Ed Schlotzhauer

An artists journey

Category: Technology

Ideas about the mechanics, techniques, and technology behind image making.

Out of Gamut
That seems like a strange thing to say. It’s not a phrase you hear in normal conversation. What can it mean? I have written some about how sensors capture color, but I realize I have not mentioned the gnarly problem of color gamut. Unfortunately, I have been bumping into the problem lately, so I had to re-familiarize myself with it. Some of my new work is seriously out of gamut.
What does gamut mean
Most writers avoid this or give overly simplified descriptions. I’m going to treat you as adults, though. If you really are someone who is completely afraid of technology you might want to skip to the end – or ignore the whole subject.
The concept of gamut is really pretty simple, but you need some specialized knowledge and you have to learn some new things about the world.
I have mentioned the CIE-1931 Chromaticity Diagram before. That sounds scary, but you have probably seen the familiar “horseshoe” diagram of colors. I recommend you watch this video to understand how it was derived and what it means. This is the diagram:
CIE-1931 Chromaticity Diagram
After a lot of research and a lot of measurement, scientists determined that this represents all possible colors a typical human can see. Just the hue – color – not the brightness.
Very simply, a gamut is just a representation of what part of this spectrum a particular device can reproduce or capture.
Show me
The next figure shows the horseshoe with some regions overlayed on it.
There are 3 triangular regions labeled: sRGB, Adobe RGB, and ProPhoto RGB. They are called color spaces. The diagram is indicating all possible colors that each color space can represent. The smallest one, sRGB, is typical of a computer monitor. It is what will be used when you share a jpg image with someone. It is small but “safe”. We lose a lot of possible colors, but everyone sees roughly the same thing on all their monitors.
Let’s jump to ProPhoto RGB. You can see that it covers the largest part of the horseshoe. In other words, ProPhoto RGB has the largest gamut. It is the best we have for representing image color and most professional photographers use this now. Unless they are doing weddings. That is a different world.
They’re not ideal?
Unfortunately, these color spaces are an ideal. The ProPhoto color space is a model for editing images. No actual devices or printers can give us the entire ProPhoto RBG gamut. Not even close. Most can barely do sRGB.
Here is a diagram of the color space a Canon pro printer can do.
The small horseshoe, labeled 4, is the printer gamut. It is larger then sRGB (3) and, overall, a lot like AdobeRGB (2). Smaller than ProPhoto RGB, which is not listed here.
It looks pretty good, and in general it is. I use one of these printers. But look at what it does not do. Most greens and extremes of cyan and blue and purple and red and orange and yellow cannot be printed. Actually, almost no extremely saturated colors can be printed.
And it is not just printers. Most monitors, even very good ones, are somewhere between sRGB and AdobeRGB spaces. This cannot really be considered a fault of the monitors or printers. The physics and engineering and cost considerations prohibit them from covering the full ideal range.
Any of these colors that I use in an image, that can’t be created by the device I am using, are referred to as “out of gamut”. Outside of the color space the device can produce. This is what I have been running in to lately.
What happens
So what happens when I try to print an image with out of gamut colors? Well, it is not like it blows up or leaves a hole in the page instead of printing anything. Printers and monitors do the best they can. They “remap” the out of gamut colors to the closest they can do. As artists, we have some control over that process, as we will see in the next section.
But the reality is that these out of gamut colors will lose detail, be washed out and without tonal contrast. When we get to looking at the print, we will say “yech, that is terrible”. Then we need to do something about it.
What can we do about it
There are things to do to mitigate the problem. Here is where we need to understand enough about the technology to know what to do.
First, we have tools to help visualize the problem. Both Lightroom Classic and Photoshop have a Soft Proof view. It will simulate the actual output for a particular printer and paper. You can also view gamut clipping for the monitor. Yes, because of gamut problems you may not be seeing the image’s real color information on your monitor.
Both Lightroom and Photoshop have versions of saturation adjustments and hue adjustment. These can help bring the out of control colors back into a printable or viewable range. With practice we can learn to tweak these settings to balance what is possible with what we want to see.
But even if we give up and decide to print images with out of gamut colors, there are options. the print settings have a great feature called “rendering intent”. They are a way to give guidance to the print engine on how we want it to handle these wild colors. Several different rendering intents are available, but the 2 that are most commonly used are Relative and Perceptual.
Rendering Intents
I use Perceptual intent most often, at least in situations where the are significant out of gamut colors. Using the Perceptual directive signifies to the print driver that I am willing to give up complete tonal accuracy for a result that “looks right”. The driver is free to “squish” the color and tone range in proportional amounts to scale the whole image into a printable range. I don’t do product photography or portraits, so I am usually not fanatical about absolute accuracy. How they work this magic is usually kept as a trade secret. But secret or not, it often does a respectable job of producing a good output.
The other common intent is Relative. This basically prints the data without modification, except that it clips out of gamut colors. That sounds severe, but the reality is that most natural scenes will not have any significant gamut problems, so no clipping will occur.
This is a great intent for most types of scenes, because no tonal compression will take place.
The answer
The answer is “your mileage may vary”. Most images of landscapes and people will not have serious out of gamut problems. When you do, this information may help you get the results you want. When you have a problem, turn on the soft proofing and try the Relative and Perceptual rendering intents. Look at the screen to see if one is acceptable. If not, go back and play with saturation and colors .
Why do I have problems? Well, I’m weird. I have been gravitating to extremely vibrant, highly saturated images. I like the look I am trying to get, but it can be hard to get it onto a print. The image at the top of this article is a slice of an image I am working with now. It is seriously out of gamut. I need to work on it a lot more to be able to print it without loss of color detail. Ah, technical limitations.
July 20, 2022
Is Scaling Bad?
I have written about image sharpness before, but I was challenged by a new viewpoint recently. An author I respect made an assertion that gave me pause. He was describing that when you enlarge film it is an optical scaling but digital enlarging requires modifying the information. Implying that modifying information was bad. So I was wondering, is digital scaling bad?
Edges and detail
Let me get two things out of the way. When we are discussing scaling we only mean upscaling, that is, enlarging an image. Shrinking or reducing an image size is not a problem for either film or digital.
The other thing is that the problems from upscaling mostly are edges or fine detailed areas. An edge is a transition from light to dark or dark to light. The more resolution the medium has to keep the abruptness of the transition, the more it looks sharp to us. Areas with gradual tone transitions, like clouds, can be enlarged a lot with little degradation.
Optical scaling
As Mr. Freeman points out, enlarging prints from film relies on optical scaling. An enlarger (big camera, used backward) projects the negative on to print paper on a platen. Lenses and height extensions are used to enlarge the projected image to the desired size.
This is the classic darkroom process that was used for well over 100 years. It still is used by some. It is well proven.
But is is ideal? The optical zooming process enlarges everything. Edges become stretched and blurred, noise is magnified. It is a near exact magnified image of the original piece of film. Unless it is a contact print of an 8×10 inch or larger negative, it has lost resolution. Walk up close to it and it looks blurry and grainy.
Digital scaling
Digital scaling is generally a very different process. Scaling of digital images is usually an intelligent process that does not just multiply the size of everything. It is based on algorithms that look at the spatial frequency of the information – the amount of edges and detail – and scales to preserve that detail.
For instance, one of the common tools for enlarging images is Photoshop. The Image Size dialog is where this is done. When resample is checked, there are 7 choices of scaling algorithms besides the default “Automatic”. I only use Automatic. From what i can figure out it analyzes the image and decides which of the scaling algorithms is optimal. It works very well.
All of these operations modify the original pixels. That is common when working with digital images and it is desirable. As a matter of fact, it is one of the advantages of digital. A non-destructive workflow should be followed to allow re-editing later.
Scaling is normally done as a last step before printing. The file is customized to the final image size, type of print surface, and printer and paper characteristics. So it is typical to do this on a copy of the edited original. In this way the original file is not modified for a particular print size choice.
Sharpening
In digital imaging, it is hard to talk about scaling without talking about sharpening. They go together. The original digital image you load into Lightroom (or whatever you use) looks pretty dull. All of the captured data is there, but it doesn’t look like what we remembered, or want. It is similar to the need for extensive darkroom work to print black & white negatives.
One of the processes in digital photography in general, and after scaling in particular, is sharpening. There are different kinds and degrees of sharpening and several places in the workflow where it is usually applied. It is too complex a subject to talk about here.
But sharpening deals mainly with the contrast around edges. An edge is a sharp increase in contrast. The algorithms increase the contrast where an edge is detected.
This changes the pixels. It’s not like painting out somebody you don’t want in the frame, but it is a change.
By the way, one of the standard sharpening techniques is called Unsharp Mask. It is mind-bending, because it is a way of sharpening an image by blurring it. Non-intuitive. But the point here is this is digital mimicry of a well known technique used by film printers. So the old film masters used the same type of processing tricks to achieve the results they wanted. They even spotted and retouched their negatives.
Modifying pixels
Let me briefly hit on what I think is the basic stumbling block at the bottom of this. Some people have it in their head that there is something wrong or non-artistic about modifying pixels. That is a straw man. It’s as silly as saying you’re not a good oil painter if you mix your colors, since they are no longer the pure colors that came out of the tubes. I have mentioned before that great prints of film images are often very different from the original frame. Does that make them less than genuine?
Art is about achieving the result you want to present to your viewers. How you get there shouldn’t matter much, and any argument of “purity” is strictly a figment of the objector’s imagination.
One of the great benefits of digital imaging is the incredible malleability of the digital data. It can be processed in ways the film masters could only dream of. We as artists need to use this capability to achieve our vision and bring our creativity to the end product.
I am glad I live in an era of digital imaging. I freely modify pixels in any way that seems appropriate to me.
June 8, 2022
Black & White – in Color
What? Isn’t that contradictory? Isn’t black & white is about the absence of color? I wanted to follow up on a previous article on how we get color information in our digital cameras with a nod to the purity of black and white and emphasize how it is still dependent on color.
Remove the color filter?
I indicated before that our sensors are panchromatic – they respond to the full range of visible light. If we want black & white images, shouldn’t we just take the color filter array off and let each photo site respond to just the grey values?
We could, but most black & white photographers would not be happy with the results. It would be like shooting black & white film. A problem with black and white film is that it eliminates all the information that comes from color. Through interpolation of the Bayer data, we get full data for red, green and blue at each pixel position. If we removed the filter array, we would have only luminosity data. So before even starting, we would be throwing away 2/3 of the data available in our image.
At that point we would have to resort to placing colored filters over the lens, like black & white shooters of old had to do. They did this to “push” the tonal separation in certain directions for the results they wanted. But this filter is global. It affects the whole image rather than being able to do it selectively as we can with digital processing. And it is an irreversible decision we would have to make while we were shooting. Why go backward?
What makes a good b&w image?
Black & white images are a very large and important sub-genre of photography. The styles and results cover a huge range. But I will generalize and say that typically the artists want to achieve a full range of black to white tones in each image with good separation. Think Ansel Adams prints.
Tones refer to the shades of grey in the resulting print. We do a lot of work to selectively control how these tones relate to each other. Typically we want rich black with a little detail preserved in them, bright whites, also containing a little detail, and a full range of distinct tones in between. These mid-range tones give us all the detail and shading.
Tone separation
If one of the goals of black & white photographers is to have high control of the tones, how do we do that? Typically by using the color information. I mentioned putting colored filters over the lens. This was the “way back” solution.
Landscape photographers like Ansel Adams often used a dark red filter to help get the deep toned skies they were known for. Red blocks blue light, forcing all the blue tones toward black.
Digital processing gives us far more control and selectivity than the film photographers had. We don’t have to put the filter over the whole lens and try to envision what the result will be. We can wait and do it on our computer where we have more control, immediate previews, and undo. But all this control would be impossible without having a full color image to work with. As a matter of fact, modern b&w processing starts by working on the color image. Initial tone and range corrections are done in color. Good color makes good b&w.
B&W conversion
Obviously, at some point the color image has to be “mapped” to b&w. This is called b&w conversion. It can be a complicated process. There are many ways to go about the conversion, and each artist has their own favorites. There is no one size fits all.
It is possible to just desaturate the image. This uses a fairly dumb algorithm to just remove the color. It is fast and easy, but it is usually about the worst way to make a good b&w image.
You could use the channels as a source of the conversion. The RGB colors are composed of red, green and blue channels. These can be viewed and manipulated directly in Photoshop. They can often be useful for isolating certain colors to work on. Isolating the red channel would be like putting a strong red filter over the lens.
Lightroom and Photoshop have built in b&w conversion tools. In LIghtroom, choose the Black & White treatment in the Basic panel of the Develop module. This has an interesting optional set of “treatments” to choose from in the grid control right under it. In Photoshop use the B&W adjustment layer.
Both of these have the power of allowing color-selective adjustments. This is huge. Tonal relationships can be controlled to a much greater degree than was possible with film. If we want to just make what were the yellow colors brighter, we can do that. Of course, Photoshop allows using multiple layers with masking to exert even more control.
There are many other techniques, such as channel mixing or gradient maps or plug-ins like Silver Effects to give different and added control. It is actually an embarrassment of riches. This is a great time to be a b&w photographer.
It starts with color
What is common to all of this, though, is that it starts from the color information. Color is key to making most great black & white images.
I sometimes hear a photographer say “that image doesn’t work well in color, convert it to b&w”. Sometimes that works, but I believe it is a bad attitude. B&w is not a means of salvaging mediocre color images. We should select images with a rich spread of tones, great graphic forms, and good color information allowing pleasing tonal separation. Black & white is its own special medium. Remember, though, usually it requires color to work.
March 30, 2022
It’s A Green World
That’s not an environmental statement. As far as our cameras are concerned, green is the “most important” color. I’ll explain why green is foundational to our photography.
Bayer filter
In my previous article I discussed the Bayer Filter and how it allows our digital cameras to reconstruct color. I made a cryptic comment that it was important that there were twice as many green cells as red and blue, but I did not explain. I’ll try to correct that. It is fascinating and highlights some of the brilliance of the Bayer filter design.
“Bryce Bayer’s patent (U.S. Patent No. 3,971,065^[6]) in 1976 called the green photosensors luminance-sensitive elements and the red and blue ones chrominance-sensitive elements. He used twice as many green elements as red or blue to mimic the physiology of the human eye. The luminance perception of the human retina uses M and L cone cells combined, during daylight vision, which are most sensitive to green light. ” This is quoted from Wikipedia. Let me try to unpack it a little.
Color description
There are several ways to describe color. Some, like the HSV or HSB or Lab models, separate the concepts of luminance and chrominance. Luminance is the tonal variation of a scene, the brightness range from black to white. Hue and saturation define the color value and purity.
It is all very complicated and, in reality, only interesting to color scientists. I strongly recommend you view this great video that explains how the CIE-1931 diagram was created and what it means. It answered a lot of my questions. As photographers and artists we have to be familiar with some of it. For instance, we have all seen a color wheel like this:
This is a simplified slice through the HSV space at a constant, maximum lightness. Such a model is useful to us because it shows all colors with their most saturated form at the outer edge and least saturated (white, colorless) in the center.
Our eyes
This is nice, but it is all possible colors, not what we really see. As the quote above about Bayer said, the eye is most sensitive to green. Green is right in the middle of the range of light we are sensitive to, the visible spectrum. Here is a plot of our sensitivity to visible color:
From: https://lightcolourvision.org/wp-content/uploads/09550-0-A-BL-EN-Sensitivity-of-Human-Eye-to-Visible-Light-80.jpg
It is clear to see, just as Mr. Bayer said, we are most sensitive to green. This is why there are twice as many green cells in the Bayer filter as red and blue. The green is used to measure the luminance, the tone range of the image. This information is critical to deriving the image detail plus the color information through a complex set of transformations.
Why is is so important to get a good measure of luminance? Because of another interesting property of the eye. We are more sensitive to luminance than color. Luminance gives detail. Think of a black and white picture you like. That image is pure luminance information, no color information at all. Yet we see all the fantastic detail and subtle tones perfectly.
Color adds a lot of interest to some images, but we can recognize most subjects perfectly well without it. The opposite is not true in general. If you took all the luminance information out of one of your images it is basically unrecognizable.
Example
Here is a quick example of a typical outdoor scene here in the Colorado mountains. This is the original image:
If I convert it to Lab mode and take just the luminance channel (L) we get a black & white version containing all the detail and tone variation that makes it recognizable:
But now if I copy just the color information (the a and b channels) it is … surreal?:
Why green?
I hope I have demonstrated some of the reasoning behind the Bayer filter. It is a key to our ability to capture color information with our cameras.
The human eye really is most sensitive to green. Having half the color filters in the Bayer filter array as green allows maximum ability to construct the luminance data we are so sensitive to. The magic of the sophisticated built in data processing algorithms let the Raw file converters take all this information and derive the luninance and color information we rely on for our images.
Does this mean we should shoot more green subjects? No. I don’t. Many on my images have little discernible green in them. Take the image at the top of this article. I love the colors in this mountain stream. I don’t look at it and think “green”. The color range is very full, though.
As I write this it is the depth of winter here. Much of the shooting I do right now is very monochrome, almost black and white. The Bayer filter is not there to make our images more green. But if you look at your histogram or channels you may be surprised at how much green data is there. Think about it, a black and white image is 33% green.
Thank you Mr. Bayer and all the scientists and engineers who have done such a great job of perfecting our digital sensing over the decades. You are doing an excellent job!
March 9, 2022
How We Get Color Images
Have you ever considered that that great sensor in your camera only sees in black & white? How, then, do we get color images? It turns out that there is some very interesting and complicated Engineering involved behind the scenes. I will try to give an idea of it without getting too technical.
Sensor
I have discussed digital camera sensors before. They are marvelous, unbelievably complicated and sophisticated chips. But they are, still, a passive collector of photons (light) that falls on them.
An individual imaging site is a small area that collects light and turns it into an electrical signal that can be read and stored. The sensor packs an unimaginable number of these sites into a chip. A “full frame” sensor has an imaging area of 24mm x 36mm, approximately 1 inch by 1.5 inch. My sensor divides that area into 47 million image sites, or pixels. It is called “full frame” because that was the historical size of a 35mm film frame.
But, and this is what most of us miss, the sensor is color blind. It receives and records all frequencies in the visible range. In the film days it would be called panchromatic. That is just a fancy word to say it records in black & white all the tones we typically see across all the colors.
This would be awesome if we only shot black & white. Most of us would reject that.
Need to introduce selective color
So to be able to give us color, the sensor needs to be able to selectively respond to the color ranges we perceive. This is typically Red, Green, and Blue, since these are “primary” colors that can be mixed to create the whole range.
Several techniques have been proposed and tried. A commercially successful implementation is Sigma’s Foveon design. It basically stacks three sensor chips on top of each other. The layers are designed so that shorter wavelengths (blue) are absorbed by the top layer, medium wavelengths (green) are absorbed by the middle layer, and long wavelengths (red) are absorbed by the bottom layer. A very cleaver idea, but it is expensive to manufacture and has problems with noise.
Perfect color separation could be achieved using three sensors with a large color filter over each. Unfortunately this requires a very complex and precise arrangement of mirrors or prisms to split the incoming light to the three sensors. In the process, it reduces the amount of light hitting each sensor, causing problems with image capture range and noise. It is also very difficult and expensive to manufacture and requires 3 full size sensors. Since the sensor is usually the most expensive component of a camera, this prices it out of competition.
Other things have been tried, such as a spinning color wheel over the sensor. If the exposure is captured in sync with the wheel rotation then 3 images could be exposed in rapid sequence giving the 3 colors. Obviously this imposes a lot of limitations on photographers, since the rotation speed has to match the shutter speed. A real problem for very long or very short exposures or moving subjects.
Bayer filter
Thankfully, a practical solution was developed by Bryce Bayer of Kodak. It was patented in 1976, but the patent has expired and the design is freely used by almost all camera manufacturers.
The brilliance of this was to enable color sensing with a single sensor by placing a color filter array (CFA) over the sensor to make each pixel site respond to only one color. You may have seen pictures of it. Here is a representation of the design:
Bayer Filter Array, from Richard Butler, DPReview Mar 29, 2017
The gray grid at the bottom represents the sensor. Each cell is a photo site. Directly over the sensor has been placed an array of colored filters. One filter above each photo site. Each filter is either red or green or blue. Note that there are twice as many green filters as either red or blue. This is important.
But wait, we expect that each pixel in our image contains full RGB color information. With this filter array each pixel only sees one color. How does this work?
It works through some brilliant Engineering with a bit of magic sprinkled in. Full color information for each pixel is constructed by interpolating based on the colors of surrounding pixels.
Restore resolution
Some sophisticated calculations have to be done to calculate the color information for each pixel. This makes each pixel end up with full RGB color values. The process is termed “demosaicking” in tech speak.
I promised to keep it simple. Here is a very simple illustration. In the figure below, if we wanted to derive a value of green for the cell in the center, labeled 5, we could average the green values of the surrounding cells. So an estimate of the green value for cell red5 is (green2+green6+green8+green4)/4
From Demosaicking: Color Filter Array Interpolation, IEEE Signal Processing Magazine, January 2005
This is a very oversimplified description. If you want to get in a little deeper here is an article that talks about some of the considerations without getting too mathematical. Or this one is much deeper but has some good information.
The real world is much more messy. Many special cases have to be accounted for. For instance, sharp edges have to be dealt with specially to avoid color fringing problems. Many other considerations such as balancing the colors complicate the algorithms. It is very sophisticated. The algorithms have been tweaked for over 40 years since Mr. Bayer invented the technique. They are generally very good now.
Thank you, Mr. Bayer. It has proven to be a very useful solution to a difficult problem.
All images interpolated
I want to emphasize a point that basically ALL images are interpolated to reconstruct what we see as the simple RGB data for each pixel. And this interpolation is only one step in the very complicated data transformation pipeline that gets applied to our images “behind the scenes”. This should take away the argument of some of the extreme purists who say they will do nothing in post processing to “damage” the original pixels or to “create” new ones. There really are no original pixels.
I understand your point of view. I used to embrace it, to an extent. But get over it. There is no such thing as “pure” data from your sensor, unless maybe you are using a Foveon-based camera. All images are already interpolated to “create” pixel data before you ever get a chance to even view them in your editor. In addition profiles and lens corrections and other transformations are applied,
Digital imaging is an approximation, an interpretation of the scene the camera was pointed at. The technology has improved to the point that this approximation is quite good. Based on what we have learned, though, we should have a more lenient attitude about post processing the data as much as we feel we need to. It is just data. It is not an image until we say it is, and whatever the data is at that point defines the image.
The image
I chose the image at the head of this article to illustrate that the Bayer filter demosaicking and other image processing steps gives us very good results. The image is detailed and with smooth, well defined color variation and good saturation. And this is a 10 year old sensor and technology. Things are even better now. I am happy with our technology and see no reason to not use it to its fullest.
Feedback?
I felt a need to balance the more philosophical, artsy topics I have been publishing with something more grounded in technology. Especially as I have advocated that the craft is as important as the creativity. I am very curious to know if this is useful to you and interesting. Is my description too simplified? Please let me know. If it is useful, please refer your friends to it. I would love to feel that I am doing useful things for people. If you have trouble with the comment section you can email me at ed@schlotzcreate.com.
March 2, 2022