe which colors “surprise you” when you see their true value.

The second method is to leave the app on Color mode, and try to predict a value before using the Value Finder to see the answer. Generally, the more saturated something is, the harder it is to judge.

A demonstration of the Helmholtz–Kohlrausch effect. Moving around the circle changes hue, and moving directly out from the center increases saturation, starting from 0% at the center. This makes the shaded region represent the maximum saturation a color can be for each specific value step. The green shading inside the region is the amount of bias: the brighter the green, the lighter that color looks than it really is. It’s a fairly true generalization that the more saturated a color is, the more it is biased. The exception is yellow, which stays fairly low bias even when saturated. Cyan also doesn’t get very saturated at all. Don’t stress about internalizing this chart. Instead, simply build intuition from the app’s modes and Value Finder to calibrate your eye.

The second pitfall is not in your eye, but in the gap between the value you see and the value you're able to put down. It applies to screen → real life alone, because a lit screen can display a wider variety of saturated colors than what's possible to create with paint. This means a rich color you see with your eye may be physically impossible to put in your painting. Have you ever emailed a design off to a print shop, and the t-shirt comes back much duller and darker?

There's one more very important nuance. It's not just that your screen can show more saturated colors than paint, it's also that for a saturation level your paint can make, that value level will always be darker than on your screen!

This is the difference between additive and subtractive color. Real life objects cannot create light. They start with white light (every color together), absorb part of it, and reflect the rest. What comes back to your eye is the color. The purer the color, the less light comes back. A screen creates its own light rather than filtering it, so it doesn't have paint's limitation, and is able to create full saturation and retain its lightness.

To show you magenta, a screen lights only its red and blue lights and leaves the green ones dark. Your eye adds the two together.

White light hits a layer of quinacridone rose paint. The paint absorbs the green and reflects the red and blue. Pure colors require more filtering of “opposites” in the exact way that allowing a complementary color in makes your mix duller.

Now that you're aware of this relationship between maximum saturation and lightness, let's visualize it. We will first observe what a screen can show.

The secret is that the highest saturation of a color only occurs at a single point on the value scale, and it’s different for each color. Every hue has an intrinsic value, and the more saturated a color gets, the more it is pulled toward that value. On a screen, a saturated blue sits darkest of all hues, then red, then green, with yellow lightest of all. Hue carries its own value!

The most saturation (chroma) each hue can reach at each value level (the L* channel). Notice this chart is very similar to the radial saturation bias chart above, but expressed a different way! The arrows represent the decrease in value when moving to paint (technically each peak drops in value and saturation, but only the value drop is shown for simplicity).

In paint, every intrinsic value sits lower, because it must filter out light to create higher saturation. Green is hit the hardest, darkening by 3-4 value steps. When you see a vibrant green on your screen, it might be an 8, but when you mix it in paint, it must be a 5 due to the pigment’s darker saturation peak. If you ever thought green was hard to paint, now you know why! Yellow and red darken by only 1 step, and blue barely moves.

When a color on your screen cannot exist in paint, you have two choices. Make the color less vibrant and keep the lightness, or keep the vibrancy and go darker. The t-shirt came back duller and darker because the print shop chose both.

Try this image in the app and look at how the yellow and blue hydrant, red stripes in the flag, and green trees appear when converting to black & white. You will notice they line up with the order of the peaks on the chart due to the colors being fairly saturated, but they won't line up perfectly with the peaks because the colors are not fully saturated.

Translation bias

A photo goes through a complex chain of physical translations before it appears on your screen. The image passes through the camera sensor and various encoding mechanisms into a file, which is then interpreted through the file’s color profile and displayed on a screen with its own gamut and calibration.

Every link in this chain answers the question “how bright is this?” in a different way. A naive tool that doesn’t handle all of this correctly teaches your eye the wrong relationships. Luckily, all of this is normalized and accounted for in Value Lab, so you don’t need to think about it!

Two axes of simplification

We can step away from biases now (whew!) and move into some more fun challenges that Value Lab solves. In a standard value study, there are two dimensions of simplification.

Value simplification is simple in theory, but actually has two nuanced components.

The first component is how you subdivide the photo histogram into value groupings. By choosing a small number of buckets (usually 3 or 4), you are saying “all values within this chunk of the input will be treated as a single value,” compressing a large range into a narrow one. This is called value compression.

The second component is which values are chosen to represent those buckets in the output image. This is usually thought of as the “key” of the painting.

Manipulating these two aspects separately is very powerful when figuring out how you want your value study to look.

Value compression is represented by the dashed lines, which currently subdivide the histogram into three buckets. Each bucket does not have to be the same size, and it’s up to the artist to figure that out. Buckets can easily be added or removed as well. The shaded divisions show how much of the full value spectrum each bucket owns. The value assigned to each shaded bucket is arbitrary and determines the key of the output image.

Shape simplification has a unique solution in Value Lab through its Detail Absorption algorithm, which has been designed from the ground up to maintain edge fidelity and truthfully represent the original values. Other tools use naive methods like neighborhood averaging (effectively blurring before bucketing), which erodes shapes and introduces sandwiched value artifacts that were never in the scene.

Naive approach — Two scenes, each reduced to three values. Notice on the lantern that the naive approach creates values that do not exist in the original (the mid-gray rim around the window of the lantern), as well as acid erosion around the sharp angles of the windows. On the boots, contours wander and melt into smears which destroy the beauty of shape design that appears organically in nature.

Modes & adjustments

Modes change the primary direction of the study. Only one mode can be active at once. There is also real value (ahem...) in switching between modes multiple times in a single study as you're painting.

Adjustments are there to apply effects regardless of what mode is active. Multiple adjustments can be active at once.

The processing order is also specific to prevent visual discordance:

exposure → contrast → mode → blur

Every feature and configuration in the app has been meticulously architected to reduce a high-dimensional problem space into an elegant manifold such that the entire value designing process flows surprisingly effortlessly, with no unpleasantness or artifacts due to the tool.

Tools like Photoshop give you every knob under the sun, and as a result, an exponentially less efficient surface area.

Even after using the app for a long time, you can still discover new uses for it. One idea I heard and loved: use Blur when drawing to prevent you from focusing too much on detail. Blur over Values mode in particular produces effects you cannot get from other tools.

Value finder

The Value Finder helps with the “simultaneous contrast” bias from above by allowing you to configure the backdrop. Most value tools just convert to grayscale, but Value Lab shows you the exact number from the value scale as you move around the reference.

Try a few different backdrops with a single color and observe how your ability to judge the value changes. With enough practice, you will be able to do it consistently even as the surrounding values are always changing!

Other goodies

Value Lab is optimized for desktop and mobile web, and a native app for iPhone and Android is coming soon.

Have fun!

Contact me if you ever have any questions, and send me the paintings you create. I would love to see them!