Category Archives: IQ

Pi HQ Cam Sensor Performance

Now that we know how to open 12-bit raw files captured with the new Raspberry Pi High Quality Camera, we can learn a bit more about the capabilities of its 1/2.3″ Sony IMX477 sensor from a keen photographer’s perspective.  The subject is a bit dry, so I will give you the summary upfront.  These figures were obtained with my HQ module at room temperature and the raspistill – -raw (-r) command:

Raspberry Pi
HQ Camera
raspistill
--raw -ag 1
Comments
Black Level256.3 DN256.0 - 257.3 based on gain
White Level4095Constant throughout
Analog Gain1Gain Range 1 - 16
Read Noise3 e-, gain 1
1.5 e-, gain 16
1.53 DN from black frame
11.50 DN
Clipping (FWC)8180 e-at base gain, 3400e-/um^2
Dynamic Range11.15 stops
11.3 stops
SNR = 1 to Clipping
Read Noise to Clipping
System Gain0.47 DN/e-at base analog gain
Star Eater AlgorithmPartly DefeatableAll channels - from base gain and from min shutter speed
Low Pass FilterYesAll channels - from base gain and from min shutter speed

Continue reading Pi HQ Cam Sensor Performance

DOF and Diffraction: 24mm Guidelines

After an exhausting two and a half hour hike you are finally resting, sitting on a rock at the foot of your destination, a tiny alpine lake, breathing in the thin air and absorbing the majestic scenery.  A cool light breeze suddenly rips the surface of the water, morphing what has until now been a perfect reflection into an impressionistic interpretation of the impervious mountains in the distance.

The beautiful flowers in the foreground are so close you can touch them, the reflection in the water 10-20m away, the imposing mountains in the background a few hundred meters further out.  You realize you are hungry.  As you search the backpack for the two panini you prepared this morning you begin to ponder how best to capture the scene: subject,  composition, Exposure, Depth of Field.

Figure 1. A typical landscape situation: a foreground a few meters away, a mid-ground a few tens and a background a few hundred meters further out.  Three orders of magnitude.  The focus point was on the running dog, f/16, 1/100s.  Was this a good choice?

Depth of Field.  Where to focus and at what f/stop?  You tip your hat and just as you look up at the bluest of blue skies the number 16 starts enveloping your mind, like rays from the warm noon sun. You dial it in and as you squeeze the trigger that familiar nagging question bubbles up, as it always does in such conditions.  If this were a one shot deal, was that really the best choice?

In this article we attempt to provide information to make explicit some of the trade-offs necessary in the choice of Aperture for 24mm landscapes.  The result of the process is a set of guidelines.  The answers are based on the previously introduced diffraction-aware model for sharpness in the center along the depth of the field – and a tripod-mounted Nikon Z7 + Nikkor 24-70mm/4 S kit lens at 24mm.
Continue reading DOF and Diffraction: 24mm Guidelines

DOF and Diffraction: Setup

The two-thin-lens model for precision Depth Of Field estimates described in the last two articles is almost ready to be deployed.  In this one we will describe the setup that will be used to develop the scenarios that will be outlined in the next one.

The beauty of the hybrid geometrical-Fourier optics approach is that, with an estimate of the field produced at the exit pupil by an on-axis point source, we can generate the image of the resulting Point Spread Function and related Modulation Transfer Function.

Pretend that you are a photon from such a source in front of a f/2.8 lens focused at 10m with about 0.60 microns of third order spherical aberration – and you are about to smash yourself onto the ‘best focus’ observation plane of your camera.  Depending on whether you leave exactly from the in-focus distance of 10 meters or slightly before/after that, the impression you would leave on the sensing plane would look as follows:

Figure 1. PSF of a lens with about 0.6um of third order spherical aberration focused on 10m.

The width of the square above is 30 microns (um), which corresponds to the diameter of the Circle of Confusion used for old-fashioned geometrical DOF calculations with full frame cameras.  The first ring of the in-focus PSF at 10.0m has a diameter of about 2.44\lambda \frac{f}{D} = 3.65 microns.   That’s about the size of the estimated effective square pixel aperture of the Nikon Z7 camera that we are using in these tests.
Continue reading DOF and Diffraction: Setup

The Nikon Z7’s Insane Sharpness

Ever since getting a Nikon Z7 MILC a few months ago I have been literally blown away by the level of sharpness it produces.   I thought that my surprise might be the result of moving up from 24 to 45.7MP, or the excellent pin-point focusing mode, or the lack of an Antialiasing filter.  Well, it turns out that there is probably more at work than that.

This weekend I pulled out the largest cutter blade I could find and set it up rough and tumble near vertically about 10 meters away  to take a peek at what the MTF curves that produce such sharp results might look like.

Continue reading The Nikon Z7’s Insane Sharpness

Canon’s High-Res Optical Low Pass Filter

Canon recently introduced its EOS-1D X Mark III Digital Single-Lens Reflex [Edit: and now also possibly the R5 Mirrorless ILC] touting a new and improved Anti-Aliasing filter, which they call a High-Res Gaussian Distribution LPF, claiming that

“This not only helps to suppress moiré and color distortion,
but also improves resolution.”

Figure 1. Artist’s rendition of new High-res Low Pass Filter, courtesy of Canon USA

In this article we will try to dissect the marketing speak and understand a bit better the theoretical implications of the new AA.  For the abridged version, jump to the Conclusions at the bottom.  In a picture:

Canon High-Res Anti-Aliasing filter
Figure 16: The less psychedelic, the better.

Continue reading Canon’s High-Res Optical Low Pass Filter

The HV Spectrogram

A spectrogram, also sometimes referred to as a periodogram, is  a visual representation of the Power Spectrum of a signal.  Power Spectrum answers the question “How much power is contained in the frequency components of the signal”. In digital photography a Power Spectrum can show the relative strength of repeating patterns in captures and whether processing has been applied.

In this article I will describe how you can construct a spectrogram and how to interpret it, using dark field raw images taken with the lens cap on as an example.  This can tell us much about the performance of our imaging devices in the darkest shadows and how well tuned their sensors are there.

Pixel level noise spectrum
Figure 1. Horizontal and Vertical Spectrogram of noise captured in the raw data by a Nikon Z7 at base ISO with  the lens cap on.  The plot shows clear evidence of low-pass filtering in the blue CFA color plane and pattern noise repeating every 6 rows there and in one of the green ones.

Continue reading The HV Spectrogram

Capture Sharpening: Estimating Lens PSF

The next few articles will outline the first tiny few steps towards achieving perfect capture sharpening, that is deconvolution of an image by the Point Spread Function (PSF) of the lens used to capture it.  This is admittedly  a complex subject, fraught with a myriad ever changing variables even in a lab, let alone in the field.  But studying it can give a glimpse of the possibilities and insights into the processes involved.

I will explain the steps I followed and show the resulting images and measurements.  Jumping the gun, the blue line below represents the starting system Spatial Frequency Response (SFR)[1], the black one unattainable/undesirable perfection and the orange one the result of part of the process outlined in this series.

Figure 1. Spatial Frequency Response of the imaging system before and after Richardson-Lucy deconvolution by the PSF of the lens that captured the original image.

Continue reading Capture Sharpening: Estimating Lens PSF

The Perfect Color Filter Array

We’ve seen how humans perceive color in daylight as a result of three types of photoreceptors in the retina called cones that absorb wavelengths of light from the scene with different sensitivities to the arriving spectrum.

Figure 1.  Quantitative Color Science.

A photographic digital imager attempts to mimic the workings of cones in the retina by usually having different color filters arranged in an array (CFA) on top of its photoreceptors, which we normally call pixels.  In a Bayer CFA configuration there are three filters named for the predominant wavelengths that each lets through (red, green and blue) arranged in quartets such as shown below:

Figure 2.  Bayer Color Filter Array: RGGB  layout.  Image under license from Cburnett, pixels shifted and text added.

A CFA is just one way to copy the action of cones:  Foveon for instance lets the sensing material itself perform the spectral separation.  It is the quality of the combined spectral filtering part of the imaging system (lenses, UV/IR, CFA, sensing material etc.) that determines how accurately a digital camera is able to capture color information from the scene.  So what are the characteristics of better systems and can perfection be achieved?  In this article I will pick up the discussion where it was last left off and, ignoring noise for now, attempt to answer this  question using CIE conventions, in the process gaining insight in the role of the compromise color matrix and developing a method to visualize its effects.[1]  Continue reading The Perfect Color Filter Array

Phase One IQ3 100MP Trichromatic vs Standard Back Linear Color, Part III

Over the last two posts we’ve been exploring some of the differences introduced by tweaks to the Color Filter Array of the Phase One IQ3 100MP Trichromatic Digital Back versus its original incarnation, the Standard Back.  Refer to those for the background.  In this article we will delve into some of these differences quantitatively[1].

Let’s start with the compromise color matrices we derived from David Chew’s captures of a ColorChecher 24 in the shade of a sunny November morning in Ohio[2].   These are the matrices necessary to convert white balanced raw data to the perceptual CIE XYZ color space, where it is said there should be one-to-one correspondence with colors as perceived by humans, and therefore where most measurements are performed.  They are optimized for each back in the current conditions but they are not perfect, the reason for the word ‘compromise’ in their name:

Figure 1. Optimized Linear Compromise Color Matrices for the Phase One IQ3 100 MP Standard and Trichromatic Backs under approximately D65 light.

Continue reading Phase One IQ3 100MP Trichromatic vs Standard Back Linear Color, Part III

Phase One IQ3 100MP Trichromatic vs Standard Back Linear Color, Part I

It is always interesting when innovative companies push the envelope of the state-of-the-art of a single component in their systems because a lot can be learned from before and after comparisons.   I was therefore excited when Phase One introduced a Trichromatic version of their Medium Format IQ3 100MP Digital Back last September because it could allows us to isolate the effects of tweaks to their Bayer Color Filter Array, assuming all else stays the same.

Figure 1. IQ3 100MP Trichromatic (left) vs the rest (right), from PhaseOne.com.   Units are not specified but one would assume that the vertical axis is relative spectral sensitivity and the horizontal axis represents wavelength.

Thanks to two virtually identical captures by David Chew at getDPI, and Erik Kaffehr’s intelligent questions at DPR, in the following articles I will explore the effect on linear color of the new Trichromatic CFA (TC) vs the old one on the Standard Back (SB).  In the process we will discover that – within the limits of my tests, procedures and understanding[1] – the Standard Back produces apparently more ‘accurate’ color while the Trichromatic produces better looking matrices, potentially resulting in ‘purer’ signals. Continue reading Phase One IQ3 100MP Trichromatic vs Standard Back Linear Color, Part I

Bayer CFA Effect on Sharpness

In this article we shall find that the effect of a Bayer CFA on the spatial frequencies and hence the ‘sharpness’ information captured by a sensor compared to those from the corresponding monochrome version can go from (almost) nothing to halving the potentially unaliased range – based on the chrominance content of the image and the direction in which the spatial frequencies are being stressed. Continue reading Bayer CFA Effect on Sharpness

Taking the Sharpness Model for a Spin – II

This post  will continue looking at the spatial frequency response measured by MTF Mapper off slanted edges in DPReview.com raw captures and relative fits by the ‘sharpness’ model discussed in the last few articles.  The model takes the physical parameters of the digital camera and lens as inputs and produces theoretical directional system MTF curves comparable to measured data.  As we will see the model seems to be able to simulate these systems well – at least within this limited set of parameters.

The following fits refer to the green channel of a number of interchangeable lens digital camera systems with different lenses, pixel sizes and formats – from the current Medium Format 100MP champ to the 1/2.3″ 18MP sensor size also sometimes found in the best smartphones.  Here is the roster with the cameras as set up:

Table 1. The cameras and lenses under test.

Continue reading Taking the Sharpness Model for a Spin – II

A Simple Model for Sharpness in Digital Cameras – Sampling & Aliasing

Having shown that our simple two dimensional MTF model is able to predict the performance of the combination of a perfect lens and square monochrome pixel with 100% Fill Factor we now turn to the effect of the sampling interval on spatial resolution according to the guiding formula:

(1)   \begin{equation*} MTF_{Sys2D} = \left|(\widehat{ PSF_{lens} }\cdot \widehat{PIX_{ap} })\right|_{pu}\ast\ast\: \delta\widehat{\delta_{pitch}} \end{equation*}

The hats in this case mean the Fourier Transform of the relative component normalized to 1 at the origin (_{pu}), that is the individual MTFs of the perfect lens PSF, the perfect square pixel and the delta grid;  ** represents two dimensional convolution.

Sampling in the Spatial Domain

While exposed a pixel sees the scene through its aperture and accumulates energy as photons arrive.  Below left is the representation of, say, the intensity that a star projects on the sensing plane, in this case resulting in an Airy pattern since we said that the lens is perfect.  During exposure each pixel integrates (counts) the arriving photons, an operation that mathematically can be expressed as the convolution of the shown Airy pattern with a square, the size of effective pixel aperture, here assumed to have 100% Fill Factor.  It is the convolution in the continuous spatial domain of lens PSF with pixel aperture PSF shown in Equation (2) of the first article in the series.

Sampling is then the product of an infinitesimally small Dirac delta function at the center of each pixel, the red dots below left, by the result of the convolution, producing the sampled image below right.

Footprint-PSF3
Figure 1. Left, 1a: A highly zoomed (3200%) image of the lens PSF, an Airy pattern, projected onto the imaging plane where the sensor sits. Pixels shown outlined in yellow. A red dot marks the sampling coordinates. Right, 1b: The sampled image zoomed at 16000%, 5x as much, because in this example each pixel’s width is 5 linear units on the side.

Continue reading A Simple Model for Sharpness in Digital Cameras – Sampling & Aliasing

A Simple Model for Sharpness in Digital Cameras – I

The next few posts will describe a linear spatial resolution model that can help a photographer better understand the main variables involved in evaluating the ‘sharpness’ of photographic equipment and related captures.   I will show numerically that the combined spectral frequency response (MTF) of a perfect AAless monochrome digital camera and lens in two dimensions can be described as the magnitude of the normalized product of the Fourier Transform (FT) of the lens Point Spread Function by the FT of the pixel footprint (aperture), convolved with the FT of a rectangular grid of Dirac delta functions centered at each  pixel:

    \[ MTF_{2D} = \left|\widehat{ PSF_{lens} }\cdot \widehat{PIX_{ap} }\right|_{pu}\ast\ast\: \delta\widehat{\delta_{pitch}} \]

With a few simplifying assumptions we will see that the effect of the lens and sensor on the spatial resolution of the continuous image on the sensing plane can be broken down into these simple components.  The overall ‘sharpness’ of the captured digital image can then be estimated by combining the ‘sharpness’ of each of them.

The stage will be set in this first installment with a little background and perfect components.  Later additional detail will be provided to take into account pixel aperture and Anti-Aliasing filters.  Then we will look at simple aberrations.  Next we will learn how to measure MTF curves for our equipment, and look at numerical methods to model PSFs and MTFs from the wavefront at the aperture. Continue reading A Simple Model for Sharpness in Digital Cameras – I

A Longitudinal CA Metric for Photographers

While perusing Jim Kasson’s excellent Longitudinal Chromatic Aberration tests[1] I was impressed by the quantity and quality of the information the resulting data provides.  Longitudinal, or Axial, CA is a form of defocus and as such it cannot be effectively corrected during raw conversion, so having a lens well compensated for it will provide a real and tangible improvement in the sharpness of final images.  How much of an improvement?

In this article I suggest one such metric for the Longitudinal Chromatic Aberrations (LoCA) of a photographic imaging system: Continue reading A Longitudinal CA Metric for Photographers

COMBINING BAYER CFA MTF Curves – II

In this and the previous article I present my thoughts on how MTF50 results obtained from  raw data of the four Bayer CFA channels – off  a uniformly illuminated neutral target captured with a typical digital camera through the slanted edge method – can be combined to provide a meaningful composite MTF50 for the imaging system as a whole1.  Corrections, suggestions and challenges are welcome. Continue reading COMBINING BAYER CFA MTF Curves – II

Combining Bayer CFA Modulation Transfer Functions – I

In this and the following article I will discuss my thoughts on how MTF50 results obtained from  raw data of the four Bayer CFA color channels off  a neutral target captured with a typical camera through the slanted edge method can be combined to provide a meaningful composite MTF50 for the imaging system as a whole.   The perimeter of the discussion are neutral slanted edge measurements of Bayer CFA raw data for linear spatial resolution  (‘sharpness’) photographic hardware evaluations.  Corrections, suggestions and challenges are welcome. Continue reading Combining Bayer CFA Modulation Transfer Functions – I

Linearity in the Frequency Domain

For the purposes of ‘sharpness’ spatial resolution measurement in photography  cameras can be considered shift-invariant, linear systems.

Shift invariant means that the imaging system should respond exactly the same way no matter where light from the scene falls on the sensing medium .  We know that in a strict sense this is not true because for instance a pixel has a square area so it cannot have an isotropic response by definition.  However when using the slanted edge method of linear spatial resolution measurement  we can effectively make it shift invariant by careful preparation of the testing setup.  For example the edges should be slanted no more than this and no less than that. Continue reading Linearity in the Frequency Domain

Sub Bit Signal

My camera has a 14-bit ADC.  Can it accurately record information lower than 14 stops below full scale? Can it store sub-LSB signals in the raw data?

With a well designed sensor the answer, unsurprisingly if you’ve followed the last few posts, is yes it can.  The key to being able to capture such tiny visual information in the raw data is a well behaved imaging system with a properly dithered ADCContinue reading Sub Bit Signal

Smooth Gradients and the Weber-Fechner Fraction

Whether the human visual system perceives a displayed slow changing gradient of tones, such as a vast expanse of sky, as smooth or posterized depends mainly on two well known variables: the Weber-Fechner Fraction of the ‘steps’ in the reflected/produced light intensity (the subject of this article); and spatial dithering of the light intensity as a result of noise (the subject of a future one).

Continue reading Smooth Gradients and the Weber-Fechner Fraction

Information Transfer: Non ISO-Invariant Case

We’ve seen how information about a photographic scene is collected in the ISOless/invariant range of a digital camera sensor, amplified, converted to digital data and stored in a raw file.  For a given Exposure the best information quality (IQ) about the scene is available right at the photosites, only possibly degrading from there – but a properly designed** fully ISO invariant imaging system is able to store it in its entirety in the raw data.  It is able to do so because the information carrying capacity (photographers would call it the dynamic range) of each subsequent stage is equal to or larger than the previous one.   Cameras that are considered to be (almost) ISOless from base ISO include the Nikon D7000, D7200 and the Pentax K5.  All digital cameras become ISO invariant above a certain ISO, the exact value determined by design compromises.

ToneTransferISOless100
Figure 1: Simplified Scene Information Transfer in an ISO Invariant Imaging System at base ISO

In this article we’ll look at a class of imagers that are not able to store the whole information available at the photosites in one go in the raw file for a substantial portion of their working ISOs.  The photographer can in such a case choose out of the full information available at the photosites what smaller subset of it to store in the raw data by the selection of different in-camera ISOs.  Such cameras are sometimes improperly referred to as ISOful. Most Canon DSLRs fall into this category today.  As do kings of darkness such as the Sony a7S or Nikon D5.

Continue reading Information Transfer: Non ISO-Invariant Case

Image Quality: Raising ISO vs Pushing in Conversion

In the last few posts I have made the case that Image Quality in a digital camera is entirely dependent on the light Information collected at a sensor’s photosites during Exposure.  Any subsequent processing – whether analog amplification and conversion to digital in-camera and/or further processing in-computer – effectively applies a set of Information Transfer Functions to the signal  that when multiplied together result in the data from which the final photograph is produced.  Each step of the way can at best maintain the original Information Quality (IQ) but in most cases it will degrade it somewhat.

IQ: Only as Good as at Photosites’ Output

This point is key: in a well designed imaging system** the final image IQ is only as good as the scene information collected at the sensor’s photosites, independently of how this information is stored in the working data along the processing chain, on its way to being transformed into a pleasing photograph.  As long as scene information is properly encoded by the system early on, before being written to the raw file – and information transfer is maintained in the data throughout the imaging and processing chain – final photograph IQ will be virtually the same independently of how its data’s histogram looks along the way.

Continue reading Image Quality: Raising ISO vs Pushing in Conversion

The Difference Between Data and Information

In photography, digital cameras capture information about the scene carried by photons reflected by it and store the information as data in a raw file pretty well linearly.  Data is the container, scene information is the substance.  There may or may not be information in the data, no matter what its form.  With a few limitations what counts is the substance, information, not the form, data.

A Simple Example

Imagine for instance that you are taking stock of the number of remaining pieces in your dinner place settings.  You originally had a full set of 6 of everything but today, after many years of losses and breakage, this is the situation in each category: Continue reading The Difference Between Data and Information

Information Transfer – The ISO Invariant Case

We know that the best Information Quality possible collected from the scene by a digital camera is available right at the output of the sensor and it will only be degraded from there.  This article will discuss what happens to this information as it is transferred through the imaging system and stored in the raw data.  It will use the simple language outlined in the last post to explain how and why the strategy for Capturing the best Information or Image Quality (IQ) possible from the scene in the raw data involves only two simple steps:

1) Maximizing the collected Signal given artistic and technical constraints; and
2) Choosing what part of the Signal to store in the raw data and what part to leave behind.

The second step is only necessary  if your camera is incapable of storing the entire Signal at once (that is it is not ISO invariant) and will be discussed in a future article.  In this post we will assume an ISOless imaging system.

Continue reading Information Transfer – The ISO Invariant Case

Information Theory for Photographers

Ever since Einstein we’ve been able to say that humans ‘see’ because information about the scene is carried to the eyes by photons reflected by it.  So when we talk about Information in photography we are referring to information about the energy and distribution of photons arriving from the scene.   The more complete this information, the better we ‘see’.  No photons = no information = no see; few photons = little information = see poorly = poor IQ; more photons = more information = see better = better IQ.

Sensors in digital cameras work similarly, their output ideally being the energy and location of every photon incident on them during Exposure. That’s the full information ideally required to recreate an exact image of the original scene for the human visual system, no more and no less. In practice however we lose some of this information along the way during sensing, so we need to settle for approximate location and energy – in the form of photoelectron counts by pixels of finite area, often correlated to a color filter array.

Continue reading Information Theory for Photographers

How Many Bits to Fully Encode My Image

My camera sports a 14 stop Engineering Dynamic Range.  What bit depth do I need to safely fully encode all of the captured tones from the scene with a linear sensor?  As we will see the answer is not 14 bits because that’s the eDR, but it’s not too far from that either – for other reasons, as information science will show us in this article.

When photographers talk about grayscale ‘tones’ they typically refer to the number of distinct gray levels present in a displayed image.  They don’t want to see distinct levels in a natural slow changing gradient like a dark sky: if it’s smooth they want to perceive it as smooth when looking at their photograph.  So they want to make sure that all possible tonal  information from the scene has been captured and stored in the raw data by their imaging system.

Continue reading How Many Bits to Fully Encode My Image

Dynamic Range and Bit Depth

My camera has an engineering Dynamic Range of 14 stops, how many bits do I need to encode that DR?  Well, to encode the whole Dynamic Range 1 bit could suffice, depending on the content and the application.  The reason is simple, dynamic range is only concerned with the extremes, not with tones in between:

    \[ DR = \frac{Maximum Signal}{Minimum Signal} \]

So in theory we only need 1 bit to encode it: zero for minimum signal and one for maximum signal, like so

Continue reading Dynamic Range and Bit Depth

Engineering Dynamic Range in Photography

Dynamic Range (DR) in Photography usually refers to the linear working signal range, from darkest to brightest, that the imaging system is capable of capturing and/or displaying.  It is expressed as a ratio, in stops:

    \[ DR = log_2(\frac{Maximum Acceptable Signal}{Minimum Acceptable Signal}) \]

It is a key Image Quality metric because photography is all about contrast, and dynamic range limits the range of recordable/ displayable tones.  Different components in the imaging system have different working dynamic ranges and the system DR is equal to the dynamic range of the weakest performer in the chain.

Continue reading Engineering Dynamic Range in Photography

Downsizing Algorithms: Effects on Resolution

Most of the photographs captured these days end up being viewed on a display of some sort, with at best 4K (4096×2160) but often no better than HD resolution (1920×1080).  Since the cameras that capture them have typically several times that number of pixels, 6000×4000 being fairly normal today, most images need to be substantially downsized for viewing, even allowing for some cropping.  Resizing algorithms built into browsers or generic image viewers tend to favor expediency over quality, so it behooves the IQ conscious photographer to manage the process, choosing the best image size and downsampling algorithm for the intended file and display medium.

When downsizing the objective is to maximize the original spatial resolution retained while minimizing the possibility of aliasing and moirè.  In this article we will take a closer look at some common downsizing algorithms and their effect on spatial resolution information in the frequency domain.

Continue reading Downsizing Algorithms: Effects on Resolution

Are micro Four Thirds Lenses Typically Twice as ‘Sharp’ as Full Frame’s?

In fact the question is more generic than that.   Smaller format lens designers try to compensate for their imaging system geometric resolution penalty  (compared to a larger format when viewing final images at the same size) by designing ‘sharper’ lenses specifically for it, rather than recycling larger formats’ designs (feeling guilty APS-C?) – sometimes with excellent effect.   Are they succeeding?   I will use mFT only as an example here, but input is welcome for all formats, from phones to large format.

Continue reading Are micro Four Thirds Lenses Typically Twice as ‘Sharp’ as Full Frame’s?

Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 2

There are several ways to extract Sensor IQ metrics like read noise, Full Well Count, PRNU, Dynamic Range and others from mean and standard deviation statistics obtained from a uniform patch in a camera’s raw file.  In the last post we saw how to do it by using such parameters to make observed data match the measured SNR curve.  In this one we will achieve the same objective by fitting mean and  standard deviation data.  Since the measured data is identical, if the fit is good so should be the results.

Sensor Metrics from Measured Mean and Standard Deviation in DN

Continue reading Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 2

Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 1

We’ve seen how to model sensors and how to collect signal and noise statistics from the raw data of our digital cameras.  In this post I am going to pull both things together allowing us to estimate sensor IQ metrics: input-referred read noise, clipping/saturation/Full Well Count, Dynamic Range, Pixel Response Non-Uniformities and gain/sensitivity.

There are several ways to extract these metrics from signal and noise data obtained from a camera’s raw file.  I will show two related ones: via SNR in this post and via total noise N in the next.  The procedure is similar and the results are identical.

Continue reading Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 1

Sensor IQ’s Simple Model

Imperfections in an imaging system’s capture process manifest themselves in the form of deviations from the expected signal.  We call these imperfections ‘noise’ because they introduce grain and artifacts in our images.   The fewer the imperfections, the lower the noise, the higher the image quality.

However, because the Human Visual System is adaptive within its working range, it’s not the absolute amount of noise that matters to perceived Image Quality (IQ) as much as the amount of noise relative to the signal – represented for instance by the Signal to Noise Ratio (SNR). That’s why to characterize the performance of a sensor in addition to signal and noise we also need to determine its sensitivity and the maximum signal it can detect.

In this series of articles I will describe how to use the Photon Transfer method and a spreadsheet to determine basic IQ performance metrics of a digital camera sensor.  It is pretty easy if we keep in mind the simple model of how light information is converted into raw data by digital cameras:

Sensor photons to DN A
Figure 1.

Continue reading Sensor IQ’s Simple Model

Can MTF50 be Trusted?

A reader suggested that a High-Res Olympus E-M5 Mark II image used in the previous post looked sharper than the equivalent Sony a6000 image, contradicting the relative MTF50 measurements, perhaps showing ‘the limitations of MTF50 as a methodology’.   That would be surprising because MTF50 normally correlates quite well with perceived sharpness, so I decided to check this particular case out.

‘Who are you going to believe, me or your lying eyes’?

Continue reading Can MTF50 be Trusted?

Olympus E-M5 II High-Res 64MP Shot Mode

Olympus just announced the E-M5 Mark II, an updated version of its popular micro Four Thirds E-M5 model, with an interesting new feature: its 16MegaPixel sensor, presumably similar to the one in other E-Mx bodies, has a high resolution mode where it gets shifted around by the image stabilization servos during exposure to capture, as they say in their press release

‘resolution that goes beyond full-frame DSLR cameras.  8 images are captured with 16-megapixel image information while moving the sensor by 0.5 pixel steps between each shot. The data from the 8 shots are then combined to produce a single, super-high resolution image, equivalent to the one captured with a 40-megapixel image sensor.’

A great idea that could give a welcome boost to the ‘sharpness’ of this handy system.  Preliminary tests show that the E-M5 mk II 64MP High-Res mode gives some advantage in MTF50 linear spatial resolution compared to the Standard Shot 16MP mode with the captures in this post.  Plus it apparently virtually eliminates the possibility of  aliasing and moiré.  Great stuff, Olympus.

Continue reading Olympus E-M5 II High-Res 64MP Shot Mode

Equivalence in Pictures: Sharpness/Spatial Resolution

So, is it true that a Four Thirds lens needs to be about twice as ‘sharp’ as its Full Frame counterpart in order to be able to display an image of spatial resolution equivalent to the larger format’s?

It is, because of the simple geometry I will describe in this article.  In fact with a few provisos one can generalize and say that lenses from any smaller format need to be ‘sharper’ by the ratio of their sensor diagonals in order to produce the same linear resolution on same-sized final images.

This is one of the reasons why Ansel Adams shot 4×5 and 8×10 – and I would too, were it not for logistical and pecuniary concerns.

Continue reading Equivalence in Pictures: Sharpness/Spatial Resolution

The Units of Spatial Resolution

Several sites for photographers perform spatial resolution ‘sharpness’ testing of a specific lens and digital camera set up by capturing a target.  You can also measure your own equipment relatively easily to determine how sharp your hardware is.  However comparing results from site to site and to your own can be difficult and/or misleading, starting from the multiplicity of units used: cycles/pixel, line pairs/mm, line widths/picture height, line pairs/image height, cycles/picture height etc.

This post will address the units involved in spatial resolution measurement using as an example readings from the popular slanted edge method, although their applicability is generic.

Continue reading The Units of Spatial Resolution

How to Measure the SNR Performance of Your Digital Camera

Determining the Signal to Noise Ratio (SNR) curves of your digital camera at various ISOs and extracting from them the underlying IQ metrics of its sensor can help answer a number of questions useful to photography.  For instance whether/when to raise ISO;  what its dynamic range is;  how noisy its output could be in various conditions; or how well it is likely to perform compared to other Digital Still Cameras.  As it turns out obtaining the relative data is a little  time consuming but not that hard.  All you need is your camera, a suitable target, a neutral density filter, dcraw or libraw or similar software to access the linear raw data – and a spreadsheet.

Continue reading How to Measure the SNR Performance of Your Digital Camera

Comparing Sensor SNR

We’ve seen how SNR curves can help us analyze digital camera IQ:

SNR-Photon-Transfer-Model-D610-4

In this post we will use them to help us compare digital cameras, independently of format size. Continue reading Comparing Sensor SNR

SNR Curves and IQ in Digital Cameras

In photography the higher the ratio of Signal to Noise, the less grainy the final image normally looks.  The Signal-to-Noise-ratio SNR is therefore a key component of Image Quality.  Let’s take a closer look at it. Continue reading SNR Curves and IQ in Digital Cameras

The Difference between Peak and Effective Quantum Efficiency

Effective Quantum Efficiency as I calculate it is an estimate of the probability that a visible photon  – from a ‘Daylight’ blackbody radiating source at a temperature of 5300K impinging on the sensor in question after making it through its IR filter, UV filter, AA low pass filter, microlenses, average Color Filter – will produce a photoelectron upon hitting silicon:

(1)   \begin{equation*} EQE = \frac{n_{e^-} \text{ produced by average pixel}}{n_{ph} \text{ incident on average pixel}} \end{equation*}

with n_{e^-} the signal in photoelectrons and n_{ph} the number of photons incident on the sensor at the given Exposure as shown below. Continue reading The Difference between Peak and Effective Quantum Efficiency

Equivalence and Equivalent Image Quality: Signal

One of the fairest ways to compare the performance of two cameras of different physical characteristics and specifications is to ask a simple question: which photograph would look better if the cameras were set up side by side, captured identical scene content and their output were then displayed and viewed at the same size?

Achieving this set up and answering the question is anything but intuitive because many of the variables involved, like depth of field and sensor size, are not those we are used to dealing with when taking photographs.  In this post I would like to attack this problem by first estimating the output signal of different cameras when set up to capture Equivalent images.

It’s a bit long so I will give you the punch line first:  digital cameras of the same generation set up equivalently will typically generate more or less the same signal in e^- independently of format.  Ignoring noise, lenses and aspect ratio for a moment and assuming the same camera gain and number of pixels, they will produce identical raw files. Continue reading Equivalence and Equivalent Image Quality: Signal

How to Get MTF Performance Curves for Your Camera and Lens

You have obtained a raw file containing the image of a slanted edge  captured with good technique.  How do you get the Modulation Transfer Function of the camera and lens combination that took it?  Download and feast your eyes on open source MTF Mapper version 0.4.16 by Frans van den Bergh.

[Edit 2023: MTF Mapper has kept improving over the years, making it in my opinion the most accurate slanted edge measuring tool available today, used in applications that range from photography to machine vision to the Mars Rover.   Did I mention that it is open source?

It now sports a Graphical User Interface which can load raw files and allow the arbitrary selection of individual edges by simply pointing and clicking, making this post largely redundant.  The procedure outlined below will still work but there are easier ways to accomplish the same task today: just “File/Open single edge image” raw files from the GUI after having inserted “–bayer green” in the additional string field.  Thanks Frans.]

The first thing we are going to do is crop the edges and package them into a TIFF file format so that MTF Mapper has an easier time reading them.  Let’s use as an example a Nikon D810+85mm:1.8G ISO 64 studio raw capture by DPReview so that you can follow along if you wish.   Continue reading How to Get MTF Performance Curves for Your Camera and Lens

The Slanted Edge Method

My preferred method for measuring the spatial resolution performance of photographic equipment these days is the slanted edge method.  It requires a minimum amount of additional effort compared to capturing and simply eye-balling a pinch, Siemens or other chart but it gives immensely more, useful, accurate, quantitative information in the language and units that have been used to characterize optical systems for over a century: it produces a good approximation to  the Modulation Transfer Function of the two dimensional camera/lens system impulse response – at the location of the edge in the direction perpendicular to it.

Much of what there is to know about an imaging system’s spatial resolution performance can be deduced by analyzing its MTF curve, which represents the system’s ability to capture increasingly fine detail from the scene, starting from perceptually relevant metrics like MTF50, discussed a while back.  In fact the area under the curve weighted by some approximation of the Contrast Sensitivity Function of the Human Visual System is the basis for many other, better accepted single figure ‘sharpness‘ metrics with names like Subjective Quality Factor (SQF), Square Root Integral (SQRI), CMT Acutance, etc.   And all this simply from capturing the image of a slanted edge, which one can actually and somewhat easily do at home, as presented in the next article.

Continue reading The Slanted Edge Method

Why Raw Sharpness IQ Measurements Are Better

Why Raw?  The question is whether one is interested in measuring the objective, quantitative spatial resolution capabilities of the hardware or whether instead one would prefer to measure the arbitrary, qualitatively perceived sharpening prowess of (in-camera or in-computer) processing software as it turns the capture into a pleasing final image.  Either is of course fine.

My take on this is that the better the IQ captured the better the final image will be after post processing.  In other words I am typically more interested in measuring the spatial resolution information produced by the hardware comfortable in the knowledge that if I’ve got good quality data to start with its appearance will only be improved in post by the judicious use of software.  By IQ here I mean objective, reproducible, measurable physical quantities representing the quality of the information captured by the hardware, ideally in scientific units.

Can we do that off a file rendered by a raw converter or, heaven forbid, a Jpeg?  Not quite, especially if the objective is measuring IQ. Continue reading Why Raw Sharpness IQ Measurements Are Better

How Sharp are my Camera and Lens?

You want to measure how sharp your camera/lens combination is to make sure it lives up to its specs.  Or perhaps you’d like to compare how well one lens captures spatial resolution compared to another  you own.  Or perhaps again you are in the market for new equipment and would like to know what could be expected from the shortlist.  Or an old faithful is not looking right and you’d like to check it out.   So you decide to do some testing.  Where to start?

In the next four articles I will walk you through my methodology based on captures of slanted edge targets:

  1. The setup (this one)
  2. Why you need to take raw captures
  3. The Slanted Edge method explained
  4. The software to obtain MTF curves

Continue reading How Sharp are my Camera and Lens?

Exposure and ISO

The in-camera ISO dial is a ballpark milkshake of an indicator to help choose parameters that will result in a ‘good’ perceived picture. Key ingredients to obtain a ‘good’ perceived picture are 1) ‘good’ Exposure and 2) ‘good’ in-camera or in-computer processing. It’s easier to think about them as independent processes and that comes naturally to you because you shoot raw in manual mode and you like to PP, right? Continue reading Exposure and ISO

What Radius to Use for Deconvolution Capture Sharpening

The following approach will work if you know the spatial frequency at which a certain MTF relative energy level (e.g. MTF50) is achieved by your camera/lens combination as set up at the time that the capture was taken.

The process by which our hardware captures images and stores them  in the raw data inevitably blurs detail information from the scene. Continue reading What Radius to Use for Deconvolution Capture Sharpening

Deconvolution vs USM Capture Sharpening

UnSharp Masking (USM) capture sharpening is somewhat equivalent to taking a black/white marker and drawing along every transition in the picture to make it stand out more – automatically.  Line thickness and darkness is chosen arbitrarily to achieve the desired effect, much like painters do. Continue reading Deconvolution vs USM Capture Sharpening