2.3 Megapixels and Crop Factors

The single, most highly emphasized feature of any model of digital camera is its Megapixel rating.  When I got into digital photography, cameras were around 6 MP (Megapixels), whereas now they’re typically between 10 and 24 MP.   Just as with the Megahertz wars between computer manufacturers in the 1990’s (and the Gigahertz wars that followed), camera manufacturers are currently in a fierce Megapixel war, and they’re hoping to capitalize on your ignorance by making you believe that their newest camera model, by virtue of simply having more Megapixels than the competition, is therefore superior.  As will be explained here, however, higher Megapixels do not automatically translate to higher image quality, and in fact many consumer-grade models with high megapixel ratings produce poorer image quality than pro-grade or pro-sumer models having fewer megapixels.  Understanding the reasons behind this apparent paradox will be important for anyone shopping for a camera in the near (and perhaps not-so-near) future.

2.3.1 Pixel Density

The first important concept that we’ll require for this discussion is that of pixel density.  This is simply a measure of how tightly packed the pixels are on the camera’s imaging sensor.  We’ll measure this in pixels per millimeter (pix/mm).  Since the horizontal and vertical pixel densities are typically the same, we won’t be overly concerned about distinguishing between these, and will refer to either as simply linear pixel density, or just pixel density for short.  The important thing to understand is that for a given number of Megapixels (which, by the way, is one million pixels, or one million of the basic dots that make up an image), the physical size of the imaging sensor inside the camera determines how tightly those pixels have to be packed together in order to all fit on the sensor.  As we’ll see, packing them too tightly can degrade image quality.
    In the case of full-frame sensors, the pixels are spread out over a relatively large sensor: roughly 36mm by 24mm.  This just happens to be the size of a 35mm film cell (remember film cameras?), and has come to be known as full-frame (despite the fact that there are even larger sensors, known as medium format sensors).  As of this writing, most consumer-grade digital cameras are not full-frame: they are cropped, meaning that they are smaller than a full-frame sensor.  For cameras with a 1.5x crop factor, the sensor is 43% smaller in both width and height than a 35mm film cell, resulting in a sensor with 66% less area than a full-frame sensor
a rather substantial loss in sensor size.  This is because the crop factor is applied to both the width and the height separately.
    So, in any consumer-grade camera with a high Megapixel rating, you can be pretty sure of one of two things: either the pixels are packed very close together on the imaging sensor, or the individual photosites (the electronic elements that capture individual pixels) are each very small, or both (i.e., small photosites packed closely together).  Figure 2.3.1 (below) crudely illustrates the relationship between pixel count, crop factor, and pixel density.

Fig. 2.3.1: Crop Factors and Pixel Density.  Left: A full-frame sensor with
40 large photosites (gray circles in the illustration).  Right: A 1.3x sensor with
15 large photosites (top) and a 1.3x sensor with 40 small photosites (bottom).

    Having photosites packed closely together can be both good and bad.  In theory, larger numbers of photosites packed tightly together should be better at resolving fine details in an image, such as the individual feather barbs in a bird’s plumage.  To the extent that the real world contains tiny, microscopic features, an ideal imaging sensor with the tiniest pixels should be best at capturing and representing those individual features.  (A simple way to think of this is in terms of a fat-fingered pianist: fatter fingers are more likely to make mistakes by hitting multiple keys on the keyboard, resulting in a poor performance).  Unfortunately, at the microscopic scale of pixels and photosites, the inconvenient realities of physics can intrude, as we describe next.

2.3.2 Noise

The problem with having photosites that are either small, or closely spaced, or both, is that the resulting pixel values (i.e., the precise hue that you see at each pixel in the resulting image) tend to be affected by noise.  What exactly do we mean by noise?  The simplest definition of image noise is pixels that are of the wrong color or brightness.  Open any of your photos in your favorite editing software, and zoom in as far as you can, till you can see the individual pixels making up the image.  In any smooth, homogeneous background region of the image you’re very likely to see at least a few pixels that differ in color or brightness from those around them.  These off-color pixels obviously don’t correspond to anything in the original scene that you photographed. 
    Figure 2.3.2 shows an example of a noisy image.  This Screech Owl (Asio otus) was photographed with a pro-sumer DSLR camera (the Canon 30D
an 8.3 Megapixel camera with a 1.6x crop factor) at an ISO setting of 640 (ISO is discussed more fully in section 2.5).  At the top is the raw image, and at the bottom is the same image after noise was reduced via software.  You can see that in the top image, the background region to the left of the owl is very speckled, whereas this same region in the bottom photo is much smoother.  Note that noise is difficult to discern in the regions of the image occupied by the bird.  Although in extreme cases it is possible to see noise in your subject, for moderate noise levels you’ll typically only notice the noise in smooth, background regions of the image.

Fig. 2.3.2: Digital Noise.  Top: A photo taken on a pro-sumer camera (Canon 30D)
at ISO 640, with noise evident in background regions.  Bottom: same image, after
selective noise-removal in Photoshop.  A higher-quality, pro sensor could
probably produce the bottom image straight out of the camera.

    Although the noise in this photo is likely due primarily to the use of a high ISO setting (ISO 640 was fairly high when the Canon 30D came out), noise of this type can also be caused by using a camera with pixels that are either too small or too closely spaced.  On the Canon 30D, the linear pixel density is 156 pixels/mmthat means that if you counted just the last row of pixels along one edge of the imaging sensor and divided that count by the length of the sensor (in mm), you’d get approximately 156.  In contrast, the Canon 5D, a pro-sumer model with a reputation for having much lower noise than its contemporaries, has a pixel density of only 122 pix/mm.  Though the bottom image in Figure 2.3.2 resulted from a noise-removal filter applied in software, you may think of this figure as illustrating, conceptually, the difference in noise levels between a camera with small, closely-spaced photosites (top image) and one with large, well-spaced, or simply higher-quality photosites (see section 2.3.4, below).
    Precisely what causes noise in small, closely-spaced photosites is a topic that we will consider later (in section 2.5).  For now, we just want to emphasize the following: that cameras with higher Megapixel ratings may (depending on sensor size) have more tightly-packed photosites, and that for cameras that do have very tightly-packed photosites, they will tend to have both (1) the ability, in theory, to resolve finer details, which is a good thing, and also (2) higher per-pixel noise levels, which is a bad thing.  Precisely how much of a good thing and how much of a bad thing, and what is the sum effect of these, is a question that is camera-specific.  The only way to find out for sure which of two cameras with different pixel densities actual provides more useful imaging detail is to compare the two side-by-side in controlled tests, and unfortunately, this is difficult for consumers to do and is rarely done by professional product reviewers (see the Comparometer at imaging-resource.com for some examples).
    Just to make sure we’re absolutely clear:  How can noise reduce the effective resolving power of a high-resolution sensor?  If it’s high-resolution, it’s high-resolution, right?  The subtlety here is that noise tends to be more visible the further you zoom in on the image, when you’re viewing it on your computer.  As you zoom in, noise that you didn’t notice before zooming suddenly starts to become more apparent.  When the bird only fills a small part of your image frame, you’ll typically want to zoom in a bit to make the bird appear larger in the frame.  The problem with noise is that it limits how much zooming you’ll be comfortable with.  With high noise levels, you may not be able to zoom in enough to make the bird appear as large in the frame as you’d like, since at a high zoom factor the noise may become so bad that the image just looks terrible
like a channel on a television with poor reception.
    But, can’t we just remove the noise later in software?
    Sometimes, yes.  If the noise is fairly moderate, it should only affect the background regions (or smoothly-colored regions of your subject, if any), and in this case you can, with sometimes greater or lesser effort, remove the noise pretty effectively in software.  The problem is that noise reduction software generally also reduces the sharpness of your image.   One solution is to painstakingly mask out the bird and then to apply noise reduction only to the background regions of the image.  If you have plenty of spare time to manually process your images in this way, then noise induced by small photosites may not overly concern you.  The greater concern is whether you’re gaining anything by buying a camera with both more pixels and more noise, and unfortunately, that’s a question that typically can’t be answered without actually buying two competing models and testing them out yourself to see which produces more
zoomable images. 
    Though it’s quite a hassle to do, it is possible to perform such comparisons.  Sites like Amazon.com that have a 30-day return policy, or like Adorama and B&H that have a 14-day return policy, will often accept returned products for a full refund, so you can, if you feel so inclined, buy two cameras, perform detailed comparisons within the allotted return period (being careful to read the fine print on the merchant’s return policy, such as regards activation counts), and then keep the better of the two models and return the other.  Until professional product-review sites start performing these types of
empirical resolution comparisons for us, we consumers have few other options.
    The option that I like best is to simply buy the most expensive camera offered by a manufacturer, subject to my budgetary constraints.  Within a given product line from a single company, it’s generally the case that you get what you pay for.  If you look at the graph below, you’ll see that while Canon’s and Nikon’s professional camera lines cost a lot more than their pro-sumer lines, they tend to have pixel densities less than or equal to that of the cheaper pro-sumer models.  This is significant, for the following reason: professionals do indeed buy the pro models, despite their often having lower pixel densities than cheaper models.  And why would they do that?  I’ll tell you why: because the pros know that the pro models really do produce better images, even if they have fewer pixels or lower pixel densities. 

Fig. 2.3.3: Pixel Density Versus Price.  Left: Canon.  Right: Nikon.
The x-axis gives pixel densities in pixels/mm.  The y-axis gives price
in US dollars.  Pro cameras tend to have higher prices despite having
lower pixel densities.  (Data current as of 2007).

Fig. 2.3.4: Prices and pixel densities of Canon cameras (2007).

Fig. 2.3.5: Prices and pixel densities of Nikon cameras (2007).

The moral of the story is: don’t be fooled by either Megapixels or pixel densities.  My advice is to select a manufacturer with the best product line of birding lenses (see Chapter 3), and then to buy the most expensive camera from that manufacturer that you can afford.  That’s what I’ve done, and I’ve yet to regret it.

2.3.3 Crop Factors and Magnification

Although the internet can be a great place to get certain types of  information, in the case of digital camera technology, the amount of misinformation circulating around is just astounding.  There are two important myths that we need to consider here.  The first is the so-called full-frame advantage.  This one isn’t quite as ridiculous as the other, since for some situations, and within the context of particular product lines, there certainly is or has been a full-frame advantage.  For bird photography, however, the ability to capture wider angles (still the primary advantage of full-frame cameras) is seldom useful.  Since most birds are both small and wary (meaning that they keep their distance), it is much more common for the bird to appear too small in frame than to appear too large.  In this regard, there’s scant reason to prefer the larger-framed sensor to the smaller one, as a general rule.
    The full-frame advantage, however, typically refers to another advantage that full-frame sensors have traditionally (i.e., in the recent past, and perhaps temporarily for the present) had: that particular full-frame camera models have tended to have low pixel densities and therefore superior noise characteristics.  To my knowledge, there is no inherent reason for this.  If a manufacturer can produce a full-frame sensor with a low pixel density, then they should be capable of producing a crop sensor (i.e., 1.3x, 1.5x, 1.6x, 2x) having the same, low pixel density, and therefore the same noise characteristics.  For the consumer, what is most relevant here is what camera models are actually available, and with what pixel densities and photosite technologies.  The point, though, is that when comparing two camera models, the fact that one model is full frame while the other is not, is irrelevant (all other things being equal) once you know which camera has the lower pixel density, or better yet, which has the better image quality.  As I mentioned before, the more expensive of the two is likely the better model (as long as you’re comparing cameras from the same manufacturer).
    The other major fallacy, or myth, that needs to be addressed is that of crop sensors providing greater magnification, or
reach.  More specifically, many people incorrectly regard a camera with a 1.5x crop factor (for example) as providing a free 50% increase in magnification.  This would be true only if you were comparing such a crop camera to a full-frame camera with exactly the same Megapixel rating, since only then would the (linear) pixel density be 50% greater.  But even then, in order to actually achieve a 50% increase in the apparent magnification in your final images, the crop sensor would have to provide equal image quality, in terms of per-pixel noise, to that of the full-frame camera.  In practice, 1.5x crop sensors typically don’t have the same number of Megapixels as same-generation full-frame cameras, and for those that do, the increased noise level would very probably force you to be more conservative in cropping the final image around the bird during postprocessing. 
    As a result, in the final image, assuming your standards for image quality are applied equally to the crop camera and the full-frame camera, the apparent magnification in the final images, after postprocessing, probably wouldn’t be a full 50% over the full-frame, and depending on the image quality of your crop camera, you might not be getting any additional magnification at all (in the final, postprocessed image).  Another way to put it is that if you did postprocess the images from the two cameras so that the image from the crop camera showed a 50% increase in bird size, that image would probably be noisier than the image from the full-frame sensor
and it may be so much noisier that you wouldn’t feel comfortable publishing that image at that size.  Most importantly, just remember: there is no free lunch.  If two cameras from the same manufacturer differ by $1000 or more in price, the more expensive camera is very, very probably the better of the two, for most photographic purposes.  Again, I refer you to the figure above showing that the more expensive pro models have tended to have lower pixel densities than the pro-sumer models.
    Finally, since we’ve been talking about pixel densities, let’s briefly consider how to compute them.  Manufacturers and retailers virtually never provide pixel densities in their advertised camera specs, but they’re easy to compute, as long as you know the crop factor and the Megapixel rating.  If your sensor has a 3:2 aspect ratio (which the vast majority of DSLR’s do nowadays, including all those from Canon and Nikon), then the formula for pixel density is:

where M is the number of pixels (i.e., roughly 8,200,000 for the Canon 30D, an 8.2 Megapixel camera) and cf is the crop factor (e.g., 1.6 for Canon’s consumer and pro-sumer models, 1.5 for many of Nikon’s models, and 1.3 for Canon’s most popular pro bodies).  If your camera’s sensor has an aspect ratio of other than 3:2, then you’ll need to replace the 2/3 in the numerator of the above equation with the appropriate ratio.
    That’s all you really need to know about computing pixel densities.  Now, let’s have a reality check.  I’ve spent most of this section telling you that higher pixel densities tend to result in more noise and therefore crummier images.  Given the never-ending hype about higher-and-higher Megapixel cameras, are you really going to choose your next camera model by selecting the one with the lower pixel density, when everyone else is choosing higher Megapixels?  Obviously, as the technology advances, the manufacturers’ ability to produce high-density sensors with lower noise levels will (hopefully) improve.  But within a single generation of cameras, I do believe the trend for lower-density sensors to have better noise characteristics is a strong one.  As a case in point, though DSLRs are now available from the top manufacturers with 15 MP and even 21 MP, I prefer my 10 MP pro Canon body (the 1D Mark III), and feel that it provides all the detail I need.  The noise characteristics of this body are excellent.  Future bodies should, however, be even better.
    As a further case in point, the hummingbird shown below was photographed through a dirty pane of glass while hand-holding a 400mm f/4 lens.  The camera was the 8.2 Megapixel Canon 30D.  I’ve heavily cropped around the bird, so what you see below is just a small portion of the full image (the full-sized image is shown below the main figure, for reference).  Yet, with only 8.2 MP I was able to capture minute details on this extremely tiny bird, without flash, and without a tripod (though with Image Stabilization enabled):

Fig. 2.3.6: Anna’s Hummingbird (Calypte anna).  Taken with the Canon 30D,
an 8 MP camera, and heavily cropped in postprocessing.   Top: Cropped image.
Bottom: Original.  1/800 sec, 400mm, f/4, ISO 500.  No flash.

    The above discussion about noise levels and image quality resulting from particular photosite sizes and spacing is based on the assumption that
all other things are equal.  In other words, we’re talking about current state-of-the-art manufacturing technology.  There are some particular tricks that manufacturers can employ (or will soon be able to employ) to reduce noise levels at a particular photosite density and/or spacing.  We briefly consider a few of these next.

2.3.4 Sensor Technologies

One of the most exciting advances in sensor design that I’ve recently found out about is the use of so-called microlenses.  These are tiny lenses each of which is positioned over a single photosite on a sensor.  So, for a 10 MP sensor, imagine ten million tiny lenses arranged in a precise rectangular array roughly 35mm long.  Wow.
    The effect of microlenses is to improve the signal-to-noise ratio at each pixel in an image.  As figure 2.3.7 illustrates, the use of microlenses results in more of the incoming light actually being collected and measured by the photosite.  The image shows a single pixel, edge-on (i.e., viewing a slice of an imaging sensor, from the side).  Because there’s empty space between photosites on the sensor, much of the incoming light is lost, since it strikes the non-reactive space between photosites.  The use of a microlens positioned above each photosite increases the proportion of incoming light that is actually captured and effectively utilized by the photosite, resulting in more signal relative to a fixed amount of noise.  The result is less per-pixel noise in the resulting image.

Fig. 2.3.7: Microlenses.  Left: no microlens.  Incoming light that misses the
photosite is lost.  Right: photosite with microlens.  More of the light is harvested
by the photosite, as a result of the light-bending properties of the microlens.
(Percent captures in this example are completely hypothetical).

    A much more recent advance is the so-called back-illuminated CMOS, in which light sensitivity is increased by moving the photosensitive material to the top of the sensor and moving the attendant wiring below.  Apparently, with traditional CMOS sensors, some of the wiring for the photosites ran above the photosensitive material, and some percentage of the incoming light would strike the wiring instead of the photosensitive well, resulting in some loss of signal.  Now that manufacturers have figured out how to manufacture the photosites with the wiring on the other side of the photoreceptive material, more of the light is actually registered and turned into signal.  Additionally, moving the wiring below the photosite apparently reduces noise due to heat, since less of the heat (and perhaps magnetism?) generated by the near circuitry is radiated up into the path of the incoming light. 

Fig. 2.3.8: Front vs. Back Illuminated CMOS Sensors.  Left: in a front-illuminated
photosite, some of the light strikes wiring on the chip (gray X’s) and is lost. 
Right: by moving the circuitry below the photosensitive material, less light is lost
(Percent captures in this example are completely hypothetical).

    As time advances, other improvements in sensor technology may be expected to occur.  For the purposes of bird photography, our main interests will be in seeing sensors emerge with both higher pixel densities and lower noise.  To the extent that this is possible, we’ll be able to
put more pixels on the bird as it is sometimes said, resulting in more resolvable details of a given bird at a given distance.  Just how much more progress can be made in this direction is unclear.  My guess is that there are probably limits to what can be achieved at the microscopic level of photosites, imposed either by manufacturing technologies or even by the laws of physics (such as when the size of a photosite drops below the longest wavelength of visible light).  Individual photosites only collect so many photons per unit time.  Smaller photosites will necessarily collect fewer photons in a given time interval, and will therefore suffer from increased sampling error and resultant noise.  Note also that as sensor resolution increases, small-scale defects in lenses may become a limiting factor.  How soon such considerations may begin to limit technological progress, and whether there are any tricks that manufacturers can use to effectively overcome these limits, is anyone’s guess. 
    In summary, for the purposes of bird photography with currently-available DSLR’s, the treatment above should provide sufficient explanation of imaging technology, as it currently stands, to serve as a basis either for choosing a camera or conceptualizing how your camera’s imaging sensor works, at a very basic level.  As advances occur that provide significant improvements in our ability to capture detailed images of birds, I plan to document them here, so check back periodically.