An Expensive and a Free Way of Matching Screen and Print

A PHOTOGRAPHER ASKS: “When I make a print, it looks duller, grayer, and more bland than what I see on my computer monitor, even when I’m using soft proofing. Do you have any tips or advice on making the computer image match my prints?”

The range of colors — or gamut — that can be printed on a typical color printer is less than what can be displayed on a computer monitor, and so Photoshop and some other image editing software packages include a ‘soft proof’ tool which limits the colors on the screen to match the printer gamut. While this is a good way of checking color limits, very often people complain that the prints are much darker that what is seen on the screen. But this is to be expected, yes? You have a nice bright monitor lit by powerful lamps behind the screen while your prints are viewed under whatever dim lighting you might have in the room where you have your computer. Comparing these side-by-side is going to be disappointing.

The soft proofing will only be close to accurate if the brightness of what you see on the screen matches the brightness of the print — where the brightest white on the screen is equal to the brightest possible white you can see on a print. This is usually not the case with many monitors, which even at their dimmest setting is far brighter than typical ambient home of office lighting conditions.

However, it is considered good practice to turn down your monitor brightness enough to allow both comfortable editing and good print matching. This will probably get you 80% of the way towards good soft proofing and it costs you nothing. Any more physical accuracy will increase your costs and decrease your convenience dramatically.

Now if you turn up your lights in the computer’s room — or turn down the monitor brightness by a lot — then you might have so much glare on your screen to make editing it difficult. To correct for this, some folks will put a shield around their computer monitor, black on the inside, preventing much stray light from the room from hitting the screen — this is like a lens hood. You might be able to make one yourself out of cardboard and black spray paint.

This still might not allow a good match in brightness between your monitor and print, because as mentioned it might be quite impractical and undesirable to have the room brightness match your monitor brightness. In this case, imaging professionals will often use a ‘proof light box’, a good-sized enclosure where you can put your print, which has a number of presumably precisely-specified lamps inside of it which can be adjusted for brightness, and so can match the brightness of the monitor.

Using a light box allows for practical monitor brightness settings as well as desirable room brightness. However, this will not work well if the color of the lamps doesn’t match the monitor. The brightest white on the monitor ought to match the color of a pure white object in the light box, not only in brightness but in overall color cast, and so the right lamps need to be selected — but be aware that changing the brightness might very well change the color temperature of the lamps (common with incandescent lamps) —making your selection considerably more difficult.

However, you still may have a problem. A computer monitor has a multitude of red, green, and blue dots of colors, which can be mixed together in fine proportions in order to produce millions of colors of relatively good accuracy. On the contrary, the colors of a print are going to be strongly influenced by the spectral qualities of the lamp used to view it. If you don’t use a specially-made spectrally accurate lamp, the colors will very likely be different — sometimes greatly so — between the monitor and print, even though the tonality of dark and light neutrals might look the same. This problem is called metamerism failure.

This still might not give you an accurate match. The sRGB standard which defines the most common data format used in digital images specifies that images should be viewed surrounded by a dark gray surround — which will cause the eye to perceive shadow tones as being brighter than if they are surrounded by a white background. Photoshop does this normally, and your light box ought to have the same shade of gray surrounding your print. However, if you will eventually view your photo in a frame with a white matte around it, then you might want to edit the image with a brighter surround — and likewise evaluate the print with the same brightness surround. Also, be sure the view the print and computer image at the same size and distance.

Human eyes constantly adjust themselves to the lighting conditions, and so if there is a strong color cast in the room — say from bright, saturated paint on the walls — then your eyes will adjust, neutralizing the color a bit. This adjustment will effect your evaluation of the images, and will change your impression of the print under ambient conditions in the room, more than what you see on a bright monitor. For this reason, unsaturated colors in the computer’s room is desirable, and a medium gray is even more desirable.

Getting a good visual match between the monitor and print is going to be difficult and expensive. But there is an alternative. What I do is measure the brightness of the various parts of the image, using Photoshop’s dropper tool and by analyzing its histogram, and I adjust the values to give me what I know will be good values in the final print. Basically, I know, based on the measured color numbers on the digital image, what the colors will look like in the final print. I know that I don’t want the shadows to be too dark and adjust accordingly. If I need saturated colors, then I’ll use proofing and adjust the image to give me good bright colors without blowing out any of the ink values, which would lead to loss of detail and texture as well as shifts in hue. This ‘by the numbers’ method is accurate and highly predictable, and I really don’t need an accurate visual match on my screen. This is inexpensive but quite accurate, if somewhat difficult to do well. It also has the advantage that I know that my colors are right, even if I’m not seeing right at any given time, like when I’m tired.

Cook Your Own Raw Files, Part 2: Some Notes on the Sensor and Demosaicing

ADMITTEDLY, DIGITAL cameras are somewhat difficult to characterize well, because there is so much variety between models, but there are a few simple measures to help. Likely, most digital camera users are familiar with the term megapixels — perhaps with the vague understanding that more is better, but like many things, there is some sort of trade-off. Unfortunately, a particular megapixel value is usually hard to directly compare to another, simply because there are other factors we have to consider, like the sharpness of lenses used, the size of the sensor itself, any modifications made to the sensor, the processing done on the raw image data by the camera’s embedded computer, and a multitude of other factors. A 12 megapixel camera might very well produce sharper, cleaner images than a 16 megapixel camera.

This is the second article in a series; the first article is here:
Cook Your Own Raw Files, Part 1: Introduction

It is important to know is that there are a fixed number of light-recording sites on a typical digital camera sensor — millions of them — which is why we talk about megapixels. But consider that for many uses, such large numbers of pixels really aren’t needed:



A building in the Soulard neighborhood, in Saint Louis, Missouri, USA.

By modern standards, this is a very small image. It is 500 pixels across by 332 pixels in height; and if we multiply them together, 500 x 332 = 166,000 total pixels. If we divide that by one million, we get a tiny 0.166 megapixels, a mere 1% or so of the total number of pixels that might be found in a contemporary camera. Don’t get me wrong — all the extra megapixels in my camera did find a good use, for when I made this image by downsampling, some of the impression of sharpness of the original image data did eventually find its way into this tiny image — if I only had a 0.166 megapixel camera, the final results would have been softer.

OK, it is very important to know that a digital image has ultimately a fixed pixel size, and if we enlarge it, we don’t get any more real detail out of it, and if we reduce it, we will we lose detail. Many beginners, if they get Photoshop or some other image editing software, will get confused over image sizes and the notorious “pixels per inch” setting, as we see in this Photoshop dialog box:



If you are displaying an image on the Internet, the “Resolution” or “Pixels/Inch” setting is meaningless because the image will display on the monitor at whatever the resolution of the display happens to be set at. Likewise, if you make a print, the Width and Height values you see here are likewise meaningless, for the dimensions of the printed image will be whatever size the image is printed at — and not these values. But — if you multiply the Pixels/Inch value by the Width or Height, you will get the actual pixel dimensions of the image.

The really important value then is the pixel dimensions: my camera delivers 4928 pixels across and 3264 pixels in the vertical dimension. I can resample the image to have larger pixel dimensions, but all those extra pixels will be padding, or interpolated data, and so I won’t see any new real detail. I can resample the image smaller, but I’ll be throwing away detail.

Sensels and Pixels

So, we might assume that my camera’s sensor has 4,928 pixels across and 3,264 pixels vertically — well, it actually has more than that, for a reason we’ll get to later. But in another sense, we can say that the camera has fewer pixels than that, if we define a pixel as having full color information. My camera then does not capture whole pixels, but only a series of partial pixels.

It would be more correct to say that a 16 megapixel camera actually has 16 million sensels, where each sensel captures only a narrow range of light frequencies. In most digital cameras, we have three kinds of sensels, each of which only captures one particular color.

The image sensor of my particular model of Nikon has a vast array of sensels — a quarter of which are only sensitive to red light, another quarter sensitive to blue light, and half sensitive to green light, arrayed in a pattern somewhat like this one:



Since I was unable to find a good photomicrograph of an actual camera sensor, this will have to do for our needs — but this abstract representation doesn’t really show us the complexity of sensors.

Not only is the sensor most sensitive to a bright yellowish-green light (as far as I can tell), it has less sensitivity to blue, and is less sensitive to red: this is on top of the fact that we have as many green sensels as we have red and blue combined. Please be aware that the camera can only deliver these colors (in a variety of brightnesses), and so somehow we are going to have to find some means of combining these colors together in order to get a full gamut of color in our final image.

We have information on only one color at each sensel location. If we want to deliver a full pixel of color at each location, we need to use an interpolation algorithm — a method of estimating the full color at any particular sensel location based on the surrounding sensels. This process of estimating colors is called demosaicing, interpolation, or debayering — converting the mosaic of colors to a continuous image.

Camera raw files are of great interest because they record image data that have not yet been interpolated; you can, after the fact on your computer, use updated software that might include a better interpolation algorithm, and so you can possibly get better images today from the same raw file than what you could have gotten years ago.

Please be aware that the kind of pattern above — called a Bayer filter, after the Eastman Kodak inventor Bryce Bayer (1929–2012) — is not the only one. Fujifilm has experimented with a variety of innovative patterns for its cameras, while the Leica Monochrom has no color pattern at all, since it shoots in black and white only. Also be aware that the colors that any given model of camera captures might be somewhat different from what I show here — even cameras that use the same basic sensor might use a different color filter array and so have different color rendering. Camera manufacturers will use different formulations of a color filter array to adjust color sensitivity, or to allow for good performance in low light.

Sigma cameras with Foveon X3 sensors have no pattern, since they stack three different colors of sensels on top of each other, giving full-color information at each pixel location. While this may seem to be ideal, be aware that this design has its own problems.

A Clarification

I am using the term ‘color’ loosely here. Be aware that many combination of light frequencies can produce what looks like — to the human eye — the same color. For example, a laser might produce a pure yellow light, but that color of yellow might look the same as a combination of red and green light. This is called metamerism, and is a rather difficult problem. There are any number of formulations of color filters that can be used in a digital camera, and we can expect them to have varying absorbance of light — leading to different color renderings. For this reason, it is difficult to get truly accurate colors from a digital camera.

A Perfect Sample Image

For my demonstrations of demosaicing, I’ll be using sections of this image (Copyright © 2001 by Bruce Justin Lindbloom; source http://www.brucelindbloom.com):



Mr. Lindbloom writes:

In the interest of digital imaging research, I am providing a set of four images that represent "perfect" images, that is, they represent a natural scene (as opposed to say, a test pattern or a gradient) which is completely void of any noise, aliasing or other image artifacts. They were taken with a virtual, six mega-pixel camera using a ray tracing program I wrote myself. The intensity of each pixel was computed in double precision floating point and then companded and quantized to 8- or 16-bits per channel at the last possible moment before writing the image file. The four variations represent all combinations of 8- or 16-bits per channel and gamma of 1.0 or 2.2. I believe these images will be useful for research purposes in answering such questions as "How many bits are needed to avoid visual defects?" and "How does one determine the number of bits of real image information, as opposed to digitized noise?" In this sense, they may provide ideal image references against which actual digitized images may be compared by various visual or statistical analysis techniques.

No camera — at the same resolution — will produce images as sharp as this, nor will any camera produce colors as accurate as this image, and no camera image will be without noise. Using perfect images for our purposes has the benefit that it will make defects more visible; real cameras will have less pristine images.

I am using two small crops of this image, 100x150 pixels in size each, which I show here enlarged 500%:





Applying the Mosaic

The sample image has brilliant, saturated colors of impossible clarity. But what happens if we pretend that this image was taken with a camera with a Bayer filter? Here I combine the image crop with a red-green-blue array similar to the one shown above:



It looks rather bad, and recovering something close to the original colors seems to be hopeless. What is worse, we apparently have lost all of the subtle color variations seen in the original image. If we take a closer look at this image:



We see that we have only three colors [technically the red, green, and blue primary colors of the sRGB standard] with only variation in brightness. We seemed to have lost much of the color of our original image.

But this is precisely what happens with a digital camera — all the richness and variety of all the colors of the world get reduced down to only three colors. However, all is not lost — if we intelligently select three distinct primary colors, we can reconstruct all of the colors that lay between them by specifying varying quantities of each primary. This is the foundation of the RGB color system. Please take a look at these articles:

• Color Spaces, Part 1: RGB
• An RGB Quiz

Removing the Matrix

Now we have lost color information because of the Bayer filter, and for most digital cameras we simply have to accept that fact and do the best we can to estimate the color information that has been lost. Since each pixel or rather sensel only delivers one color — and we need three for full color — we can have to estimate the colors by looking at the neighboring sensels and making some assumptions about the image. Red sensels need green and blue color data, green sensels lack red and blue data, and blue sensels need red and green.

A very simple method for doing this is the Nearest Neighbor interpolation algorithm, where we grab adjacent sensel colors and use those to estimate the full color of the image. Here is an illustration of a nearest neighbor algorithm:


Take a look at the three original color channels — in the red, we only have data from the red sensel, and the rest are black — and so we copy that red value to the adjoining sensels. Since we have two different green sensels, here we split the difference between them and copy the resulting average to the red and blue sensels. We end up with a variety of colors when we are finished. Now there are a number of ways we can implement a nearest neighbor algorithm, and these depend on the arrangement of the colors on the sensor, and each one will produce somewhat different results.

Here we apply a nearest neighbor algorithm to our sample images:





OK, it is apparent that we can reproduce areas of uniform color well, giving us back the colors of the original image. However, edges are a mess. Since the algorithm used has no idea that the lines on the second image are supposed to be gray, it gives us color artifacts. Generally, all edges are rough. Also notice that there is a bias towards one color on one side of an object, and a bias towards another color on the other side of the same object — in the samples, the orange patches have green artifacts on its top and left edges, and red artifacts on its bottom and right edges. This bias makes sense since we are only copying color data from one side of each sensel.

We can eliminate this bias if we replace the Nearest Neighbor algorithm with something that is symmetric. A Bilinear algorithm will examine the adjacent colors on all sides of each sensel, getting rid of the bias. Our sample images here are demosaiced with a bilinear algorithm:





OK, a bilinear algorithm eliminates the directional bias of color artifacts, which is good. While the edges are still rough, they do seem a bit softer — which makes sense, since we are taking data from a wider range of sensels, which in effect blurs the image a bit.

Demosaicing algorithms all assume that colors of adjacent pixels are going to be more similar to each other than they are different — and if we cannot make this assumption, then we can’t do demosaicing. Nearest neighbor algorithms assume that all colors in a 2x2 block are basically identical, while the bilinear algorithm assumes that colors change uniformly in a linear fashion. If we sample sensels farther away, we can assume more complicated relationships, such as a power series, and this assumption is built into the bicubic algorithm, which produces smoother results than those illustrated.

More complex algorithms will give us better gradations and smoothness in color, but have the side-effect of softening edges, and so there is research in ways of discovering edges to handle them separately, by forcing colors along an edge to one value or another. Some algorithms are optimized for scanning texts, while others are better for natural scenes taken with a camera. Be aware that noise will change the results also, and so there are some algorithms that are more resistant to noise, but may not produce sharp results with clean images.

As high frame rates are often desired for cameras, complex algorithms for producing JPEGs may not be desired, simply because it will take much longer to process each image — however, this is less of a problem with raw converters on computers, since we can assume that a slight or even long delay is more acceptable.

Notice that our bilinear images have a border around them. Because the bilinear algorithm takes data from all around each sensel, we don’t have complete data for the sensels on the edges, and so there will be a discontinuity on the border of the image. Because of this, cameras may have slightly more sensels than what is actually delivered in a final JPEG — edges are cropped.

We ought not assume that a camera with X megapixels needs to always deliver an image at that size: perhaps it makes sense to deliver a smaller image? For example, we can collapse each 2x2 square of sensels to one pixel, producing an image with half the resolution and one quarter the size, and possibly with fewer color artifacts. Some newer smartphone cameras routinely use this kind of processing to produce superior images from small, noisy high-megapixel sensors. This is a field of active research.

Antialias

Don’t pixel peep! Don’t zoom way into your images to see defects in your lens, focus, or demosaicing algorithms! Be satisfied with your final images on the computer screen, and if you make a large print, don’t stand up close to it looking for defects. Stand back at a comfortable distance, and enjoy your image.

Perhaps this is wise advice. Don’t agonize over tiny details which will never be seen by anyone who isn’t an obsessive photographer. This is especially true when we routinely have cameras with huge megapixel values — and never use all those pixels in ordinary work.

But impressive specifications can help you even if you never use technology to its fullest. If you want a car with good acceleration at legal highway speeds, you will need a car that can go much faster — even if you never drive over the speed limit. If you don’t want a bridge to collapse, build it to accept far more weight than it will ever experience. Most lenses are much sharper when stopped down by one or two stops: and so, if you want a good sharp lens at f/2.8, you will likely need to use an f/1.4 lens. A camera that is tolerable at ISO 6400 will likely be excellent at ISO 1600. If you want exceptionally sharp and clean 10 megapixel images, you might need a camera that has 24 megapixels.

OK, so then let’s consider the rated megapixel count of a camera as a safety factor, or as an engineering design feature intended to deliver excellent results at a lower final resolution. Under typical operating conditions, you won’t use all of your megapixels. As we can probably assume, better demosaicing algorithms might produce softer, but cleaner results, and that is perfectly acceptable. Now perhaps those color fringes around edges are rarely visible — although resampling algorithms might show them in places, and so getting rid of them ought to be at least somewhat important.

My attempts to blur the color defects around edges was not fruitful. In Photoshop, I attempted to use a half dozen methods of chroma blur and noise reduction, and either they didn’t work, or they produced artifacts that were more objectionable than the original flaws. The problem is, once the camera captures the image, the damage is already done: the camera does in fact capture different colors on opposite sides of a sharp edge — a red sensel might be on one side of an edge, and the blue sensel captures light from the other side.

In order to overcome unpleasant digital artifacts, most cameras incorporate antialias filters: they blur the image a bit so that light coming into the camera is split so that it goes to more than one sensel. This process can only be done at the time of image capture, it cannot be duplicated in software after the fact. Since I am working with a perfect synthetic image, I can simulate the effect of an antialias filter before I impose a mosaic.

Here I used box blur, at 75% opacity on the original image data before applying the mosaic: demosaicing leads to a rather clean looking image:






Now I could have used a stronger blurring to get rid of more color, but I think this is perfectly adequate. What little color noise that still remains can be easily cleaned up by doing a very slight amount of chroma noise reduction — but it is hardly needed. If I would have used stronger blurring, the residual noise would have been nearly non-existent, but it would also have made the image softer. Note that we still have the border problem, easily fixed by cropping out those pixels.

It is typically said that the purpose of the antialias filter is to avoid moiré patterns, as illustrated in the article The Problem of Resizing Images. Understand that moiré patterns are a type of aliasing — in any kind of digital sampling of an analog signal, such as sound or light, if the original analog signal isn’t processed well, the resulting digital recording or image may have an unnatural and unpleasant roughness when heard or seen. From my experience, I think that avoiding the generation of digital color noise defects due to the Bayer filter is a stronger or more common reason for using an antialias filter. But of course, both moiré and demosaicing noise are examples of aliasing in general, so solving one problem solves the other.

Producing a good digital representation of an analog signal requires two steps: blurring the original analog signal a bit, followed by oversampling — collecting more detailed data than you might think you actually need. In digital audio processing, the original analog sound is sent through a low-pass filter, eliminating ultrasonic frequencies, and the resulting signal is sampled at frequency more than double the highest frequency that can be heard by young, healthy ears — but this is not wasteful but rather essential for high-quality digital sound reproduction. Likewise, high megapixel cameras — often considered wasteful or overkill — ought to be seen as digital devices which properly implement oversampling so as to get a clean final image.

Anti-antialias

There has been a trend in recent years of DSLR camera makers producing sensors without antialias filters. A common opinion of this practice is that it makes for sharper-looking images. Indeed they are sharper looking — compare our images that implement the nearest neighbor algorithm with those which are blurred at the bottom. But is that apparent sharpness due to aliasing — a defect of processing — and isn’t real detail at all?

So in some respects, getting rid of an antialias filter ought to be considered a mistake. Perhaps Nikon realizes this, offering both the D800, which incorporates an antialias filter, and the D800E, which incorporates another filter which cancels out the effect of the antialias filter. But be aware that there is no way to digitally correct for the loss of the antialias blurring step of the original analog signal. Any attempts, after the fact, to correct for the digital aliasing flaws, will inevitably be worse than if the analog signal was blurred to begin with.

However, practically speaking, it is extremely difficult to get a pixel-level sharp image from a high megapixel camera, especially when the camera is a small image format like DSLRs. Optics usually aren’t all that good and excellent optics are often rare. Also, camera shake, poor focus, narrow depth of field, and diffraction will all blur the light hitting the sensor, giving us the analog pre-blur that is needed to produce clean digital images. In this case, elimination of the antialias filter can actually provide truly sharper images — since we don’t want to further blur light that is already blurry enough to avoid artifacts.

Be aware that construction of an antialias filter is rather problematic and they are not necessarily mathematically perfect for the purpose of pre-blurring the analog light signal for the Bayer filter. We do find a wide variation of anti-alias filters in various camera models, with some having the reputation of being too strong than needed.

Some Further Notes

A quick Web search for “demosaicing algorithms” will bring up many scholarly articles regarding the strengths and weaknesses of the various methods of interpolation, but these are rarely of interest to a photographer.

What a photographer needs to know is that the process does exist, and that most of the time, the method used isn’t too particularly important. The results and artifacts produced by demosaicing only become critical to image quality when the photographer is trying to do something extreme — like making tiny crops of an image or producing huge images, where exceptional optics are used with refined technique: where there is a real need to produce the cleanest possible images. This might also be useful if you are using a low-resolution camera with sharp optics. Otherwise, for casual photography, the details of demosaicing are of little value. Sometimes we simply need to retouch out obvious defects, like blurring the color along particularly noxious edges.

Most raw converter software does not give us any choice in demosaicing algorithm. But some that do include Raw Photo Processor, dcraw, darktable, and RawTherapee. The latter is interesting because you can quickly see the results of changing the algorithm used.

Logically, I am presenting demosaicing as the first step in processing a raw file, but this is not necessarily the best thing to do — I am simply describing it now to get it out of the way. The Dcraw tutorial demonstrates that white balance ought to be done before demosaicing, otherwise additional color artifacts might be generated.

Click here for the previous article in the series:

Cook Your Own Raw Files, Part 1: Introduction

And the next article:

Cook Your Own Raw Files, Part 3: Demosaic Your Images

Brief Advice on Learning Photography

SOMEONE WRITES “I am buying a new camera, but cannot make up my mind…” The correspondent then lists a number of expensive recent-model cameras, commenting on various technical features, and why one might be more suitable than other for his stated purpose of taking photographs of sports, and of wildlife while hiking. He states that he is a beginner.


A camera purchase can be an agonizing experience, especially given the choices available.

When I first got into digital photography, back in the year 2001, I spent hours going over the reviews, and ended up getting a very expensive camera, one which was a top-rated camera back in those days. I wanted a camera mainly for taking nature photos while hiking.

I was extremely disappointed in my photographs, and wasn't able to return the camera for a refund. That disappointment killed my interest in photography for a number of years.

Later, only when I needed to deliver good-quality photos, did I learn about photography, and so I discovered that my ‘bad’ camera was actually pretty good, especially after I learned the basics of composition, white balance, and exposure. Also, I learned to overcome some poor features of the camera with the right post-processing software and techniques.

Be aware that the newest cameras today operate in a similar manner to cameras decades old; and old problems such as the color of light, and the basics of focus, exposure, and shutter speeds haven’t changed. Newer cameras won't do the thinking for you, although they try sometimes. Don't expect that a camera will make your photography good.

If you are a person who tends to get buyer’s remorse, then I would not suggest spending too much money on something, even if reviews and people like me strongly recommend it. However, I would avoid getting something that lots of people criticize. Rather, look for good values.

Until you know what you are doing, you are merely guessing at this time. Don't worry too much about it, we've all gone through it. Here are some suggestions:

• Obtain or borrow an inexpensive camera, maybe one that is used. A super zoom camera might be good, or an older, used DSLR with a good zoom lens. If you are worried about making the right purchase, then spending only a little money on something OK might be better than getting an expensive camera that will be disappointing.
• Go out and shoot lots of photos with it, under a variety of conditions, of various subjects.
• Simultaneously, learn the basic theory of photography: exposure, shutter speeds, aperture, ISO speed, focus, etc. Learn the basics of general visual arts theory: composition, light, color: find good photographs and paintings and study them; find out what makes them good. Learn how to use your camera; don’t try to make lots of adjustments to your camera at first.
• Get feedback from your images. You can post photos on the DPreview forums and other places. It is most important that you post disappointing photos there, and ask why a particular photo might be disappointing. You are likely to get excellent feedback if you present a problematic photo and ask for advice for improvement.
• Take the advice and experiment some more. See if your photos are less disappointing.
• Find a fellow photo hobbyist or club and go out shooting with them, especially one who is more experienced.
• I would not post photos on forums that you think are really good, expecting praise from others. Very often I’ve seen beginner photographers post photos, saying “look how great my photo is,” and they end up being savagely criticized. This is the nature of the modern artistic ego which seeks perfection: a thick skin is needed at times. A measure of humility is needed: rather, post a photo and ask for suggested improvements. Eventually you might be surprised and get lots of compliments.
• Learn the basics of post-processing on the computer. Beginners tend to become enamored of special effects, but instead try to thoroughly learn the basics of levels, contrast, white balance, resizing, cropping, sharpening, etc.
• Once you gain lots of hard-earned knowledge and experience, then you will pretty much know what kind of purchases you will need in the future.

Many beginners go through a phase of loving the photographic process, first placing lots of emphasis on gear, and then later on techniques. That is natural. But always keep your eye on the final purpose of photography: making good photographs.

Sensor Size and the Total Quantity of Light

IT IS SOMETIMES SURPRISING that even inexpensive cameras can take good quality images in the bright mid-day sun. I've seen many photos from cell-phones and from ridiculously cheap point-and-shoot cameras that have more than adequate image quality. Maybe these images aren't particularly optically sharp, but even low-end cameras can produce images in full sunlight that have a low amount of digital noise. They are “good enough.”

But one of the overarching rules of thumb in photography is that the larger the sensor size (or film size), generally speaking, the better the image quality of the final photograph. A bigger sensor makes it easier to have a lower noise image, a bigger sensor makes it easier to make matching quality optics, a bigger sensor makes it easier (up to a point) to make an ergonomic camera, and so forth. Now, I'm not saying that quality photos can’t be taken with a tiny image sensor, rather, it is easier to take an image with higher technical image quality by using a larger sensor. See the article One Easy Rule for Quality Images for more details.



A comparison of camera sensor sizes. [Source and attribution]

As noted, even low-end digital cameras can produce good images in broad daylight. The problem is that their image quality tends to sharply decline as the light gets dimmer. These cameras, taking photos under dim incandescent lighting, produce images that are a noisy mess, with terrible color rendition and digital grain ruining the sharpness of the image. Now, perhaps a tripod could help, but certainly these kinds of cameras are very disappointing for hand-held images.

Our eyesight doesn’t work as we might naïvely think. One scene, which to our eyes appears to be slightly dimmer than another, might in fact have half of the total amount of light falling on it. Likewise, a scene that appears to be only somewhat brighter than another might in reality be twice as bright.  In particular, where I live, in the mid-lattitudes of the northern hemisphere, we get to enjoy long periods of dusk in mid-summer; the fading daylight seems to last for hours, until we finally notice that it is very dark out. Our eyes valiantly attempt to see in the dimming light, until the laws of physics and biology finally conspire against our vision, and we are plunged into darkness. Our eyes attempt to flatten out the huge range of brightness that we experience.

A hazy day may be objectively half as bright as a sunny day, although it certainly seems to be only slightly dimmer. A cloudy day may be one fourth as bright, while an overcast day may be one eighth as bright. At sunset, it may be one sixteenth as bright as a bright sunny day, and a bright day may be thirty two times as bright as what we find at dusk. On ground covered with snow or white sand, a scene may be twice as bright as what we are accustomed to, and there is a real risk of contracting snow blindness due to the excessive amount of light.

Cameras, like eyes, are designed to work over a large range of brightness. Camera lenses have adjustable apertures to vary the amount of light hitting the sensor, and the shutter speed can be varied over a large ranges of values. The sensors also have varying amounts of sensitivity to light. But a sensor with twice the surface area of another collects twice the total amount of light, and we could assume (all things otherwise being equal) that it can operate similarly in light that is half as bright.

Now I've taken decent photos in dark places with a cheap point-and-shoot camera, but that was only when the camera was sitting on a tripod and its shutter was open for a long time. I certainly could not hand-hold the camera and expect to get anything else except digital noise. However, I can and do often take fairly decent hand-held shots at dusk with my Nikon DSLR. The major difference between these two cameras is simply the size of the sensor: the Nikon lets in a far larger total amount of light.



Same scene taken with a newer cell phone camera on top, and an older DSLR camera on the bottom.

We know that cheap point-and-shoot cameras, selling for less than US$50, and having tiny sensors, can take good images in broad daylight with little digital noise. Let us take this quality as our baseline, and determine what size of a sensor we need if we want to take images of similar quality and with similar camera settings under dimmer lighting. This table shows standard digital sensor sizes, along with the lighting conditions that would be equivalent to typical cell phone cameras in bright daylight:

Sensor size	Use	Sensor area in square millimeters	Lighting condition
1/4”	Cell phones and toy digital cameras.	7.68	Bright daylight
1/3.2”	Premium cell phone cameras.	15.5	Hazy sunlight
1/2.3”	Compact digital cameras.	28	Cloudy bright
1/1.7”	Premium compact cameras.	43	Light overcast
2/3”	Some bridge cameras.	58	Heavy overcast
CX or 1”	Nikon 1 series.	116	Sunset
Micro 4/3rds	Olympus and Panasonic mirrorless interchangeable lens cameras.	225	Dusk
APS-C	Most Nikon, Pentax, and Sony DSLRs; lower-end Canon sensors are slightly smaller at 329 square mm. Also found in some premium rangefinder cameras.	370	Indoor sports, stage shows
35mm, “Full frame”	High-end cameras from Nikon, Pentax, Sony, Canon, and Leica.	864	Bright street lighting at night

A camera sensor that has twice the surface area ought to produce an image with a similar amount of digital noise when the lighting is half as bright, all things else being equal. Certainly there are more factors involved, but sensor size is one of the most significant when it comes to image noise.

Photojournalists tend to use the cameras near the bottom of the list, especially if they need to capture a scene in dim lighting without the use of a flash. Note that the Micro 4/3rds cameras are fairly close in sensor size to the APS-C sized cameras, and their discrete size and noiseless operation make them viable for some work under dim lighting. Manufacturers have recently been putting the larger APS-C and 35mm sensors into compact cameras, which many photographers find highly desirable.

For more information, along with some of the data I used to make the table above, see these Wikipedia articles:

• Image sensor format
• Exposure value

Blue Cameras Take the Best Pictures

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1 Bit Depth Cameras

PLEASE EXAMINE these two photos:





Not too much difference.  The second photo has perhaps slightly greater contrast, saturation, and sharpness.

Now both these images came from the same RAW file, but had different processing; I reduced these images to this size using Photoshop's Bicubic algorithm.

Now let's zoom far into a small part of the original images:



The second image has one bit per color channel!  Each of the red, green, and blue color channels is either fully on or off, and so we have only 8 possible colors per pixel versus millions of possible colors in the top image. But when we reduced the image in size, these pixels were averaged, and so appear normal in the final image. By averaging, we can obtain every possible color in the sRGB gamut.

Lousy, horrible image quality, when we pixel-peep at 100% resolution; but it looks just fine at the resolution shown on the screen.

But we really don't have to do pixel averaging to get a plausible image:



The pixels on this image are black and white only — no shades of gray. This image is interesting, but it has poor image quality; but if we had more and smaller pixels, an image like this would appear to the eye to be a continuous grayscale of high quality.

The notorious megapixel wars of a few years ago had new models of cameras coming out with ever higher pixel densities, at the expense of higher noise. When I got my Nikon D40 back in 2008, I specifically chose that model because it had the best balance between noise and pixel density. Since I was going to produce full-resolution images for the book Catholic St. Louis: A Pictorial History, I needed a minimum number of high-quality pixels for full-page images. Now I could have used a higher pixel density camera, but at that time I had problems with image downsizing, which harms sharpness.

The problem with high megapixel, small sensor cameras is noise; but noise is not a problem if you don't look too closely: and clever image manipulation can make this noise invisible or unobjectionable in the final image. Let's not forget that the single best predictor of image quality is sensor size. You can get a decent small image with a small sensor, but a large sensor will deliver high image quality with both small and large images.

What this little experiment tells us is that we don't need high quality pixels to get a high quality final image, if we have enough pixels.

Suppose some manufacturer could cram 250 megapixels into a typical ASP-C sized sensor as found in consumer grade DSLR cameras. If you zoom into your image at 100% resolution, the image might appear to be only noise, but by clever processing you could produce excellent large images with superb detail.

We can learn a few lessons from old film photography. The crystals of silver halide embedded in film change chemically when exposed to light. Please be aware that light, when examined closely, appears to be made up of little discrete chunks called photons. Whenever a number of photons would hit a piece of silver halide, no matter how large or small a crystal, the crystal would change. And so, large crystals are more sensitive to light than smaller crystals, since roughly the same number of photons would likely change either size crystal. The crystals that absorbed photons would then remain in the negative, and would appear black, while the crystals that did not absorb photons would be washed away during developing, and so only the transparent film remain.

So in a certain sense, digital photography is more analog than film photography, and film is more digital than digital sensors.  A film grain is either on or off, with no intermediate steps, no shades of gray. In developed film there is either a grain at any given part of a negative, or there isn't: it is all black or white. Only when we look at the negative from a distance can we perceive an image.

Film grain has many advantages. Because the grains have random shapes, we do not see aliasing in film images — we don't see digital jaggies or interference patterns. By mixing various sizes of grains, we can expand the dynamic range of film: some grains will be more likely than others to be exposed, and so we can get both good shadow and highlight detail, as well as a gradual fall-off in highlights, instead of an abrupt cutoff in highlights as we see with digital cameras (Some Fuji digital cameras had two sizes of sensors to avoid this a bit). Film grain can also add perceived texture as well as sharpness.

Digital photography, following the long example of film technology, could utilize very large numbers of tiny pixel sensors of varying sizes and shapes, and these sensors would register only on or off signals: they would be 1 bit sensors, just like film grain. Although pixel peeping on such an image would be painful, we should expect to obtain superior final images, with much higher dynamic range, good fall-off on highlights, and nice texture and sharpness without digital artifacts. We also would have better control over digital noise, particularly in the shadows.

One interesting advantage to having super high pixel densities is that camera manufacturers could include sensors of various color sensitivities, greatly expanding the color gamut of the sensor, as well as provide far more accurate color rendition. These extra color sensors could emulate the Purkinje effect, and be able to accurately capture an image that corresponds to how humans see in dim lighting, particularly at dusk. Some sensors could be sensitive to infrared, and others to ultraviolet. We have so many pixels in our camera that it wouldn't be much of a waste to assign a certain number of them for odd uses: it would hardly cause a degradation for most images. Various sizes of pixels could be scattered across the sensor, giving better dynamic range; random shapes and sizes could produce images less prone to aliasing, as well as providing more sharpness.

Sensor design would be challenging, for various trade-offs will need to be made: what would be a good balanced design? Blue sensitivity is poor in the standard Bayer design, and red sensors could be boosted in number also. Why can't we also include a number of panchromatic sensors as well, instead of relying mainly on green for luminance? What percentage of the sensor area should be dedicated to big sensors, how many tiny pixels should be included? Can small pixels be superimposed upon big pixels? If varying shapes of pixels are used, what shapes are best; pointy or round? This design offers great potential. Manufacturers could even incorporate Foveon-style sensors, where multiple colors are sensed at the same location. They could even get very clever, by changing the size of the sensors according to depth.

RAW conversion for such a sensor would be difficult — or rather interesting — and would likely require a much faster on-board computer in the camera. But for a hypothetical 250 megapixel camera, we would have precisely one bit per pixel, and so the RAW file size would be only about 31 megabytes in size before compression: not terribly much larger than what we have today. JPEGs produced by this kind of camera would not be full resolution, but then we don't want full resolution, we need to have some sort of averaging of the pixels. But even a lightly processed TIFF file would produce a superior image; up close, we would perceive something that would look like photographic grain, which is much more organic than digital pixels.

This kind of camera would give us unprecedented control over the RAW conversion process. You want an infrared image? You got it. You want ultraviolet? Easy. You want to trade off color accuracy for dynamic range? You want to bring up detail in both the highlights and shadows? A one-bit depth camera can be very useful.

Frequently Asked Questions

HERE ARE A FEW questions photographers often get from the public.

Q: What kind of camera should I get?

What do you want to do with it?

Oh, I don't know. I think that I would like to get into photography. It might be a fun hobby; I've always been creative.

I would suggest getting a cheap one, a camera that has just the basics and manual override. A camera store can help you with this. Get a camera cheap enough so that you won't get buyer's remorse or get too upset if you drop and break it. Also get some inexpensive photo editing software for your computer, as well as an introductory book on photography. Play with the camera and software, and follow the examples in the book to see if you like photography. Maybe after six months you will find that you want to upgrade your camera, or you will find out that you aren't interested in pursuing it further. In any case, your investment is insignificant.

Q: What kind of filter do I need to protect my lens?

None.

You have to very careful with the kind of filter you use, for cheap filters will optically degrade your images, sometimes severely. If you do need one, B+W filters are highly regarded.

But if you spent so much on a lens that you worry about damaging it, then you ought to worry about image quality degradation — otherwise a cheaper lens would suit your purposes. Be careful in handling it, and a rigid lens hood and lens cap is your best protection against damage. I would use a filter only if it is needed creatively (like using polarizers to cut reflections or darken blue skies), or if you are in a salt-air environment.

Q: I want to get my wife the ultimate anniversary present. What is the best camera you can buy?

Might I suggest the Nikon D3x? It is very popular with professionals and takes superb photos. You will need a variety of lenses to go with it. I would suggest getting the 400mm f/2.8G lens for taking photos of wildlife, the 200mm f/2G for indoor sports pictures, the 14-24mm f/2.8G, a wide angle lens, particularly suitable for taking photos indoors, the 135mm f/2.0 DC and the 85mm f/1.4 for taking portraits, the 60mm f/2.8G Micro lens for close-ups, and the 28-300mm zoom lens for general purpose use.

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Q: I have a bunch of 512 MB CF cards. I want to buy a new camera that will use these.

Why do I hear this question so often?

The card you use in a camera is really not that important. Buy whatever card the new camera uses. You will find that the new cards are cheap, and are likely faster and more capacious than your old cards.

Q: How do I get good bokay?

Perhaps you mean bokeh? The word bokeh comes from Japanese, and is used to describe the quality of background blur in an image.

These factors contribute to getting a stronger background blur: getting closer to the subject, opening up the lens aperture, using a longer focal length, and using a camera with a larger sensor. Now the quality of the background blur can be harsh or creamy, and this depends of the lens design itself; some lenses, especially those made for portraiture, will have better bokeh than others, and will be priced accordingly.

Q: My wife has a Nikon D3x camera and 7 lenses that she wants to sell because they are too big and heavy to be practical. Where can I get a good price for used equipment?

You can sell your equipment to Adorama or B&H Photo. These large dealers are likely to give good prices for high-end equipment.

Q: Thinkin about turnin pro. What camera do pro's use? Is $200 too much to charge for a wedding?

Professionals use whatever camera they want to use. And $200 is way too much for you to charge for a wedding.

Q: What DPI setting should I use for my photos?

The dots per inch setting does not matter at all, and it will have no effect whatsoever when you print out your photos or display them on your computer. All that matters is the pixel dimensions of your image. Too few pixels will look terrible if you attempt to make a large print of an image.

DPI does matter when you submit your photos to a commercial publisher. Typically book and magazine publishers will specify a fixed DPI setting: usually 300 dots per inch. When you submit your photo, they will not resize your photo at all, they will merely crop it to fit. For this reason, the publisher will specify both an image size and a DPI size, and your camera must have enough pixels to ensure that your image will cover this size. They will typically specify a size larger than they will use, to allow for bleed off the edge of the paper and to give them flexibility of layout. As a photographer, this is actually very helpful, for you process your image as you want it to look: resizing photos can degrade quality greatly.

Q: What kind of camera do I need to take good indoor pictures of my kids?

Are you having problems?

Yeah, my current camera is slow and my kids are fast. If I do manage to get a photo, either the photo is really grainy, or the kids are blurry, or the flash goes off and the picture doesn't look all that great.

I would suggest getting a low-end DSLR. Most large electronics retailers and camera stores will have fairly inexpensive cameras from Canon, Nikon, and other manufacturers that will solve your problem.

DSLRs in general produce superior results because they have larger sensors than compact cameras. This reduces the problem you see with grain and blur and reduces the need for a flash. Because these cameras have a mirror and separate viewfinder, they are able to incorporate a much faster focusing mechanism than what is found in compacts.

The trade-off is that these cameras are bulkier and heavier, so I would recommend going to the store and trying it out yourself. Usually, people get used to the larger size fairly quickly and the quality improvement is usually worth it.

The lens that comes with your camera is probably adequate. However, you may want to spend about $100 more and get a fast lens, like a 50mm f/1.8. This will let in more light and will allow you to avoid blurring and avoid using the flash in dimmer light.

Q: Why are my interior photos yellow?

That is due to the relatively yellow color of lighting used inside.Your camera has a built-in automatic white balance which attempts to correct for the color of light, but it doesn't always work well. Take at look at my article White Balance, Part 1.

You might try manually setting your camera to incandescent, tungsten, or fluorescent; just remember to set it back to automatic when you are done.

For really good work, you can calibrate the camera to match the lighting precisely. For this you need a target that is guaranteed to be neutral in color. Click here for an inexpensive card.

Q: I want a compact camera with excellent image quality but I don't want a DSLR.

Well, that will be a problem. DSLRs, due to their design, will produce better...

I just told you that I don't want a DSLR! Don't you listen? Or are you just stupid? I want a compact camera that will take as good pictures as a DSLR.

Your options are limited — it could be quite expensive —

Money is no object.

Well, in that case, might I recommend the Leica M9? Like DSLRs, it incorporates superb interchangeable lenses, among the best in the industry, and it has a full-frame sensor like those found in the highest-quality DSLRs. Because of its compact size and silent shutter, it is prized by photojournalists who need to be discrete. And may I suggest an assortment of lenses to go with it...

Um — well — sorry. I'm afraid that is a little beyond my budget. Can you recommend something else?

I think the Sony NEX-5 or the Fuji X-100 would be perfect for you. Not quite as flexible or high performance as a DSLR, but both of these will deliver excellent image quality similar to a DSLR in a compact size. You might also investigate the Micro 4/3rds cameras, which have smaller sensors than DSLRs, but offer much greater quality than typical compacts.

Q: Black and white photography is obsolete. Why don't you get it?

What are you talking about?

Color is where it's at now. Black and white was merely a product of historical forces, ignorance, and technological compromises, and so it has no relevance to us today; black and white photography is something that ought to be abandoned and forgotten.

This sounds familiar. See my article Black and White.

Q: Why do my pictures look washed out?

What do you mean by washed out?

Well, I'm not really sure. But the colors are disappointing. They are pale. The sky is often white instead of blue.

It sounds like your photos are overexposed — that is really common. Take a look at your camera's manual and look up the exposure override function. If your colors look pale, then try adjusting the camera to expose the picture less — -2/3 E.V. would be a good start. You can also try to increase the saturation in the camera, but don't do too much of it. Take a look at my article Three Opportunities for Overexposure.

Unfortunately you now risk underexposing your shots — your pictures could look dark and muddy instead of washed out. See if your camera has a histogram feature: you want to be sure that your histograms don't show too much or too little exposure.

Q: Help! I was told to set my camera to Adobe RGB so that I can capture brighter colors, and now my colors are all muted!

Well, don't do that then.

Most cameras and computers use the sRGB color standard, which can capture about 35% of all colors. Adobe RGB is useful since it can capture 50% of all colors: but if you don't know how to handle managed color workflows on your computer then I wouldn't bother. See my article Imaginary and Impossible Colors.

Q: Should I use RAW or JPEG?

RAW is technically superior, but you are forced to do more work on the computer. Do you want to do no work at all on the computer, and will you do all your work in the camera? Or do you want to spend extra time sitting in front of your computer making your images look right? If you are going to do photo editing anyway, then shooting in RAW is useful since it gives you more flexibility in adjusting white balance and exposure.

Be aware that Adobe Camera RAW, as found in Photoshop and Lightroom, will not produce the same results as the camera's own JPEG. For this reason, I usually use Nikon's View NX2 software to do my RAW conversion before moving the image into Photoshop.

Q: What kind of camera do I need for sports?

If you are shooting sports outdoors in the daytime, an inexpensive point-and-shoot camera with a decent zoom lens should be adequate. If you are shooting sports indoors or at night, you will likely need a DSLR with a fast telephoto lens. This will be quite expensive to do well, and it would be far cheaper to hire someone to do it for you.

Q: What settings do I use to take pictures at night?

I can't tell you that, but your camera and your eyes can tell you. If you are far away from artificial lighting, set your camera's white balance to daylight, otherwise set it to tungsten or incandescent. Put your camera on a tripod. Set the self-timer, and let the camera make whatever exposure it wants. You may find that the camera can't focus, you will have to figure out the camera's manual focus feature. If the camera tells you it is too dark, and you have a manual exposure feature, then use it. Adjust the exposure time until the image on the screen looks good to you. Your exposure could be several minutes long or longer. See the Wikipedia article Exposure Value for hints.

Q: Can you recommend a good lightweight tripod?

I can recommend a good tripod or a light tripod, but not a good light tripod. If you are serious about providing a solid platform for your camera, I would recommend getting a heavy tripod, and a tall one at that. You don't want to lean over all the time to take pictures, and a high camera angle is sometimes superior. A heavy tripod will likely be both sturdier and much more stable than a light one. Having extreme stability is important when taking long exposures, such as at night or in dim interiors.

I've had bad experiences with light tripods. Vibrations from people walking across the floor, a truck rumbling outside, the wind blowing, or even the weight of the camera itself can ruin a photo.

Photoshop Wishlist #1

I AM CURRENTLY evaluating Adobe Photoshop CS5 on my computer, and have 18 days left until the trial copy expires. For the most part, I am delighted by the product, and see many improvements over my old CS3 version. It does not require that much additional computer power — and sometimes it uses even less, since it uses the graphics processor and memory to do tasks once reserved for the main processor.

Photoshop is a venerable, highly developed and nuanced product, and like any complex, actively developed system that's been around for a long time, has many features which see little use nowadays, as well as the refinement to be able to do important things very, very well.

However, a highly developed system may find it difficult to adapt to new conditions, having been optimized for previous conditions. Photoshop has its roots as a raster image processor primarily for graphics arts professionals, and is well-known as a good platform for doing digital art, with its excellent support of many paintbrush-like tools for creating images from scratch. But it is also used in photography, as its name suggests. I am beginning to see some limitations of its photographic capabilities, and one major limit is that images are always strictly bound to an output medium.

For most Photoshop users, this limit means that you edit your images in the sRGB color space, with eight bits per color channel. That isn't too bad, and this is an obvious approach for 90% of all users: after all, that is the standard format used by most cameras and Internet web browsers. Certainly you would want to edit a file in the format which the camera delivers and what your computer can display. Photoshop does things the way it ought to be — right?

I see some problems with this. Each color channel has a maximum value of 255, a minimum value of 0, and we can use only integer steps between: 1, 2, 3, and so forth, with no intermediate values. This lack of precision is of little consequence to most users, and if you do need greater precision — for example, if you are applying severe curves to your image — then certainly you can use 16 bit mode (as I do) to increase the number of possible values. This extra precision helps avoid digital processing artifacts such as banding, and also lets you get better shadow detail.

CS5 has a great improvement over CS3 in that it allows far more operations on 32 bit images, giving us great precision in image manipulation; I haven't tried it yet, but look forward to experimenting with it.

But that isn't good enough. I'd like to see fractional RGB numbers. I want RGB values greater than 255.  I want negative RGB numbers. But this is madness! You cannot display an image with RGB values greater than 255! And what on earth are negative RGB values? Those are clearly impossible, there is no such thing as negative light!

But remember that I stated that in Photoshop images are always bound to a specific output medium, which for most photographer users is probably 8 bit sRGB. While clearly I do eventually want an 8 bit sRGB image, while I work on processing an image, there may be times when my intermediate files will be out of that gamut. And I do process my images mainly in the wide ProPhoto gamut — or in the ultra-wide L*a*b colorspace — with 16 bits per channel to overcome the limits of sRGB, at least temporarily.

Do not think of processing images as a step-by-step process, where each increment produces a superior image.  Sometimes you have to make an image look worse before you can make it look better. I propose making images so bad that they are impossible to print, or even view accurately on your computer monitor — at least temporarily.

For example, when I apply a severe curve to an image, anything that ought to go over 255 is set to 255, and so we lose information and image detail. However, if its value ought to be 300, I want it to be 300, even though it is out of the gamut for the time being.  If I tell Photoshop to make an image twice as bright, I want the entire image to be twice as bright, without worrying about losing highlight detail. I will deal with the gamut when I need to deal with it, which is when I'm preparing the final image for print or web display.

I often add together multiple images to make a final image. What I have to do is apply an opacity to each layer (which is like doing division) to get my final result, but certainly there must be severe rounding errors, and we are losing tremendous amounts of detail in the shadows as a result, which is a bad thing, and especially since digital photography is known for often having terrible shadows. What I would like to do is be able to add together images with impunity. Image addition, which is called Linear Burn in Photoshop, has a maximum value of 255, but if the final value of all this addition ought to be 500, that is what I would like to see.

Generally speaking, I would like to see in Photoshop a pure kind of image algebra, where we can do all sorts of operations on images in a way that follows the standard rules of arithmetic, such as add, subtract, multiply, and divide, as well as other more obscure operations such as exponentials. To do this accurately, we can't have the hard cutoffs of 0 and 255, nor should be we limited to mere integers.

This brings us to negative RGB numbers. These in fact can represent real colors. For example, if you work in a narrow-gamut color space similar to sRGB, and you want to represent a real color outside of its gamut, you can mathematically represent this if you are willing to allow at least one RGB number which is negative or greater than 255. So a negative RGB does not mean negative light, but rather that it is merely an out-of-gamut condition. If we are allowed to use negative numbers — and numbers greater than 255 —  then we will be able to represent all colors while still using a system that is otherwise identical to our narrow-gamut color system. This system will remain relative to a particular gamut, while not being limited to that gamut.

This has many benefits to a careful Photoshop user. If you work in the ProPhoto or Adobe RGB color spaces, and I know many people do, how then do you know that a particular color is out of sRGB gamut? Certainly you can turn on the Gamut Warning feature (I use it all the time), but how can you create a mask for this sort of thing? Can you tell, just by looking at an RGB value, that it is out of gamut? By using large and negative numbers, we can then precisely identify what is out of gamut simply by the numbers: is it greater than 255 or less than 0?

I often attempt to brighten shadows, and try to add lots of local contrast so that dark areas of an image still appear to be dark to the eye, yet in fact are not all that dark, and instead show lots of detail. This is often impossible to do well due to the arithmetical rounding errors found in low RGB values, which is a contributing factor to noise.  Ideally, a numerical representation of RGB would give equal precision to all levels of perceived brightness, but that is not what we currently have, as can be seen in the illustration below:



Most of our current systems of numerically representing color are biased towards midtones, particularly saturated green and magenta tones, while offering a paucity of dark and bright colors. This gives us the risk of banding in our final image.  Having fractional RGB numbers would alleviate this problem greatly, and though we can use 16 bit images, having fractional values would give us a better guarantee of processing shadow values — and highly saturated dark colors — to avoid rounding errors.  I've noticed that 8 bit sRGB in particular handles navy blue rather poorly, which is a pity, for that is my favorite color. We always risk banding when we have large areas of dark blue, as is often found in brilliant deep blue winter skies, especially when using a polarizing filter. We see the same problem with bright yellow colors.

If you take an 8 bit image and convert it to 16 bit, Photoshop multiplies the RGB values so that they fill the new numerical representation. So a value of 255 will be converted to 32768, which is the maximum 16 bit number. In the 32 bit system, which uses floating point numbers, 255 is converted to 1.0, which is the maximum value allowed in that system: all smaller RGB values are some fraction less than 1.

Instead, I propose an alternative method. When you convert an 8 bit image to this new system, all values remain unchanged. The difference is that your values can, after processing, be greater than 255, less than 0, or some fractional number. With this system, you can be very careful, and never allow your image to go out of gamut, or you can edit to your heart's content and worry about gamut later. If you edit an sRGB image in the sRGB color space, your image may want to go out of gamut and you will never know it, except that detail will disappear.

There are a few problems with my system.  First, you can't see the extra colors if you don't have a wide gamut monitor, but we already see this problem when working in the Adobe RGB or L*a*b color space. The other problem comes when we want to convert a high-precision image back down to 8 bits.

The key to working with images in any gamut is to do by-the-numbers processing, and have a thorough understanding of the channel structure of the images. Instead of merely determining if an image looks OK on your screen, you instead measure an image to be sure the colors are right. Calibrating your images is more important than calibrating your monitor.

Converting an image back down to an output format like 8 bit sRGB is more problematic, but take a look at Photoshop's own conversion options from 32 bit images.

However, doing something like this may not work well within the Photoshop product, as it would require a major redesign of many features. However, I do think that it would be quite useful for accurate image processing.

Nikon View NX 2

NIKON RELEASED a new version of their free photo editing software: you can get it here: View NX 2.

In my opinion, this software produces better RAW conversions than does my version of Adobe Camera RAW with Photoshop CS3. I've noticed that it has much better color consistency between differently exposed images of the same subject, which is important for exposure blending — and it produces fewer noisy color artifacts along high-contrast edges. The colors also look much better, and the automatic color aberration removal is a time-saving feature.

But I can't wait to try ACR on Photoshop CS5 — when I can afford it.

A huge problem is that the old View NX is very slow, primarily because it uses lots of memory. The new NX 2 looks much better and has excellent performance. Now I can have all my applications open at one time without my computer grinding to a halt.

I use the D-Lighting feature all the time to boost shadows, but the old version just had a few settings — and the low setting often didn't work at all, or made things worse. The new version has a very smooth slider for continuous adjustment.

UPDATE: There appears to be a bug in the software.  When I make edits to a RAW file and save them, I do an output to TIFF, which then I pull into Photoshop for further editing.  THIS DOES NOT WORK. The TIFF file has the original RAW image, without the edits. Not good.

CORRECTION: The bug I saw is due to me previously editing the image in Adobe Camera RAW. This creates a sidecar file, which apparently confuses ViewNX.  If I delete that sidecar file — it has the file extension .xmp — ViewNX works properly.

One Easy Rule for Quality Images

MANY PHOTO hobbyists, if they take pictures with one of the very many consumer compact cameras, soon ask for advice on how to improve their picture quality.

Admittedly, many photo faults are hard to describe, unless you know what to look for — certainly I know that from experience. But I constantly hear complaints that photos are fuzzy and grainy.

Initially, there seems to be a belief that something is wrong — either with the photographer himself or perhaps the camera isn't working right. Well, yes, maybe. Simple problems like camera shake are easily overcome, and learning how to set the camera properly will improve image quality. But even excellent technique still ultimately gives poor quality results: the images have a certain irreducible amount of fuzz and grain. Our eager beginner realizes that the problem is with the camera, and seeks to upgrade.

Then comes the agonizing part. Should I get Canon, or Nikon, or Sony, or Olympus, or Pentax? Something else? Ought I save my money to get a Leica? Then what lenses should I get?  Is is true that prime lenses are better than zooms? Should I get a 35mm or 50mm lens? Is f/1.4 maximum aperture better than f/1.8? What are the sharpest lenses? How many megapixels should I get? Are off-brand lenses as sharp as manufacturer's brands? Which camera model should I get? Should I buy new or used? Should I wait until the new models come out? What will give me the best image quality?

Some photographers assert that the camera does not matter, that any camera, used properly, will produce good results. The important things, they claim, are composition and subject matter. This is undoubtably true — but the camera acts as an intermediary between artistic vision and the material world as photographed. The medium used has a great effect on how the viewer perceives the final artwork; the medium used can be so jarring as to even overwhelm the purported final purpose of composition and subject. We see this in Lomography, where the photographer intentionally uses poor-quality cameras and ignores camera technique to achieve a very specific low-fidelity look. But does that ‘look’ serve the higher purpose, or is it an end in itself?

(Note: much contemporary emphasis on studying the media itself rather than the content of media, is due to the pioneering studies of Marshall McLuhan; although this study is worthy in itself, in my opinion, we need to re-emphaize content while not discounting the medium. A photographer who spends too much time contemplating the meaning of photography at the expense of higher things may have images that suffer in quality.)

But consider the fact that a single fuzzy, grainy photograph of a dear loved one is of vastly greater importance — to the lover — than a gallery of even the most finely crafted art photographs. As Chesterton wrote, “if a thing is worth doing it is worth doing badly.” But some subjects, out of worthiness and justice, ought to be portrayed in a better way, although a poor image will suffice.

Inexpensive compact digital cameras have a low signal-to-noise ratio, that is, much of the image as delivered consists of artifacts that are not present in the scene photographed. We perceive this noise as fuzziness and grain. We may see a certain blocky quality of the pixels if we view the image close up: this is due to high lossy JPEG compression often found in compact cameras. Also, consider that many low-end cameras include aggressive noise reduction — this invariably results in the destruction of detail.

It is understandable that photographers would want to get rid of this extraneous noise and deliver a clearer final image, a picture that is more faithful to the subject of the photo, a picture that looks sharper and cleaner.

There is basically just one easy rule for quality photography: a bigger sensor means better quality.

Many factors go into making a quality image, but the overwhelming quality advantage goes to the larger sensor. Many problems simply fall away when the sensor size is larger. First and foremost, optical quality becomes less of a concern when the sensor is big: you just don't need that good of a lens when it delivers an image over a larger surface area. Sharpness is a given when you use a large sensor. The problem of packing too many pixels in a small area leads to the problem of noise, and a large sensor with big pixels will have very low noise.

Within the market of inexpensive consumer cameras, we see sensor sizes ranging from less than 2 square millimeters to over 360 square millimeters. We can expect image quality to be roughly proportional to surface area.


Original image source and attribution: en.wikipedia.org/wiki/File:Sensor_sizes_overlaid_inside.svg

Good image quality is easy to find and is low in cost, as long as you are willing to use a larger camera. Any DSLR-class camera will deliver far better image quality than a typical compact camera, since they all have much larger sensors, as well as inherently better lenses and other incidentals.

Well, some photographers insist that they want to improve their image quality, but only in a compact camera. That is tough. There are some new cameras that do offer larger sensors in a compact case — but these are often at extreme cost. Good quality can be found in slightly larger, and much more economical cameras. You just have to be humble and accept the compromise of having a slightly larger camera.

By sensor I also mean film. Chemical photographic technology is nearly two centuries old, and is very highly refined. The image quality that can be delivered by a used 35mm film camera is quite impressive, and there is no comparison when you use medium or large format film. Film cameras are inexpensive, but are less convenient than digital. Large format cameras are extremely inconvenient, but also deliver unmatched results.

Film cameras are inconvenient, but can deliver superior results. We can likewise capture extreme quality images with even cheap digital cameras if are willing to be inconvenienced.  By taking multiple overlapping photos of an object — but only a static object, unfortunately — we can simulate an arbitrarily large camera sensor by fusing the images together on the computer.

Fail

BEWARE! Memory cards for digital cameras can and do fail. To avoid problems, consider this:

• Upload your photos to a computer every day, and more often if you are doing something critical.
• If you are doing product shoots, like I did this past weekend, consider shooting tethered. This automatically sends the photos to the computer, either over a cable or via wireless transmitter.
• Copy the photos to another hard drive, just to be safe. And copy them to DVD.
• Occasionally reformat the memory card, using the format function found in the camera; this ensures a clean file structure on the card. This erases the photos on the card!
• Wedding photographers, especially, use cameras that can write the images to two cards simultaneously.
• Get software that can recover data from the memory card. This is not necessarily a good safety net, though, as I found out today attempting to recover data from a friend's card.

These may work for you:

I use Nikon Transfer software to copy photos from my camera. This is a free download from Nikon. If you keep the camera cable connected to the computer, you can configure this software to make uploads simple and painless:



You can configure this to automatically save the images to a second location. Adobe Photoshop has a similar program that also works well.

Some tethered applications:

• Nikon Camera Control Pro 2 Software offers tethered shooting for Nikon cameras.
• Canon's EOS Utility, which comes with Canon DSLRs, has tethered shooting built-in.
• Sofortbild is a freeware tethered shooting application for Mac and Nikon DSLRs.
• A review of more applications can be found here.

PhotoRec is a a free data recovery program than runs on Windows, Mac, and Unix. There are a myriad of commercial products that do recovery also.

Canon EOS 1D series cameras can write to multiple memory cards simultaneously. So does the Nikon D3 series cameras. These are expensive, but because you don't want to get the bride angry, they are worth every penny.