Using Magenta Filter for Shooting With a dSLR Camera Under the Daylight

Lee Holder with CC30M filter In the previous article and during lengthy discussions on various forums, we promised to demonstrate the usefulness of magenta filters. Those filters compensate the imbalance between the sensitivities of color channels in digital cameras. Promises should be fulfilled, and having this filter, it takes nearly no time to prepare the demonstration – given that you have that sunlight...

The Test Shot

Test shot I have already used the view from the balcony of my Moscow apartment as a test scene for quite some years. For different times of the day and during the year I can get very different lighting conditions, from back-lit to the side and front-lit scene. For this case, it was something between the front and side-light, which you can judge by the directions of the shadows.

Raw histogram without filter Raw histogram with CC30M filter In this experiment, the camera used was a Canon 1D Mk III with a 35/1.4 lens mounted on it. I shot two series of seven frames each. The exposure difference between two consequent frames was 0.3 eV. The first series were shot without the filter, while the second with a Lee gel filter CC30M on the lens. One shot was chosen from each of the series for further analysis. The criteria for the choice was the very start of the clipping in the green channel.

As it was expected, the start of the clipping occurred at different exposures.

1. For the series without the filter, it was 1/100 s f/8
2. For the series with the filter in for of the lens, the clipping started 1/50 s f/8

At a first glance, presuming the filter blocks only the green channel, this means that the red and the blue channels should be exposed one stop better. However, the blue and red channels are sensitive to a pretty wide range of spectrum. That's why the actual per channel exposures were changed less than that, as it is obvious when looking at the white balance coefficients.

1. For the shot without the filter, both R and B coefficients are about 2, that is white balancing is equivalent to pushing these channels +1 eV.
2. For the shot with the filter, both coefficients were about 1.3, that is about 0.4 eV push for white balancing.

For further analysis we are choosing an area which is marked on the shot with a red rectangle. Raw Photo Processor was used to process the shots. Sharpening and color blurring were switched off. Demosaicing was using the AHD algorithm. As we saw from the prior experiments, this converter is less prone to producing noise.

Noise In the Red Channel

Red channel. Left: with CC30M, right: without filter As it was shown in the previous article, the noise between different channels varies greatly. For daylight, the most noisy one is generally the red channel, and the least noise is found in the green channel. This is clearly visible in the shadows of flat-lit objects even before any post-processing. Post-processing through the increase of contrast and sharpening amplifies this effect and the difference just jumps out at the viewer. For shots of real life, this effect might not be so obvious, however in the neutral areas below the mid-tone like the triangle-shaped area of the mesh fence to the left of the staircase leading down to the basement the effect is visible without any adjustments in post-processing. In this dark triangle, the Standard Deviation in the red channel is 1.4 for the filtered shot, and 1.85 for the unfiltered shot. Given that the channel values are around 22, this is quite a difference.

If one starts to push the shadows from 21-22 to 42-44 and applies slight sharpening (300%, 0.5, threshold 10) he gets something like illustration provided here, where to the left is the crop from the filtered image, while to the right, there is the crop from the image shot without the filter.

Looks like the filter used for this experiment is not strong enough, and the experiments should be repeated with a filter of a higher density, however even with CC30M filter, the difference is quite obvious.

Pro et Contra

As we can see, the filter deprived us of one stop of light, and helped slightly more then half a stop to avoid the imbalance of the channels. It is possible that a filter which will attenuate the light for one and a half stop will improve the situation even further. In other words

1. If there is enough light (say when shooting a landscape using a tripod) and the possibility that post-processing of shadow areas is quite possible, the filter will improve the situation.
2. If there is not enough light, the filter will make the situation worse.

Summing up, the filter is useful if you are shooting at base ISO.

The price of the solution is about $40 ( gel filter CC30M or CC40M plus Lee Gel Snap Holder). The Gel Snap Holder can be mounted on any lens, with the diameter of up to 82 mm, and on most flashguns. One size fits all.

Channel Noise and Raw Converters

As we mentioned in our previous article, the difference in sensitivity between the color channels of a digital camera leads to different per channel levels of details. Consequently, we have a different level of noise in different channels. The other factor which determines the difference in the levels of noise, is the greater number of green pixels in the Bayer sensor. This allows to decrease the level of noise in the green channel by approximately 1.4 times and makes the difference in noise between channels even greater. What results from this is a certain imbalance in noise levels for different channels

However, it turns out that the topic has much more dimension that meets the eye. As a matter or fact, while converting the Bayer structure into a regular 3-component RGB image, the source channels are mixed together. This happens mainly because of two reasons.

1. If the white balance is achieved through matrix multiplications, where the matrix compensates for both white balance and performs the color transform to account for the color properties of the sensor (a sort of input profile) the channels are mixed.
2. While performing demosaicing through complex interpolation like with AHD or VNG, the color channels are also mixed.

It is evident that this problem will be most noticeable in shadows, where the signal to noise ratio tends to be low. To demonstrate this effect, we decided to underexpose our test target.

The scenario when we underexpose by 3 steps and then compensate in the raw converter by 2 steps is a well-known method of raw processing, often caused by exposure to the right (ETTR) technique of shooting. Following the ETTR method, in many cases the middle tones are underexposed in order to preserve the highlights from clipping. Next, at the stage of the raw conversion, the middle tones are pushed to the place (more details on this can be found in the article Zones and Digital: Two Methods of Exposing).

Test Shot and The Method of Testing

The test target was a piece of white cardboard. This target was lit by bright daylight at about noontime. The shot of the target was taken using -3 eV exposure compensation. The color temperature as displayed in Adobe Camera Raw (ACR) was 5200K.

For the file that was converted from raw without any additional exposure compensation in the raw converter, the average RGB values were ranging from 39 to 40 (on 0-255 scale). Setting ACR exposure compensation slider to +1.4 eV resulted in values ranging from 69 to 70. These values were set as target values for all the tested converters.

In order to make noise more visible, during post processing all renditions were put through the same curve (0,50; 255,100) and the same sharpening (300%, radius 0.3, threshold 10).

For all the converters tested, all internal sharpening settings and noise reduction were set to 0. We are going to look not only into the comparison of the noise level between converters, but also into the comparison of the noise levels between different channels in the same converter.

For those converters where the half-mode exists, we also performed conversion in that mode. To make the comparison more meaningful, the image obtained through half-mode was upsized two times in Photoshop CS3 to match the dimensions of the normal image. For this, we used Image Size – Bicubic Sharper transform.

Test frame was shot by Canon 1D MKIII camera at ISO 100.

dcraw

dcraw half-mode channel noise For half-mode in dcraw, using -h key, we can see a striking difference in noise between the channels (from hereon out, the red channel is displayed on the left part of the image, while the green channel is on the right). The difference is much greater than it was in the experiment outlined in the previous article, for almost the same shot. This is partially because for that experiment the shot was underexposed by -2 eV, not by -3 eV as it was for this experiment. Yet another reason is the different processing. There are also other reasons for such a difference, which we will analyze a little later.

dcraw AHD-mode For AHD demosaicing we see a different situation. The image contains high frequency noise. The green channel becomes more noisy by a good margin. This is very understandable because demosaicing employs data from the neighboring channels, inevitably mixing the noise from those channels into the green channel. The data from the neighboring channels is needed to restore details or the phantom appearance of the details in the image. However, the level of the noise in the red channel is now decreased because the data from the less noisy green channel is mixed into the red.

Adobe Camera Raw

Adobe Camera Raw Adobe Camera Raw does not have any methods to control demosaicing by the user. The noise is very similar in character to AHD implementation found in dcraw, however it has a little more fuzzy appearance. The noise in the red channel is lower than the one originating from dcraw. However, the green channel also appears more fuzzy. In the green channel we can also see low frequency noise, which obviously was added there from the red channel. It should be noted that the other two raw converters do not exhibit this effect at all.

Raw Photo Processor

RPP, half mode Raw Photo Processor (RPP) is a free Mac-only converter. The focus of the development of this raw converter was the quality of the resulting image. Comparing its output in half-mode to the half-mode of dcraw, we see that RPP output looks significantly better, especially when it comes to the noisy red channel. We can conclude that the noise levels in RPP are lower because, internally, this converter uses calculations in floating point. This results in a much lesser digital noise, because now we are not introducing rounding errors.

RPP, AHD mode For AHD demosaicing the noise is also lower than it is for ACR conversions. The noise is well-shaped without fuzziness. It is a high-frequency noise, and it has a low level. Additionally, the green channel does not contain low frequency noise, which sets RPP apart from ACR in this regard.

Conclusion

Once again, we see the difference in noise between the channels. As expected and in line with earlier demonstrations, this difference is quite significant.

We also can conclude that the character of the noise depends on the raw converter and on the method of demosaicing of the raw data.

Of course, in the absence of a detailed description of the internals of the converters (except dcraw which is OpenSource) we can't categorize the reasons in great detail, saying that this noise component comes from rounding errors, while that originates from matrix multiplications performed in order to establish white balance. Nevertheless, the difference in the noise levels between different raw converters is so great that it becomes the major factor for choosing one converter over the other, if extreme adjustments of the image are needed.

Why it is Important

One might ask why this noise is of any interest as it become visible only after intensive manipulations of the image, however, it is not that simple.

1. The red channel is very contrasty on the landscape shots, especially for the shots that contain cloudy skies. If we need to use the red channel in Channel Mixer, in Photoshop (following Dan Margulis), then the noise from the red channel will make the sky noisy.
2. In many cases the red channel is the auxiliary color like it is in the sky. Auxiliary colors create fine gradations. If the auxiliary channel is noisy, the gradations will be murky.
3. The experiments were performed at ISO 100. By increasing the sensitivity in the camera, we increase the problem.

Renditions of Three Popular Colour Targets

These Lab renditions of Macbeth ColorChecker, ColorChecker DC, and ColorChecker SG (attached archive contains three 16-bit Lab TIFF files of the targets; if you need them larger please upsize in Photoshop using "Nearest Neighbor" method in Image -> Image Size) can serve as a visual reference as well as for adjustments "by numbers" during raw conversion and post-processing.

Please note that ColorChecker SG contains a structure very similar in appearance to 24-patch regular ColorChecker; but the values of patches are in fact different.

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ColorCheckerTargets.zip	13.67 KB

Spot-Metering: Reading Adams in Reverse Direction

Zone Intervals

What is the connection between exposure and raw converters? Or, to put it another way, why consider exposure on this site?

For that, there are two reasons. First, we would like to discuss various photography-related topics. Second, the quality of the resulting image largely depends on the correct exposure, as do the time and the effort spent during the conversion.

Lets start with the definitions Adams suggested to the zones.

The zones are separated by one step eV, the borders of the zones are about half a stop (½ eV) from the denoted centres.

Zone 0 spreads about ½ eV into the region of higher exposures (lower densities), and towards infinity into the region of lower exposures; while Zone X spreads about ½ eV into the region of lower exposures (higher densities), and towards infinity into the region of higher exposures.

Zone 0

Black, no texture or detail.

Zone I

Near black, no detail. Darkest beginning of gradation.

Zone II

Dark gray-black, possibility of slight texture, you think you see it - maybe. Mostly gradation.

Zone III

Important Zone, very dark gray, but good texture and detail can be seen. Dark textured bark on shadow side of tree. Where you will probably want to "place" your shadow details. Darkest detail and texture.

Zone IV

Medium-dark gray, dark green foliage, shadow side of Caucasian skin. Details plainly visible.

Zone V

Your meter's suggested settings. Medium gray, Kodak 18% Gray Card, clear dark blue Northern sky, excellent detail visible. (We need to mention here that modern spot-meters are calibrated somewhere between 12% and 14%, most often to 12.7% = 18%/SQRT(2) – that is ½ eV darker, with the goal of better preservation of highlights.)

Zone VI

Rich mid-tone gray, average Caucasian skin in sunlight, shadowed snow on bright sunny day, sharp fine detail visible.

Zone VII

Bright light gray, highest Zone that will still hold good details. Weathered white paint, silver hair.

Zone VIII

Light gray-white, shows last texture (minimum) but no detail. Reflected highlights from light colored skin, textured snow in sun. Gradation exist.

Zone IX

Almost white, must be compared to white to tell difference, no detail or significant texture visible. Lightest gradation values.

Zone X

Reproduces as paper base white, no image recorded. In print, will appear as specular highlights, sun reflection from chrome bumper, sunlit drops of sparkling water, etc.

The key does not necessarily appear in the beginning of the record. Reading each zone descriptions backwards one can easily see that those descriptions contain very specific statements on the level of details perceived in each zone. Often the statement is in the end of the particular description. For those zones that contain most important details the statement is always at the end. The total number of zones where Adams suggests perceivable details do exist is only 5 – from Zone III to Zone VII. Evaluating a particular scene one can decide where he or she wants the highest possible level of perceived details; and what should be the brightness of that zone. The choice of brightness is limited for best detail perception. Only 3 zones qualify as comfort zones to contain the details the viewer can fully appreciate, those are: Zone IV to Zone VI. If your spot-meter places everything into Zone IV, all you need now is to decide on the initial compensation for the particular scene. It should be 1 eV or 2 eV, once again depending on the desired brightness of the zone containing important details. This way there is much less guesswork and the results are much more predictable.

Andrey Tverdokhleb who designed the raw processing software RPP (Mac-only, http://www.raw-photo-processor.com/RPP/Overview.html) suggested to add the following. The method above works just as well and does not depend on the dynamic range of the camera. However, one should account for the fact that in the case of a camera with a very limited dynamic range Zone IV may already contain visible noise.

Zones and Digital: Two Methods of Exposing

The question whether one should expose "to the right" or should it be "exposure for the subject" (centred exposure) causes a lot of discussions when it comes to digital capture. Technically, this boils down to a simple question of placement lightly textured whites, like snow - should those be hitting the right wall of the histogram; or should they be placed about 2 stops lower, to the left on the histogram; or even somewhere between 0 and 2.

The table below compares two exposure techniques, the centred exposure and exposure to the right (ETTR) at base ISO and for a fully exposed channel (for the majority of the cameras and shooting conditions Green channels are most exposed), not taking the white balance into account. As you can see, exposure to the right results in substantial, 2.5 stops, underexposure of middle tones - instead of "prescribed" middle RGB value of about 117 at Zone V we have only 53, that is Zone V is exposed to somewhere between Zones III and II. Exposing for highlights is ETTR, and it is very close to dialing -2.5 eV compensation for the spot-meter. In this case contrast and density distribution with default converter curves will be obviously wrong, unless a converter has an ETTR mode of conversion. Once again, noise in shadows, especially on higher ISO settings and under artificial light can become a very serious issue.

Centered exposure suggests that there is slightly less then 3 eV headroom from spotmeter readings to the highlights (instead of nearly 5 stops in the case of ETTR). However shadows have rather high values and are essentially much less noisy and more detailed then with ETTR method. It can be said that on a resulting image dynamic range is re-distributed compared to ETTR, favoring highly detailed midtones and noiseless shadows.
Zones, ETTR, and Centred Exposure
To account for the white balance one can divide linear RGB values by white balance coefficients. This makes ETTR even less attractive, as now for a typical camera daylight shots Red and Blue channels are additionally underexposed 1/2 stop; under incandescent light underexposure of the Blue channel can be more then 2 stops. No wonder that with "exposure to the right" many cameras will exhibit an excessive amount of noise even in midtones if those are brought up to target RGB values of about 117 in conversion or post-processing. Images like that are often lacking details, and can't be sharpened in an effective manner because noise becomes intolerable. The alternative is to leave midtones at low RGB values; but then the image looks flat and underexposed.
Same happens when ISO is bumped in the camera. We increase sensitivity in order to compensate for the decrease in of the number of photons hitting the sensor. Each stop of ISO bump is effectively counter-acting devision of linear RGB values by some value close to 2; the exact value depends on the analogue circuitry used in the camera. The smaller is the photon count in midtones the higher is the noise. Details become fuzzy, level of details decrease. The higher is the ISO setting, the more important in terms of overall image quality and image usability it becomes to expose for the subject.

Certainly in a lot of scenes higher values of Zone VIII as well as Zones IX and X are nearly absent and can be ignored completely; in such cases both methods produce results close to each other.

Interesting exception however is Fuji S5. Due to the double structure of the sensor those cameras have, when the main sensor is exposed to Zone V the secondary is exposed about 3.5 stops higher, between Zones VIII and IX. This way for centred exposure the whole range of the zones is captured at base ISO and even at 1 stop above base. However a special tone curve is needed here to compensate for the flatness of the image that results from a regular type of tone curve.

For a raw converter developer it makes sense to use a "Zone V" slider to control the tonality of the rendered image. Effectively this slider counteracts the decision on shifting Zone V taken at the time of shooting. This slider is nothing more but one of the parameters controlling the shape of the tonal curve applied during conversion. In many cases the initial value for this slider can be set to the amount of eV compensation as it is recorded in the raw file. The slider range of -5 to 5 eV proved to be sufficient in the testing. The other useful slider sets the dynamic range for the resulting image.

White Balance in Digital Cameras: Problems

Current digital cameras provide the photographer with what is considered to be excellent means for instant diagnostics of an image right after the shot is taken.

1. We can evaluate the shot on the LCD screen.
2. The camera is nice enough to display a histogram (luminosity histogram and for many high quality cameras, per channel histograms as well).
3. Finally, the camera can display zones of clipping for both under and overexposed parts of the image.

It is the popular opinion that these means of diagnostics are very good and really allow the photographer to evaluate exposure errors such as under- and over-exposure, among others. Of course these are all common misconceptions.

• The single channel histogram is calculated based on the luminosity formula that represents a linear combination of the values of R, G, and B channels after gamma-correction; while the total weights of R and B channels are only of about 20%. And that is the best case scenario, because for some cameras this histogram in fact represents only the G-channel, ignoring the other two. Such a histogram not only can't help the exposure evaluation, but in many cases is extremely misleading.
• All other means of diagnostics like per channel histograms, blinkers, and the image itself displayed on the LCD on the back of the camera, are displayed after the white balance is applied (it might be worth mentioning, that all the settings in camera distort the histogram; this includes contrast, brightness, saturation, sharpening, and tonal curve, often referred to as gamma-conversion. This happens because the histogram is derived from the JPEG preview and represents the rendered data more than the actual raw data captured.)

Different display of the same frame data Let's illustrate the above statement, referring to the white balance. Here we have three sequential shots of the corner of my room lit by a lamp that has 5000K CCT. These shots are taken with different white balance settings, 10000K, Auto WB, and Incandescent. As we see, both histograms and the general appearance of the image are vastly different, however the raw data captured in all three shots practically identical, as far as that is possible, given that the shots are hand-held.

This unfortunate trait of digital cameras devalues those built-in diagnostic tools if we do not program the camera in a very particular way, as it will be shown below. Let us first note that everything described below has no bearing to photographers who shoot JPEGs, for their case, the camera displays the final result and the histogram, preview, and overexposure are all real, everything that is cut off is already truncated in the JPEG conversion. The following text is for you if you shoot raw and if you try to use your camera to the most of its capabilities.

All camera histograms below result from Canon 1D mk III with standard settings for contrast and color space set to Adobe RGB.

The Theory: Spectral Sensitivity of Sensors

Kodak KAF-10500 spectral sensitivity Let's have a look at the curves of the spectral sensitivity of a sensor found in a modern digital camera. Those curves are not published by many, but one can find some for Kodak, Dalsa, and even Sony sensors. On this illustration you can see the curves for the quantum efficiency, taken from the data sheet for Kodak KAF-10500 found in Leica M8 cameras. Sensors for different cameras have different curves of this type because such curves depend on the semi-conductor properties of the particular sensor, as well as the spectral characteristics of the Bayer filter used.

Suppose we are shooting a gray card under some fixed lighting conditions. Per channel response will be the product of the spectral response of the light source and the spectral sensitivity of the particular channel (to be precise, it's an integral taken on the wavelength from blue to red).

The Tool: Rawnalyze

To get images without the white balance compensation, the most widely used tool is dcraw -r 1 1 1 1. However, the resulting pixel values will be scaled thus making the image harder to evaluate.

Rawnalyze is a much better tool for the job. It allows one to look into the unmodified raw data, builds a histogram, allows to play with white balance, etc. The histogram display is not perfect, having the linear scale on the X-axis; it is also unknown how the black point is applied, if it is applied at all, but as of the time of writing, this is the best tool available.

Rawnalyze can crash while executing certain commands, like selection. This can be cured if the program window is maximized. The author is aware of the problem and it looks like the last versions of Rawnalyze do not exhibit such behavior.

Besides Rawnalyze, the photos of the histograms on the back of the cameras will also be used, because it is the only information that is available at the time of shooting

The Practice: White Balance for Daylight

Camera WB at daylight The simplest way to evaluate the real scope of the problem is to shoot a gray card, or just a white piece of paper, which does not contain an optical brightener, using sun as the light source, and then evaluate the histograms. Setting exposure correction to +2 eV (that is exposing the gray card for the third zone instead of the fifth), and using Auto WB, we will see a histogram similar to the one shown on the left.

Everything is as expected, narrow peaks appear to be exactly balanced in each of the channels. The camera suggested that the color temperature is 5200K which looks normal for a Moscow spring early afternoon and dusty air. The histogram displayed by Adobe ACR or any other standard converter looks just the same as this one.

The peaks on the histogram are not ideally sharp and narrow, because there is some brightness variation across the field, due to some lighting variations caused by the reflections from the walls, the ceiling, and other subjects in the room; the lens, although very good (Canon 85/1.8 at f/8) still vignettes; the camera body itself also vignettes: the sharper is the angle of the incidence of the light beams hitting the sensor, the lower is the response of the sensor, and it is well known that this angle becomes sharper towards the edges of the frame.

Raw histogram for 5200K (daylight) image Now let's have a look at the real data, uncooked raw. It is very obvious that the best exposed channel is the green one, while the worst exposed is the red. The blue channel is somewhere in between. It is important to mention how different this histogram is from the histograms displayed by the camera itself, and by the raw converters. They are taking care of this “error” for us.

More Practice: The Detailed Description of the Problem

We have two problems, right with the broad daylight:

1. The poor exposure of the red channel as compared to the green and, to less extent, the exposure problem with the blue channel.
2. Clipping of the green channel, which the camera will give no warning of.

Both problems can be easily illustrated - as with the digital cameras one doesn't need to wait for lab processing.

Noise in the Red Channel

Noise in red channel vs green (dcraw -h processing) Let's take an image of the same sheet of paper, applying -3 eV from the middle grade. Thus we get into the beginning of shadow values. Next, let's stretch the channels a little bit, the same way we do it when we extract details from shadows. Looking at the channel planes at 100%, pixel level, we can easily see that the red channel is much noisier than the green, and that is even before applying any sharpening. There is nothing really strange here, because the red channel is underexposed by three stops, not by two, as the green one. Of course, to make the difference more pronounced, a strong dose of post processing was applied, however, if the red channel is weaker than we had it here, the difference will be even more drastic.

Noise level depends on raw converter used, interpolation mode and so on.

Unfortunately, there is no easy way to cope with this problem. The sensitivity of the channels could be balanced either by a filter on the lens (for this particular camera model it would be magenta CC30M), or with the same filter on the light source (flashgun). However, this can be done only if the light is strong enough. The results with the filter discussed in the next article.

Clipping that Camera Fails to Warn About

Cityscape at daylight Camera histogram of cityscape Because the camera “fixes” the histogram, it is highly possible that in some situations, one channel clips, while the camera histogram fails to show it. As a matter of fact, if you're shooting, setting the exposure in such a way that the histogram comes close to the right wall (ETTR, exposed to the right) the clipping will occur more as a routine rather than as an exception. Here the image shows a very typical cityscape lit by the sun coming from a side. Now, looking at the photo of the camera LCD, we can say that the histogram seems fine and there is no overexposure.

Cityscape RAW data Unfortunately, the green channel is clipped, while the red channel is one stop underexposed, as you can see it on the histogram of the raw data.

If the white balance is intentionally changed, or if the contrast setting is changed, then the histogram displayed on the back of the camera will change too, as we discussed earlier, however the raw data captured will be absolutely the same.

So we are coming to the conclusion that even per channel camera histogram does not provide us with trustworthy information about under- or over-exposure of the channels. In one of the next articles we will try to show how to tune the camera to avoid this kind of misinformation.

The Cooler Lighting

Camera histogram at 7500K light Raw histogram at 7500K light If the lighting is cooler, nothing really changes. From the point of view of the camera, the blue channel is more exposed, as you can see it on the image, however, the level of details is less than that of the green channel. If the white balance of the camera is set to the real value of the light color temperature and not to the Auto (or as many often do it, to daylight), then the readings on the camera LCD will deviate from the reality even more. Wider peaks on the histograms compared to the shot taken at daylight are because with the artificial it was more difficult to achieve the evenness of the light across the target, than it was with the sun.

Once again we can see the that the statement holds true: histogram displayed on the back of the camera differs greatly from the real histogram of raw data.

Incandescent Light

Doll under incandescent lamp Camera histogram (2800K light) If the light is provided by the bulbs, the behavior changes to the opposite: the camera can give a false alarm of an overexposure. This time, the model was a red doll on a white stool. The source of light was a 60 Watt incandescent bulb; the camera evaluated its color temperature as 3200K, while Adobe ACR suggested 2800K. The camera histogram was of a very strong overexposure. Please note two things, first, the exposure correction was set to +2, second, the black halo around the doll - those is the blinker, the area that the camera considered to be overexposed.

Raw histogram (2800K light) The real raw histogram shows no sign of overexposure. And even more, there is a headroom of about half a stop in spite the fact that we already applied a +2 eV exposure correction in the camera.

It is now pretty obvious that one of the reasons of the constant bad quality of the images taken under artificial light is the banal underexposure resulting from the blind trust in histograms as they are displayed on the back of the cameras. If the shot is exposed as it is recommended by the camera, it turns underexposed by more than 2 stops, resulting in a noisy, poorly detailed image.

Conspiracy Theory

The spectral sensitivity of the sensor is established at the design time. The manufacturers of the cameras are forced to set the per channel sensitivity in such a way that the resulting images will be acceptable under both daylight and incandescent bulbs. From this point of view, the high sensitivity of the green light channel is a forced trade-off, allowing to get a reasonable color of the human face, under artificial lighting (the green is the auxiliary color, not generally present in the faces, but it is the closest to luminosity, and if it is underexposed, the noise will prevail). A knowledgeable photographer should correct for this, shooting in the daylight. Camera preview, ideally, is supposed to show the shot after the white balance is applied, especially taking into the account, such modes as print right from the camera, or print all selected from the compact flash card.

The situation with the histograms is much more complicated. Of course it is desirable and sensible when the histogram on the camera and the histogram displayed by the raw converter are similar, however the photographer should be informed of the real state of matters in the raw file. It well may be that those photographers interested in the real state of matters are very few, and they can use an easy way to cut the corner, and to trick the histogram display, forcing the camera to show a histogram which is much closer to the reality. Thus, the camera manufacturers have chosen the way that most of the users can appreciate.

What to Do Next

Once again, we have two problems:

1. The tooles used in the camera for under- an over-exposure diagnostics are taking into account the setting of the white balance, which is completely wrong in the case of shooting raw.
2. The sensitivities of the channels are unbalanced, which results in the excessive noise in the weaker channels.

The first problem can easily be cured with camera settings. The trick is to set the per channel white balance coefficients all close to 1. This is known as setting white balance to UniWB. We will describe how to set UniWB into the camera in the next article.

The second problem can be cured either by a color correction filter on the lens or by a similar filter on the light source. We demonstrate the benefits of this approach for daylight shooting in the separate article.