More myths about travel photography

December 7, 2021 / spqr / Leave a comment

Below are some more myths associated with travel.

MYTH 13: Landscape photographs need good light.

REALITY: In reality there is no such thing as bad light, or bad weather, unless it is pouring. You can never guarantee what the weather will be like anywhere, and if you are travelling to places like Scotland, Iceland, or Norway the weather can change on the flip of a coin. There can be a lot of drizzle, or fog. You have to learn to make the most the situation, exploiting any kind of light.

MYTH 14: Manual exposure produces the best images.

REALITY: Many photographers use aperture-priority, or the oft-lauded P-mode. If you think something will be over- or under-exposed, then use exposure-bracketing. Modern cameras have a lot of technology to deal with taking optimal photographs, so don’t feel bad about using it.

MYTH 15: The fancy camera features are cool.

REALITY: No, they aren’t. Sure, try the built-in filters. They may be fun for a bit, but filters can always be added later. If you want to add filters, try posting to Instagram. For example, high-resolution mode is somewhat fun to play with, but it will eat battery life.

MYTH 16: One camera is enough.

REALITY: I never travel with less than two cameras, a primary, and a secondary, smaller camera, one that fits inside a jacket pocket easily (in my case a Ricoh GR III). There are risks when you are somewhere on vacation and your main camera stops working for some reason. A backup is always great to have, both for breakdowns, lack of batteries, or just for shooting in places where you don’t want to drag a bigger camera along, or would prefer a more inconspicuous photographic experience, e.g. museums, art galleries.

MYTH 17: More megapixels are better.

REALITY: I think optimally, anything from 16-26 megapixels is good. You don’t need 50MP unless you are going to print large posters, and 12MP likely is not enough these days.

MYTH 18: Shooting in RAW is the best.

REALITY: Probably, but here’s the thing, for the amateur, do you want to spend a lot of time post-processing photos? Maybe not? Setting the camera to JPEG+RAW is the best of both worlds. There is the issue of JPEG editing being destructive and RAW not.

MYTH 19: Backpacks offer the best way of carrying equipment.

REALITY: This may be true getting equipment from A to B, but schlepping a backpack loaded with equipment around every day during the summer can be brutal. No matter the type, backpacks + hot weather = a sweaty back. They also make you stand out, just as much as a FF camera with a 300mm lens. Opt instead for a camera sling, such as one from Peak Design. It has a much lower form factor and with a non-FF camera offers enough space for the camera, an extra lens, and a few batteries and memory cards. I’m usually able to shove in the secondary camera as well. They make you seem much more incognito as well.

MYTH 20: Carrying a film-camera is cumbersome.

REALITY: Film has made a resurgence, and although I might not carry one of my Exakta cameras, I might throw a half-frame camera in my pack. On a 36-roll film, this gives me 72 shots. The film camera allows me to experiment a little, but not at the expense of missing a shot.

MYTH 21: Travel photos will be as good as those in photo books.

REALITY: Sadly not. You might be able to get some good shots, but the reality is those shots in coffee-table photo books, and on TV shows are done with much more time than the average person has on location, and with the use of specialized equipment like drones. You can get some awesome imagery with drones, especially for video, because they can get perspectives that a person on the ground just can’t. If you spend an hour at a place you will have to deal with the weather that exists – someone who spends 2-3 days can wait for optimal conditions.

MYTH 22: If you wait long enough, it will be less busy.

REALITY: Some places are always busy, especially so it if is a popular landmark. The reality is short of getting up at the crack of dawn, it may be impossible to get a perfect picture. A good example is Piazza San Marco in Venice… some people get a lucky shot in after a torrential downpour, or some similar event that clears the streets, but the best time is just after sunrise, otherwise it is swamped with tourists. Try taking pictures of lesser known things instead of waiting for the perfect moment.

MYTH 23: Unwanted objects can be removed in post-processing.

REALITY: Sometimes popular places are full of tourists… like they are everywhere. In the past it was impossible to remove unwanted objects, you just had to come back at a quieter time. Now there are numerous forms of post-processing software like Cleanup-pictures that will remove things from a picture. A word of warning though, this type of software may not always work perfectly.

MYTH 24: Drones are great for photography.

REALITY: It’s true, drones make for some exceptional photographs, and video footage. You can actually produce aerial photos of scenes like the best professional photographers, from likely the best vantage points. However there are a number of caveats. Firstly, travel drones have to be a reasonable size to actually be lugged about from place to place. This may limit the size of the sensor in the camera, and also the size of the battery. Is the drone able to hover perfectly still? If not, you could end up with somewhat blurry images. Flight time on drones is usually 20-30 minutes, so extra batteries are a requirement for travel. The biggest caveat of course is where you can fly drones. For example in the UK, non-commercial drone use is permitted, however there are no-fly zones, and permission is needed to fly over World heritage sites such as Stonehenge. In Italy a license isn’t required, but drones can’t be used over beaches, towns or near airports.

A review of SKRWT – keystone correction for IOS

December 2, 2021 / spqr / Leave a comment

For a few years now, I have been using SKRWT, an app that does perspective correction in IOS.

The goal was to have some way of quickly fixing issues with perspective, and distortions, in photographs. The most common form of this is the keystone effect (see previous post) which occurs when the image plane is not parallel to the lines that are required to be parallel in the photograph. This usually occurs when taking photographs of buildings where we tilt the camera backwards, in order to include the whole scene. The building appears to be “falling away” from the camera. Fig.1 shows a photograph of a church in Montreal. Notice, the skew as the building seems to tilt backwards.

The process of correcting distortions with SKRWT is easy. Pick an image, and then a series of options are provided in the icon bar below the imported picture. The option that best approximates the types of perspective distortion is selected, and a new window opens, with a grid overlaid upon the image. A slider below the image can be used to select the magnitude of the distortion correction, with the image transformed as the slider is moved. When the image looks geometrically corrected, pressing the tick stores the newly corrected image.

Fig. 2: *The simple process of correcting a vertical distortion*

Using the SKRWT app, the perspective distortion can be fixed, but at a price. The problem is that correcting for the perspective distortion requires distorting the image, which means it will likely be larger than the original, and will need to be cropped (otherwise the image will contain black background regions).

Here is a third example, of Toronto’s flatiron building, with the building surrounded by enough “picture” to allow for corrective changes that don’t cut off any of the main object.

Overall the app is well designed and easy to use. In fact it will remove quite complex distortions, although there is some loss of content in the images processed. To use this, or any similar perspective correction software properly, you really have to frame the building with enough background to allow for corrections – not so you are left with half a building.

The sad thing about this app is something that plagues a lot of apps – it has become a zombie app. The developer was suppose to release version 1.5 in December 2020, but alas nothing has appeared, and the website has had no updates. Zombie apps work while the system they are on works, but upgrade the phone, or OS, and there is every likelihood it will no longer work.

The photography of Daidō Moriyama

November 26, 2021 / spqr / Leave a comment

Daidō Moriyama was born in Ikeda, Osaka, Japan in 1938, and came to photography in the late 1950s. Moriyama studied photography under Takeji Iwamiya before moving to Tokyo in 1961 to work as an assistant to Eikoh Hosoe. In his early 20’s he bought a Canon 4SB and started photographing on the streets on Osaka. Moriyama was the quintessential street photographer focused on the snapshot. Moriyama likened snapshot photography to a cast net – “Your desire compels you to throw it out. You throw the net out, and snag whatever happens to come back – it’s like an ‘accidental moment’” [1]. Moriyama’s advice on street photography was literally “Get outside. It’s all about getting out and walking.” [1]

In the late 1960s Japan was characterized by street demonstrations protesting the Vietnam War and the continuing presence of the US in Japan. Moriyama joined a group of photographers, associated with the short-lived (3-issue) magazine Provoke (1968-69), which really dealt with elements of experimental photography. His most provocative work during the Provoke-era was the are-bure-boke style that illustrates a blazing immediacy. His photographic style is characterized by snapshots which are gritty, grainy black and white, out-of-focus, extreme contrast, Chiaroscuro (dark, harsh spotlighting, mysterious backgrounds). Moriyama is “drawn to black and white because monochrome has stronger elements of abstraction or symbolism, colour is something more vulgar…”.

“My approach is very simple — there is no artistry, I just shoot freely. For example, most of my snapshots I take from a moving car, or while running, without a finder, and in those instances I am taking the pictures more with my body than my eye… My photos are often out of focus, rough, streaky, warped etc. But if you think about I, a normal human being will in one day receive an infinite number of images, and some are focused upon, other are barely seen out of the corners of one’s eye.”

Moriyama is an interesting photographer, because he does not focus on the camera (or its make), instead shoots with anything, a camera is just a tool. He photographs mostly with compact cameras, because with street photography large cameras tend to make people feel uncomfortable. There were a number of cameras which followed the Canon 4SB, including a Nikon S2 with a 25/4, Rolleiflex, Minolta Autocord, Pentax Spotmatic, Minolta SR-2, Minolta SR-T 101 and Olympus Pen W. One of Moriyama’s favourite film camera’s was the Ricoh GR series, using a Ricoh GR1 with a fixed 28mm lens (which appeared in 1996) and sometimes a Ricoh GR21 for a wider field of view (21mm). Recently he was photographing with a Ricoh GR III.

“I’ve always said it doesn’t matter what kind of camera you’re using – a toy camera, a polaroid camera, or whatever – just as long as it does what a camera has to do. So what makes digital cameras any different?”

Yet Moriyama’s photos are made in the post-processing stage. He captures the snapshot on the street and then makes the photo in the darkroom (or in Silver Efex with digital). Post-processing usually involves pushing the blacks and whites, increasing contrast and adding grain. In his modern work it seems as though Moriyama photographs in colour, and converts to B&W in post-processing (see video below). It is no wonder that Moriyama is considered by some to be the godfather of street photography, saying himself that he is “addicted to cities“.

“[My] photos are often out of focus, rough, streaky, warped, etc. But if you think about it, a normal human being will in one day perceive an infinite number of images, and some of them are focused upon, others are barely seen out of the corner of one’s eye.”

For those interested, there are a number of short videos. The one below shows Moriyama in his studio and takes a walk around the atmospheric Shinjuku neighbourhood, his home from home in Tokyo. There is also a longer documentary called Daidō Moriyama: Near Equal, and one which showcases some of his photographs, Daido Moriyama – Godfather of Japanese Street Photography.

Artist Daido Moriyama – In Pictures | Tate (2012)

Removing unwanted objects from pictures with Cleanup.pictures

November 18, 2021 / spqr / Leave a comment

Ever been on vacation somewhere, and wanted to take a picture of something, only to be thwarted by the hordes of tourists? Typically for me it’s buildings of architectural interest, or wide-angle photos in towns. It’s quite a common occurrence, especially in places where tourists tend to congregate. There aren’t many choices – if you can come back at a quieter time that may be the best approach, but often you are at a place for a limited time-frame. So what to do?

Use software to remove the offending objects, or people. Now this type of algorithm designed to remove objects from an image has been around for about 20 years, known in the early years as digital inpainting, akin to the conservation process where damaged, deteriorating, or missing parts of an artwork are filled in to present a complete image. In its early forms digital inpainting algorithms worked well in scenes where the object to be removed was surrounded by fairly uniform background, or pattern. In complex scenes they often didn’t fair so well. So what about the newer generation of these algorithms?

There are many different types of picture cleaning software, some stand-alone such as the AI-powered IOS app Inpaint, others in the form of features in photo processing software such as Photoshop. One new-comer to the scene is web-based, open-source, Cleanup.pictures. It is incredibly easy to use. Upload a picture, choose the size of the brush tool, paint over the unwanted object with the brush tool, and voila! a new image, sans the offending object. Then you can just download the “cleaned” image. So how well does it work? Below are some experiments.

The first image is a vintage photograph of Paris, removing all the people from the streets. The results are actually quite exceptional.

The second image is a photograph taken in Glasgow, where the people and passing car have been erased.

The third image is from a trip to Norway, specifically the harbour in Bergen. This area always seems to have both people and boats, so it is hard to take clear pictures of the historical buildings.

The final image is a photograph taken by Prokudin-Gorskii Collection at the Library of Congress. The image is derived from a series of glass plates, and suffers from some of the original glass plates being broken, with missing pieces of glass. The result of cleaning up the image, actually has done a better job than I could ever have imagined.

The AI used in this algorithm is really good at what it does, like *really good*, and it is easy to use. You just keep cleaning up unwanted things until you are happy with the result. The downsides? It isn’t exactly perfect all the time. In regions to be removed where there are fine details you want to retain, they are often removed. Sometimes areas become “soft” because they have to be “created” because they were obscured by objects before – especially prevalent in edge detail. Some examples are shown below:

Creation of detail during inpainting

It only produces low-res images, with a maximum width of 720 pixels. You can upgrade to the Pro version to increase resolution (2K width). It would be interesting to see this algorithm produce large scale cleaned images. There is also the issue of uploading personal photos to a website, although they do make the point of saying that images are discarded once processed.

For those interested in the technology behind the inpainting, it is based on an algorithm known as large mask inpainting, developed by a group at Samsung, and associates [1]. The code can be obtained directly from github for those who really want to play with things.

Suvorov, R., et al. Resolution-robust Large Mask Inpainting with Fourier Convolutions (2022)

Demystifying Colour (viii) : CIE colour model

November 17, 2021 / spqr / Leave a comment

The Commission Internationale de l’Eclairage (French for International Commission on Illumination) , or CIE is an organization formed in 1913 to create international standards related to light and colour. In 1931, CIE introduced CIE1931, or CIEXYZ, a colorimetric colour space created in order to map out all the colours that can be perceived by the human eye. CIEXYZ was based on statistics derived from extensive measurements of human visual perception under controlled conditions.

In the 1920s, colour matching experiments were performed independently by physicists W. David Wright and John Guild, both in England [2]. The experiments were carried out with 7 (Guild) and 10 (Wright) people. Each experiment involved a subject looking through a hole which allowed for a 2° field of view. On one side was a reference colour projected by a light source, while on the other were three adjustable light sources (the primaries were set to R=700.0nm, G=546.1nm, and B=435.8nm.). The observer would then adjust the values of three primary lights until they can produce a colour indistinguishable from a reference light. This was repeated for every visible wavelength. The result of the colour-matching experiments was a table of RGB triplets for each wavelength. These experiments were not about describing colours with qualities like hue and saturation, but rather just attempt to explain how combinations of light appear to be the same colour to most people.

Fig.1: An example of the experimental setup of Guild/Wright

In 1931 CIE amalgamated Wright and Guild’s data and proposed two sets of of colour matching functions: CIE RGB and CIE XYZ. Based on the responses in the experiments, values were plotted to reflect how the average human eye senses the colours in the spectrum, producing three different curves of intensity for each light source to mix all colours of the colour spectrum (Figure 2), i.e. Some of the values for red were negative, and the CIE decided it would be more convenient to work in a colour space where the coefficients were always positive – the XYZ colour matching functions (Figure 3). The new matching functions had certain characteristics: (i) the new functions must always be greater than or equal to zero; (ii) the y function would describe only the luminosity, and (iii) the white-point is where x=y=z=1/3. This produced the CIE XYZ colour space, also known as CIE 1931.

Fig.2: *CIE RGB colour matching function*s

Fig.3: *CIE XYZ colour matching functions*

The CIE XYZ colour space defines a quantitative link between distributions of wavelengths in the electromagnetic visible spectrum, and physiologically perceived colours in human colour vision. The space is based on three fictional primary colours, X, Y, and Z, where the Y component corresponds to the luminance (as a measure of perceived brightness) of a colour. All the visible colours reside inside an open cone-shaped region, as shown in Figure 4. CIE XYZ is then a mathematical generalization of the colour portion of the HVS, which allows us to define colours.

Fig.4: *CIE XYZ colour space* (G denotes the axis of neutral gray).

The luminance in XYZ space increases along the Y axis, starting at 0, the black point (X=Y=Z=0). The colour hue is independent of the luminance, and hence independent of Y. CIE also defines a means of describing hues and saturation, by defining three normalized coordinates: x, y, and z (where x+y+z=1).

x = X / (X+Y+Z)
y = Y / (X+Y+Z)
z = Z / (X+Y+Z)
z = 1 - x - y

The x and y components can then be taken as the chromaticity coordinates, determining colours for a certain luminance. This system is called CIE xyY, because a colour value is defined by the chromaticity coordinates x and y in addition to the luminance coordinate Y. More on this in the next post on chromaticity diagrams.

The RGB colour space is related to XYZ space by a linear coordinate transformation. The RGB colour space is embedded in the XYZ space as a distorted cube (see Figure 5). RGB can be mapped onto XYZ using the following set of equations:

X = 0.41847R - 0.09169G - 0.0009209B
Y = -0.15866R + 0.25243G - 0.0025498B (luminance)
Z = -0.082835R + 0.015708G + 0.17860B

CIEXYZ is non-uniform with respect to human visual perception, i.e. a particular fixed distance in XYZ is not perceived as a uniform colour change throughout the entire colour space. CIE XYZ is often used as an intermediary space in determining a perceptually uniform space such as CIE Lab (or Lab), or CIE LUV (or Luv).

CIE 1976 CIEL*u*v*, or CIELuv, is an easy to calculate transformation of CIE XYZ which is more perceptually uniform. Luv was created to correct the CIEXYZ distortion by distributing colours approximately proportional to their perceived colour difference.
CIE 1976 CIEL*a*b*, or CIELab, is a perceptually uniform colour differences and L* lightness parameter has a better correlation to perceived brightness. Lab remaps the visible colours so that they extend equally on two axes. The two colour components a* and b* specify the colour hue and saturation along the green-red and blue-yellow axes respectively.

In 1964 another set of experiments were done allowing for a 10° field of view, and are known as the CIE 1964 supplementary standard colorimetric observer. CIE XYZ is still the most commonly used reference colour space, although it is slowly being pushed to the wayside by CIE1976. There is a lot of information on CIE XYZ and its derivative spaces. The reader interested in how CIE1931 came about in referred to [1,4]. CIELab is the most commonly used CIE colour space for imaging, and the printing industry.

What is (camera) sensor resolution?

November 5, 2021 / spqr / 3 Comments

What is sensor resolution? It is not the number of photosites on the sensor, that is just photosite count. In reality sensor resolution is a measure of density, usually the number of photosites per some area, e.g. MP/cm². For example a full-frame sensor with 24MP has an area of 36×24mm = 864mm², or 8.64cm². Dividing 24MP by this gives us 2.77 MP/cm². It could also mean the actual area of a photosite, usually expressed in terms of μm².

Such measures are useful in comparing different sensors from the perspective of density, and characteristics such as the amount of light which is absorbed by the photosites. A series of differing sized sensors with the same pixel count (image resolution) will have differing sized photosites and sensor resolutions. For 16 megapixels, a MFT sensor will have 7.1 MP/cm², APS-C 4.4 MP/cm², full-frame 1.85 MP/cm², and medium format 1.1 MP/cm². For the same pixel count, the larger the sensor, the larger the photosite.

*Sensor resolution for the same image resolution, i.e. the same pixel count (e.g. 16MP)*

It can also be used in comparing the same sized sensor. Consider the following three Fujifilm cameras and their associated APS-C sensors (with an area of 366.6mm²):

The X-T30 has 26MP, 6240×4160 photosites on its sensor. The photosite pitch is 3.74µm (dimensions), and the pixel density is 7.08 MP/cm².
The X-T20 has a pixel count of 24.3MP, or 6058×4012 photosites with a photosite pitch is 3.9µm (dimensions), and a pixel density is 6.63 MP/cm².
The X-T10 has a pixel count of 16.3MP, or 4962×3286 photosites with a photosite pitch is 4.76µm (dimensions), and a pixel density is 4.45 MP/cm².

The X-T30 has a higher sensor resolution than both the X-T20 and X-T10. The X-T20 has a higher sensor resolution than the X-T10. The sensor resolution of the X-T30 is 1.61 times as dense as that of the X-T10.

Sometimes different sensors have similar photosite sizes, and similar photosite densities. For example the Leica SL2 (2019), is full-frame 47.3MP sensor with a photosite area of 18.23 µm² and a density of 5.47 MP/cm². The antiquated Olympus PEN E-P1 (2009) is MFT 12MP sensor with a photosite area of 18.32 µm² and a density of 5.47 MP/cm².

How does high-resolution mode work?

November 1, 2021 / spqr / Leave a comment

One of the tricks of modern digital cameras is a little thing called “high-resolution mode” (HRM), which is sometimes called pixel-shift. It effectively boosts the resolution of an image, even though the number of pixels used by the camera’s sensor does not change. It can boost a 24 megapixel image into a 96 megapixel image, enabling a camera to create images at a much higher resolution than its sensor would normally be able to produce.

So how does this work?

In normal mode, using a colour filter array like Bayer, each photosite acquires one particular colour, and the final colour of each pixel in an image is achieved by means of demosaicing. The basic mechanism for HRM works through sensor-shifting (or pixel-shifting) i.e. taking a series of exposures and processing the data from the photosite array to generate a single image.

An exposure is obtained with the sensor in its original position. The exposure provides the first of the RGB components for the pixel in the final image.
The sensor is moved by one photosite unit in one of the four principal directions. At each original array location there is now another photosite with a different colour filter. A second exposure is made, providing the second of the components for the final pixel.
Step 2 is repeated two more times, in a square movement pattern. The result is that there are four pieces of colour data for every array location: one red, one blue, and two greens.
An image is generated with each RGB pixel derived from the data, the green information is derived by averaging the two green values.

No interpolation is required, and hence no demosaicing.

*The basic high-resolution mode process (the arrows represent the direction the sensor shifts)*

In cameras with HRM, it functions using the motors that are normally dedicated to image stabilization tasks. The motors effectively move the sensor by exactly the amount needed to shift the photosites by one whole unit. The shifting moves in such a manner that the data captured includes one Red, one Blue and two Green photosites for each pixel.

There are many benefits to this process:

The total amount of information is quadrupled, with each image pixel using the actual values for the colour components from the correct physical location, i.e. full RGB information, no interpolation required.
Quadrupling the light reaching the sensor (four exposures) should also cut the random noise in half.
False-colour artifacts often arising in the demosaicing process are no longer an issue.

There are also some limitations:

It requires a very steady scene. It doesn’t work well if the camera is on a tripod, yet there is a slight breeze, moving the leaves on a tree.
It can be extremely CPU-intensive to generate a HRM RAW image, and subsequently drain the battery. Some systems, like Fuji’s GFX100 uses off-camera, post-processing software to generate the RAW image.

Here are some examples of the high resolution modes offered by camera manufacturers:

Fujifilm – Cameras like the GFX100 (102MP) have a Pixel Shift Multi Shot mode where the camera moves the image sensor by 0.5 pixels over 16 images and composes a 400MP image (yes you read that right).
Olympus – Cameras like the OM-D E-M5 Mark III (20.4MP), has a High-Resolution Mode which takes 8 shots using 1 and 0.5 pixel shifts, which are merged into a 50MP image.
Panasonic – Cameras like the S1 (24.2MP) have a High-Resolution mode that results in 96MP images. The Panasonic S1R at 47.3MP produces 187MP images.
Pentax – Cameras like the K-1 II (36.4MP) use a Pixel Shift Resolution System II with a Dynamic Pixel Shift Resolution mode (for handheld shooting).
Sony – Cameras like the A7R IV (61MP) uses a Pixel Shift Multi Shooting mode to produce a 240MP image.

Do you need 61 megapixels, or even 102?

October 27, 2021February 21, 2023 / spqr / Leave a comment

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).

Optics

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.

Postscript

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

What is image resolution?

October 20, 2021October 28, 2021 / spqr / Leave a comment

Sometimes a technical term gets used without any thought to its meaning, and before you know it becomes an industry standard. This is the case with the term “image resolution”, which has become the standard means of describing how much detail is portrayed in an image. The problem is that the term resolution can mean different things in photography. In one context it is used in describing the pixel density of devices (in DPI or PPI). For example a screen may have a resolution of 218 ppi (pixels-per-inch), and a smartphone might have a resolution of 460ppi. There is also sensor resolution, which is concerned with photosite density on a sensor based on sensor size. You can see how this can get confusing.

Fig.1: *Image resolution is about detail in the image* *(the image on the right becomes pixelated when enlarged)*

The term image resolution really just refers to the number of pixels in an image, i.e. pixel count. It is usually expressed in terms of two numbers for the number of pixel rows and columns in an image, often known as the linear resolution. For example the Ricoh GR III has an APS-C sensor with a sensor resolution of 6051×4007, or about 24.2 million photosites on the physical sensor. The effective number of pixels in an image derived from the sensor is 6000×4000, or a pixel count of 24 million pixels – this is considered the image resolution. Image resolution can be used in the context of describing a camera in broad context, e.g., the Sony A1 has 50 megapixels, or based on dimensions, “8640×5760”. It is often used in the context of comparing images, e.g. the Sony A1 with 50MP has a higher resolution than the Sony ZV-1 with 20MP. The image resolution of two images is shown in Figure 1 – a high resolution image has more detail than an image with lower resolution.

Fig.2: *Image resolution and sensor size for 24MP.*

Technically when talking about the sensor we are talking about photosites, but image resolution is not about the sensor, it is about the image produced from the sensor. This is because it is challenging to attempt to compare cameras based on photosites, as they all have differing properties, e.g. photosite area. Once the data from the sensor has been transformed into an image, then the photosite data becomes pixels, which are dimensionless entities. Note that the two dimensions representing the image resolution will change depending on the aspect ratio of the sensor. So while a 24MP image on a 3:2 sensor (APS-C) will have dimensions of 6000 and 4000, a full-frame sensor with the same pixel count will have dimensions of roughly 5657×4243.

Fig.3: *Changes in image resolution within different sensors.*

Increasing image resolution does not always mean increasing the linear resolution, or detail in the same amount. For example a 16MP image from 3:2 ratio sensor would produce an image with resolution of 4899×3266. A 24MP images from the same type of sensor would increase the pixel count by 50%, however the vertical and horizontal dimensions would only increase by 20% – so a much lower change in linear resolution. To double the linear resolution would require an increase in resolution to a 64MP image.

Is image resolution the same as sharpness? Not really, this has more to do with an images spatial resolution (this is where the definition of the word resolution starts to betray itself). Sharpness concerns how clearly defined details within images appear, and is somewhat subjective. It’s possible to have a high resolution image that is not sharp, just like its possible to have a low resolution image that has a good amount of acuity. It really depends on the situation it is being viewed in, i.e. back to device pixel density.

Photosites – Quantum efficiency

October 15, 2021 / spqr / Leave a comment

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.

Crafting Pixels

Vintage lenses, photography and digital imagery