Do you need 61 megapixels, or even 102?

The highest “native” resolution camera available today is the Phase One FX IQ4 medium format camera at 150MP. Higher than that there is the Hasselblad H6D-400C at 400MP, but it uses pixel-shift image capture. Next in line is the medium format Fujifilm GFX 100/100S at 102 MP. In fact we don’t get to full-frame sensors until we hit the Sony A7R IV, at a tiny 61MP. Crazy right? The question is how useful are these sensors for the photographer? The answer is not straightforward. For some photographic professionals these large sensors make inherent sense. For the average casual photographer, they likely don’t.

People who don’t photograph a lot tend to be somewhat bamboozled by megapixels, like more is better. But more megapixels does not mean a better image. Here are some things to consider when thinking about when considering megapixels.

Sensor size

There is a point when it becomes hard to cram any more photosites into a particular sensor – they just become too small. For example the upper bound with APC-S sensors seems to be around 33MP, with full-frame it seems to be around 60MP. Put too many photosites on a sensor and the density of the photosites increases, as the size of the photosites decreases. The smaller the photosite, the harder it is for it to collect light. For example Fuji APS-C cameras seem to tap out at around 26MP – the X-T30 has a photosite pitch of 3.75µm. Note that Fuji’s leap to a larger number of megapixels also means a leap to a larger sensor – the medium format sensor with a sensor size of 44×33mm. Compared to the APS-C sensor (23.5×15.6mm), the medium format sensor is nearly four times the size. A 51MP medium format sensor has photosites which are 5.33µm in size, or 1.42 times of size of the 26MP APS-C sensor.

The verdict? Squeezing more photosites onto the same size sensor does increase resolution, but sometimes at the expense of how light is acquired by the sensor.

Image and linear resolution

Sensors are made up of photosites that acquire the data used to make image pixels. The image resolution of an image describes the number of pixels used to construct an image. For example a 16MP sensor with a 3:2 aspect ratio has an image resolution of 4899×3266 pixels – the dimensions are sometimes termed the linear resolution. To obtain twice the image resolution we need a 64MP sensor, rather than a 32MP sensor. A 32MP sensor has 6928×4619 photosites, which results in a 1.4 times increase in the linear resolution of the image. The pixel count has doubled, but the linear resolution has not. Upgrading from a 16MP sensor to a 24MP sensor means a ×1.5 increase in the pixel count, and a ×1.2 increase in linear resolution. The transition from 16MP to 64MP is a ×2 increase in linear resolution, and a ×4 increase in the number of pixels. That’s why the difference between 16MP and 24MP sensors is also dubious (see Figure 1).

Fig.1: Different image resolutions and megapixels within an APS-C sensor

To double the linear resolution of a 24MP sensor you need a 96MP sensor. So the 61MP sensor provides about double the linear resolution of a 16MP sensor, as the 102MP sensor doubles the 24MP sensor.

The verdict? Doubling the pixel count, i.e. image resolution, does not double the linear resolution.

Photosite size

When you have more photosites, you also have to ask what their physical size is. Squeezing 41 million photosites on the same size sensor as one which previously had 24 million pixels means that each pixel will be smaller, and that comes with its own baggage. Consider for instance the full-frame camera, the full-frame Leica M10-R, which has a 7864×5200 photosites (41MP) meaning the photosite size is roughly 4.59 microns. The full-frame 24MP Leica M-E has a photosite size of 5.97 microns, so 1.7 times the area. Large photosites allow more light to be captured, while smaller photosites gather less light, so when their low signal strength is transformed into a pixel, more noise is generated.

The verdict? From the perspective of photosite size, 24MP captured on a full-frame sensor will be better than 24MP on an APS-C sensor, which in turn is better than 24MP on a M43 sensor (theoretically anyways).


Comparing the quality of a 16MP lens to a 24MP lens, we might determine that the quality, and sharpness of the lens is more important than the number of pixels. In fact too many people place an emphasis on the number of pixels and forget about the fact that light has to pass through a lens before it is captured by the sensor and converted into an image. Many high-end cameras already provide an in-camera means of generating a high-resolution images, often four times the actual image resolution – so why pay more for more megapixels? Is a 50MP full-frame sensor any good without optically perfect (or near-perfect) lenses? Likely not.

The verdict? Good quality lenses are just as important as more megapixels.

File size

People tend to forget that images have to be saved on memory cards (and post-processed). The greater the megapixels, the greater the resulting file size. A 24MP image stored as a 24-bit/pixel JPEG will be 3.4MB in size (at 1/20). As a 12-bit RAW the file size would be 34MB. A 51MP camera like the Fujifilm GFX 50S II would have a 7.3MB JPEG, and a 73MB 12-bit RAW. If the only format used is JPEG it’s probably, fine, but the minute you switch to RAW it will use way more storage.

The verdict? More megapixels = more megabytes.

Camera use

The most important thing to consider may be what the camera is being used for?

  • Website / social media photography – Full-width images for websites are optimal at around 2400×1600 (aka 4MP), blog-post images max. 1500 pixels in width (regardless of height), and inside content max 1500×1000. Large images can reduce website performance, and due to screen resolution won’t be visualized to their fullest capacity anyways.
  • Digital viewing – 4K televisions have roughly 3840×2160 = 8,294,400 pixels. Viewing photographs from a camera with a large spatial resolution will just mean they are down-sampled for viewing. Even the Apple Pro Display XDR only has 6016×3384=20MP view capacity (which is a lot).
  • Large prints – Doing large posters, for example 20×30″ requires a good amount of resolution if they are being printed at 300DPI, which is the nominal standard. So this needs about 54MP (check out the calculator). But you can get by with less resolution because few people view a poster at 100%.
  • Average prints – An 8×10″ print requires 2400×3000 = 7.2MP at 300DPI. A 26MP image will print maximum size 14×20″ at 300DPI (which is pretty good).
  • Video – Does not need high resolution, but rather 4K video at a descent frame rate.

The verdict? The megapixel amount really depends on the core photographic application.


So where does that leave us? Pondering a lot of information, most of which the average photographer may not be that interested in. Selecting the appropriate megapixel size is really based on what a camera will be used for. If you commonly take landscape photographs that are used in large scale posters, then 61 or 102 megapixels is certainly not out of the ballpark. For the average photographer taking travel photos, or for someone taking images for the web, or book publishing, then 16MP (or 24MP at the higher end) is ample. That’s why smartphone cameras do so well at 12MP. High MP cameras are really made more for professionals. Nobody needs 61MP.

The voverall erdict? Most photographers don’t need 61 megapixels. In reality anywhere between 16 and 24 megapixels is just fine.

Further Reading

Photosites – Quantum efficiency

Not every photo that makes it through the lens ends up in a photosite. The efficiency with which photosites gather incoming light photons is called its quantum efficiency (QE). The ability to gather light is determined by many factors including the micro lenses, sensor structure, and photosite size. The QE value of a sensor is a fixed value that depends largely on the chip technology of the sensor manufacturer. The QE is averaged out over the entire sensor, and is expressed as the chance that a photon will be captured and converted to an electron.

Quantum efficiency (P = Photons per μm2, e = electrons)

The QE is a fixed value and is dependent on a sensor manufacturers design choices. The QE is averaged out over the entire sensor. A sensor with an 85% QE would produce 85 electrons of signal if it were exposed to 100 photons. There is no way to effect the QE of a sensor, i.e. you can’t change things by changing the ISO.

The QE is typically 30-55% meaning 30-55% of the photons that fall on any given photosite are converted to electrons. (front illuminated sensors). In back illuminated sensors, like those typically found on smartphones, the QE is approximately 85%. The website Photons to Photos has a list of sensor characteristics for a good number of cameras. For example the sensor in my Olympus OM-D E-M5 Mark II has a supposed QE of 60%. Trying to calculate the QE of a sensor in non-trivial.

Fixing the “crop-factor” issue

We use the term “cropped sensor” only due to the desire to describe a sensor in terms of the 35mm standard. It is a relative term which compares two different types of sensor, but it isn’t really that meaningful. Knowing that a 24mm MFT lens “behaves” like a 48mm full-frame lens is pointless if you don’t understand how a 48mm lens behaves on a full-frame camera. All sensors could be considered “full-frame” in the context of their environment, i.e. a MFT camera has a full-frame sensor as it relates to the MFT standard.

As mentioned in a previous post, the “35mm equivalence” is used to relate a crop-factor lens to its full-frame equivalent. The biggest problem with this is the amount of confusion it creates for novice photographers. Especially as focal lengths on lenses are always the same, yet the angle-of-view changes according to the sensor. However there is a solution to the problem, and that is to stop using the focal length to define a lens, and instead use AOV. This would allow people to pick a lens based on its angle-of view, both in degrees, but also from a descriptive point of view. For example, a wide angle lens in full-frame is 28mm – its equivalent in APS-C in 18mm, and MFT is 14mm. It would be easier just to label these by the AOV as “wide-74°”.

It would be easy to categorize lenses into six core groups based on horizontal AOV (diagonal AOV in []) :

  • Ultra-wide angle: 73-104° [84-114°]
  • Wide-angle: 54-73° [63-84°]
  • Normal (standard): 28-54° [34-63°]
  • Medium telephoto: 20-28° [24-34°]
  • Telephoto: 6-20° [8-24°]
  • Super-telephoto: 3-6° [4-8°]
Lenses could be advertised using a graphic to illustrate the AOV (horizontal) of the lens. This effectively removes the need to talk about focal length.

They are still loosely based on how AOV related to 35mm focal lengths. For example 63° relates to the AOV of a 35mm lens, however it no longer really relates to the focal length directly. A “normal-40°” lens would be 40° no matter the sensor size, even though the focal lengths would be different (see table below). The only lenses left out of this are fish-eye lenses, which in reality are not that common, and could be put into a
specialty lens category, along with tilt-shift etc.

Instead of brochures containing focal lengths they could contain the AOV’s.

I know most lens manufacturers describe AOV using diagonal AOV, but this is actually more challenging for people to perceive, likely because looking through a camera we generally look at a scene from side-to-side, not corner-to-corner.

Wide/ultra-wide angle lenses

Normal lenses

Telephoto lenses

The effect of crop sensors on lenses

Lenses used on crop-sensor cameras are a little different to those of full-frame cameras. Mostly this has to do with size – because the sensor is smaller, the image circle doesn’t need to be as large, and therefore less glass is needed in their construction. This allows crop-sensor lenses to be more compact, and lighter. The benefit is that for lenses like telephoto, a smaller size lens is required. A 300mm FF equivalent in MFT only needs to be 150mm. But what does focal-length equivalency mean?

Focal-Length Equivalency

The most visible effect of crop-sensors on lenses is the angle-of-view (AOV), which is essentially where the term crop comes from – the smaller sensor’s AOV is a crop of the full frame. Take a photograph with two cameras: one with a full-frame and another with an APS-C sensor, from the same position using lens with the same focal lengths. The camera with the APS-C sensor will have a more narrowed AOV. For example a 35mm lens on a FF camera has the same focal length as a FF on an MFT or APS-C camera, however the AOV will be different on each. An example of this is shown in Fig.1 for a 35mm lens (showing horizontal AOV).

Fig.1: AOV for 35mm lenses on FF, APS-C, and MFT

Now it should be made clear that none of this affects the focal length of the lens. The focal length of a lens remains the same – regardless of the sensor on the camera. Therefore a 50mm lens in FF, APS-C or MFT will always have a focal length of 50mm. What changes is the AOV of each of the lenses, and consequently the FOV. In order to obtain the same AOV on a cropped-sensor camera, a new lens with the appropriate focal length must be chosen.

Manufacturers of crop-sensors like to use the term “equivalent focal length“. Now this is the focal length AOV as it relates to full-frame. So Olympus says that a MFT lens with a focal length of 17mm has a 34mm FF equivalency. It has an AOV of 65° (diagonal, as per the lens specs), and a horizontal AOV of 54°. Here’s how we calculate those (21.64mm is the diagonal of the MFT sensor, which is 17.3×13mm in size):

  • 17mm MFT lens → 2*arctan(21.64/(2*17)) = 65° (diag)
  • 17mm MFT lens → 2*arctan(17.3/(2*17)) = 54° (hor)
  • 34mm FF lens → 2*arctan(36/(2*34)) = 55.8° (hor)

So a lens with a 17mm focal length on a camera with a 2.0× crop factor MFT sensor would give an AOV equivalent of to that of a 34mm lens. An APS-C sensor has a crop factor of ×1.5, so a 26mm lens would be required to give an AOV equivalent of the 34mm FF lens. Figure 2 depicts the differences between 50mm FF and APS-C lenses, and the similarities between a 50mm FF lens and a 35mm APS-C lens (which give approximately the same AOV/FOV).

Fig.2: Example of lens equivalencies: FF vs. APS-C (×1.5)

Interchangeability of Lenses

On a side note, FF lenses can be used on crop-sensor cameras because the image circle of the FF lens is larger than the crop sensor. The reverse is however not possible, as a CS lens has a smaller image circle than a FF sensor. The picture below illustrates the various combinations of FF/MFT sensor cameras, and FF/MFT lenses.

Fig.3:The effect of interchanging lenses between FF and crop sensor cameras.

Of course all this is pointless if you don’t care about comparing your crop-sensor camera to a full-frame camera.

NOTE: I tend to use horizontal AOV rather than the manufacturers more typical diagonal AOV. It makes more sense because I am generally viewing a scene in a horizontal context.

From photosites to pixels (iii) – DIP

DIP is the Digital Image Processing system. Once the ADC has performed its conversion, each of the values from the photosite has been converted from a voltage to a binary number representing some value in its bit depth. So basically you have a matrix of integers representing each of the original photosites. The problem is that this is essentially a matrix of grayscale values, with each element of the matrix representing with a Red, Green of Blue pixel (basically a RAW image). If a RAW image is required, then no further processing is performed, the RAW image and its associated metadata are saved in a RAW image file format. However to obtain a colour RGB image and store it as a JPEG, further processing must be performed.

First it is necessary to perform a task called demosaicing (or demosaiking, or debayering). Demosaicing separates the red, green, and blue elements of the Bayer image into three distinct R, G, and B components. Note a colouring filtering mechanism other than Bayer may be used. The problem is that each of these layers is sparse – the green layer contains 50% green pixels, and the remainder are empty. The red and blue layers only contain 25% of red and blue pixels respectively. Values for the empty pixels are then determined using some form of interpolation algorithm. The result is an RGB image containing three layers representing red, green and blue components for each pixel in the image.

The DIP process

Next any processing related to settings in the camera are performed. For example, the Ricoh GR III has two options for noise reduction: Slow Shutter Speed NR, and High-ISO Noise Reduction. In a typical digital camera there are image processing settings such as grain effect, sharpness, noise reduction, white balance etc. (which don’t affect RAW photos). Some manufacturers also add additional effects such as art effect filters, and film simulations, which are all done within the DIP processor. Finally the RGB image image is processed to allow it to be stored as a JPEG. Some level of compression is applied, and metadata is associated with the image. The JPEG is then stored on the memory card.

From photosites to pixels (ii) – ADC

The inner workings of a camera are much more complex than most people care to know about, but everyone should have a basic understanding of how digital photographs are created.

The ADC is the Analog-to-Digital Converter. After the exposure of a picture ends, the electrons captured in each photosite are converted to a voltage. The ADC takes this analog signal as input, and classifies it into a brightness level represented by a binary number. The output from the ADC is sometimes called an ADU, or Analog-to-Digital Unit, which is a dimensionless unit of measure. The darker regions of a photographed scene will correspond to a low count of electrons, and consequently a low ADU value, while brighter regions correspond to higher ADU values.

Fig. 1: The ADC process

The value output by the ADC is limited by its resolution (or bit-depth). This is defined as the smallest incremental voltage that can be recognized by the ADC. It is usually expressed as the number of bits output by the ADC. For example a full-frame sensor with a resolution of 14 bits can convert a given analog signal to one of 214 distinct values. This means it has a tonal range of 16384 values, from 0 to 16,383 (214-1). An output value is computed based on the following formula:

ADU = (AVM / SV) × 2R

where AVM is the measured analog voltage from the photosite, SV is the system voltage, and R is the resolution of the ADC in bits. For example, for an ADC with a resolution of 8 bits, if AVM=2.7, SV=5.0, and 28, then ADU=138.

Resolution (bits)Digitizing stepsDigital values
Dynamic ranges of ADC resolution

The process is roughly illustrated in Figure 1. using a simple 3-bit, system with 23 values, 0 to 7. Note that because discrete numbers are being used to count and sample the analog signal, a stepped function is used instead of a continuous one. The deviations the stepped line makes from the linear line at each measurement is the quantization error. The process of converting from analog to digital is of course subject to some errors.

Now it’s starting to get more complicated. There are other things involved, like gain, which is the ratio applied while converting the analog voltage signal to bits. Then there is the least significant bit, which is the smallest change in signal that can be detected.

Photosites – Well capacity

When photons (light) enter a lens of a camera, some of them will pass through all the way to the sensor, and some of those photons will pass through various layers (e.g. filters) and end up in being gathered in the photosite. Each photosite on a sensor has a capacity associated with it. This is normally known as the photosite well capacity (sometimes called the well depth, or saturation capacity). It is a measure of the amount of light that can be recorded before the photosite becomes saturated (no long able to collect any more photons).

When photons hit the photo-receptive photosite, they are converted to electrons. The more photons that hit a photosite, the more the photosite cavity begins to fill up. After the exposure has ended, the amount of electrons in each photosite is read, and the photosite is cleared to prepare for the next frame. The number of electrons counted determines the intensity value of that pixel in the resulting image. The gathered electrons create a voltage which is an analog signal -the more photons that strike a photosite, the higher the voltage.

More light means a greater response from the photosite. At some point the photosite will not be able to register any more light because it is at capacity. Once a photosite is full, it cannot hold any more electrons, and any further incoming photons are discarded, and lost. This means the photosite has become saturated.

Fig.1: Well-depth illustrated with P representing photons, and e- representing electrons.

Different sensors can have photosites with different well-depths, which affects how many electrons the photosite can hold. For example consider two photosites from different sensors. One has a well-depth of 1000 electrons, and the other 500 electrons. If everything remains constant from the perspective of camera settings, noise etc., then over an exposure time the photosite with the smaller well-depth will fill to capacity sooner. If over the course of an exposure 750 photons are converted to electrons in each of the photosites, then the photosite with a well-depth of 1000 will be 75% capacity, and the photosite with a well-depth of 500 will become saturated, discarding 250 of the photons (see Figure 2).

Fig.2: Different well capacities exposed to 750 photons

Two photosite cavities with the same well-capacities, but differing size (in μm) will also affect how quickly the cavity fills up with electrons. The larger sized photosite will fill up quicker. Figure 3 shows four differing sensors, each with a different photosite pitch, and well capacity (the area of each box abstractly represents the well capacity of the photosite in relation to the photosite pitch).

Fig.3: Examples of well capacity in various sensors

Of course the reality is that electrons do not need a physical “bin” to be stored in, the photosites are just shown in this manner to illustrate a concept. In fact the concept of well-depth is somewhat ill-termed, as it does not take into account the surface area of the photosite.

What is a crop factor?

The crop factor of a sensor is the ratio of one camera’s sensor size in relation to another camera’s sensor of a different size. The term is most commonly used to represent the ratio between a 35mm full-frame sensor and a crop sensor. The term was coined to help photographers understand how existing lenses would perform on new digital cameras which had sensors smaller than the 35mm film format.

How to calculate crop factors?

It is easy to calculate a crop factor using the size of a crop-sensor in relation to a full-frame sensor. This is usually determined by comparing diagonals, i.e. full-frame sensor diagonal/cropped sensor diagonal. The diagonals can be calculated using Pythagorean Theorem. Calculate the diagonal of the crop-sensor, and divide this into the diagonal of a full-frame sensor, which is 43.27mm.

Here is an example of deriving the crop factor for a MFT sensor (17.3×13mm):

  1. The diagonal of a full-frame sensor is √(36²+24²) = 43.27mm
  2. The diagonal of the MFT sensor is √(17.3²+13²) = 21.64mm
  3. The crop factor is 43.27/21.64 = 2.0

This means a scene photographed with a MFT sensor will be smaller by a factor or 2 than a FF sensor, i.e. it will have half the physical size in dimensions.

Common crop factors

TypeCrop factor
APS-C (Canon)1.6
APS-C (Fujifilm Nikon, Ricoh, Sony, Pentax)1.5
APS-H (defunct)1.35
35mm full frame1.0
Medium format (Fuji GFX)0.8

Below is a visual depiction of these crop sensors compared to the 1× of the full-frame sensor.

The various crop-factors per crop-sensor.

How are crop factors used?

The term crop factor is often called the focal length multiplier. That is because it is often used to calculate the “full-frame equivalent” focal length of a lens on a camera with a cropped sensor. For example, a MFT sensor has a crop factor of 2.0. So taking a MFT 25mm lens, and multiplying it by 2.0 gives 50mm. This means that a 25mm lens on a MFT camera would behave more like a 50mm lens on a FF camera, in terms of AOV, and FOV. If a 50mm mounted on a full-frame camera is placed next to a 25mm mounted on a MFT camera, and both cameras were the same distance from the subject, they would yield photographs with similar FOVs. They would not be identical of course, because they have different focal lengths which modifies characteristics such as perspective and depth-of-field.

Things to remember

  • The crop-factor is a value which relates the size of a crop-sensor to a full-frame sensor.
  • The crop-factor does not affect the focal length of a lens.
  • The crop-factor does not affect the aperture of a lens.

The low-down on crop sensors

Before the advent of digital cameras, the standard reference format for photography was 35mm film, with frames 24×36mm in size. Everything in analog photography had the same frame of reference (well except for medium format, but let’s ignore that). In the early development of digital sensors, there were cost and technological issues with developing a sensor the same size as 35mm film. The first commercially available dSLR, the Nikon QV-1000C, released in 1988, had a ⅔” sensor with a crop-factor of 4. The first full-frame dSLR would not appear until 2002, the Contax N Digital, sporting 6 megapixels.

Using a camera with a sensor smaller presented one significant problem – the field of view of images captured using these sensors was narrower than the reference 35mm standard. When camera manufacturers started creating sensors smaller than 24×36mm, they had to create a term which described them in relation to a 35mm film-frame (full-frame). For that reason the term crop sensor is used to describe a sensor that is some percentage smaller than a full-frame sensor (sometimes the term cropped is used interchangeably). The picture a crop sensor creates is “cropped” in relation to the picture created with a full-frame sensor (using the lenses with the same focal length). The sensor does not actually cut anything, it’s just that parts of the image are simply ignored. To illustrate what happens in a full-frame versus a cropped sensor, consider Fig.1.

Fig.1: A visual depiction of full-frame versus crop sensor in relation to the 35mm image circle.

Lenses project a circular image, the “image circle”, but a sensor only records a rectangular portion of the scene. A full-frame sensor, like the one from the Leica SL2 captures a large portion of the 35mm lens circle, whereas the Micro-Four-Thirds cropped sensor of the Olympus OM-D E-M1, only captures the central portion of the lens – the rest of the image falls outside the scope of the sensor (the FF sensor is shown as a dashed box). While crop-sensor lenses are smaller than those of full-frame cameras, there are limits to reducing their size from the perspective of optics, and light capture. Fig.2 shows another perspective on crop sensors based on a real scene, comparing a full-frame sensor to an APS-C sensor (assuming the same “size” lenses, say 50mm).

Fig.2: Viewing full-frame versus crop (APS-C)

The benefits of crop-sensors

  • Crop-sensors are smaller than full-frame sensors, therefore the cameras are generally smaller. This means cameras are generally smaller in dimensions and weigh less.
  • The cost of crop-sensor cameras, and the cost of their lenses is generally lower than FF.
  • A smaller size of lens is required. For example, a MFT camera only requires a 150mm lens to achieve the equivalent of a 300mm FF lens, in terms of field-of-view.

The limitations of crop-sensors

  • Lenses on a crop-sensor camera with the same focal-length as those on a full-frame camera will generally have a smaller AOV. For example a FF 50mm lens will have an AOV=39.6°, while a APS-C 50mm lens would have an AOV=26.6°. To get a similar AOV on the cropped sensor APS-C, a 33mm equivalent lens would have to be used.
  • A cropped sensor captures less of the lens image circle than a full-frame.
  • A cropped sensor captures less light than a full-frame (which has larger photosites which are more sensitive to light).

Common crop-sensors

A list of the most common crop-sensor sizes currently used in digital cameras, as well as the average sensor sizes (sensors from different manufacturers can differ by as much as 0.5mm in size), and example cameras is summarized in Table 1. A complete list of sensor sizes can be found here. Smartphones are in a league of their own, and usually have small sensors of the type 1/n”. For example the Apple iPhone 12 Pro max has 4 cameras – the tele camera uses a 1/3.4″ (4.23×3.17mm) sensor, and the tele camera a 1/3.6″ sensor (4×3mm).

TypeExample Cameras
1/2.3″6.16×4.62mmSony HX99, Panasonic Lumix DC-ZS80, Nikon Coolpix P950
1″13.2×8.8mmCanon Powershot G7X M3, Sony X100 VII
MFT / m43 17.3×13mmPanasonic Lumix DC-G95, Olympus OM-D E-M1 Mark III
APS-C (Canon)23.3×14.9mmCanon EOS M50 Mark II
APS-C 23.5×15.6mmRicoh GRIII, Fuji X-E3, Sony α6600, Sigma sd Quattro
35mm Full Frame 36×24mmSigma fpL, Canon EOS R5, Sony α, Leica SL2-S, Nikon Z6II
Medium format44×33mmFuji GFX 100
Table 1: Crop sensor sizes.

Figure 3 shows the relative sizes of three of the more common crop sensors: APS-C (Advanced Photo System type-C), MFT (Micro-Four-Thirds), and 1″, as compared to a full-frame sensor. The APS-C sensor size is modelled on the Advantix film developed by Kodak, where the Classic image format had a size of 25.1×16.7mm.

Fig.3: Examples of crop-sensors versus a full-frame sensor.

Defunct crop-sensors

Below is a list of sensors which are basically defunct, usually because they are not currently being used in any new cameras.

TypeSensor sizeExample Cameras
1/1.7″7.53×5.64mmNikon Coolpix P340 (2014), Olympus Stylus 1 (2013), Leica C (2013)
2/3″8.8×6.6mmFujifilm FinePix X10 (2011)
APS-C Foveon 20.7×13.8mmSigma DP series (2006-2011)
APS-H Foveon26.6×17.9mmSigma sd Quattro H (2016)
APS-H27×18mmLeica M8(2006), Canon EOS 1D Mark IV (2009)
Table 2: Defunct crop sensor sizes.

From photosites to pixels (i) – the process

We have talked briefly about digital camera sensors work from the perspective of photosites, and digital ISO, but what happens after the light photons are absorbed by the photosites on the sensor? How are image pixels created? This series of posts will try and demystify some of the inner workings of a digital camera, in a way that is understandable.

A camera sensor is typically made up of millions of cavities called photosites (not pixels, they are not pixels until they are transformed from analog to digital values). A 24MP sensor has 24 million photosites, typically arranged in the form of a matrix, 6000 pixels wide by 4000 pixel high. Each photosite has a single photodiode which records a luminance value. Light photons enter the lens and pass through the lens aperture before a portion of light is allowed through to the camera sensor when the shutter is activated at the start of the exposure. Once the photons hit the sensor surface they pass through a micro-lens attached to the receiving surface of each of the photosites, which helps direct the photons into the photosite, and then through a colour filter (e.g. Bayer), used to help determine the colour of pixel in an image. A red filter allows red light to be captured, green allows green to be captured and blue allow blue light in.

Every photosite holds a specific number of photons (sometimes called the well depth). When the exposure is complete, the shutter closes, and the photodiode gathers the photons, converting them into an electrical charge, i.e. electrons. The strength of the electrical signal is based on how many photons were captured by the photosite. This signal then passes through the ISO amplifier, which makes adjustments to the signal based on ISO settings. The ISO uses a conversion factor, “M” (Multiplier) to multiply the tally of electrons based on the ISO setting of the camera. For higher ISO, M will be higher, requiring fewer electrons.

Photosite to pixel

The analog signal then passes on to the ADC, which is a chip that performs the role of analog-to-digital converter. The ADC converts the analog signals into discrete digital values (basically pixels). It takes the analog signals as input, and classifies them into a brightness level (basically a matrix of pixels). The darker regions of a photographed scene will correspond to a low count of electrons, and consequently a low ADU value, while brighter regions correspond to higher ADU values. At this point the image can follow one (or both) of two paths. If the camera is set to RAW, then information about the image, e.g. camera settings, etc. (the metadata) is added and the image is saved in RAW format to the memory card. If the setting is RAW+JPEG, or JPEG, then some further processing may be performed by way of the DIP system.

The “pixels” passes to the DIP system, short for Digital Image Processing. Here demosaicing is applied, which basically converts the pixels in the matrix into an RGB image. Other image processing techniques can also be applied based on particular camera settings, e.g. image sharpening, noise reduction, etc. is basically an image. The colour space specified in the camera is applied, before the image as well as its associated meta-data is converted to JPEG format and saved on the memory card.

Summary: A number of photons absorbed by a photosite during exposure time creates a number of electrons which form a charge that is converted by a capacitor to a voltage which is then amplified, and digitized resulting in a digital grayscale value. Three layers of these grayscale values form the Red, Green, and Blue components of a colour image.