Tag Archives: dynamic range

IQ, Raspberry Pi, Sensors

Pi HQ Cam Sensor Performance

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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 →

adobe camera raw, Color, LR, Processing, Sharpening

How Is a Raw Image Rendered?

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What are the basic low level steps involved in raw file conversion?  In this article I will discuss what happens under the hood of digital camera raw converters in order to turn raw file data into a viewable image, a process sometimes referred to as ‘rendering’.  We will use the following raw capture by a Nikon D610 to show how image information is transformed at every step along the way:

Nikon D610 with AF-S 24-120mm f/4 lens at 24mm f/8 ISO100, minimally rendered from raw as outlined in the article.
Figure 1. Nikon D610 with AF-S 24-120mm f/4 lens at 24mm f/8 ISO100, minimally rendered from raw by Octave/Matlab following the steps outlined in the article.

Rendering = Raw Conversion + Editing

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Information Science, IQ, Sensors

Information Transfer: Non ISO-Invariant Case

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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 →

Information Science, IQ, Quantization, Sensors

How Many Bits to Fully Encode My Image

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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.

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IQ, Sensors

Dynamic Range and Bit Depth

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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 →

IQ, Sensors

Engineering Dynamic Range in Photography

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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.

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IQ, Sensors

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

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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 →

IQ, Sensors

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

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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 →

Exposure, IQ, Sensors, Uncategorized

How to Measure the SNR Performance of Your Digital Camera

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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 →

Equivalence, Exposure, Formats, IQ, Sensors

Comparing Sensor SNR

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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 →

IQ, Sensors

SNR Curves and IQ in Digital Cameras

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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 →

Equivalence, Exposure, IQ, Sensors

Equivalence and Equivalent Image Quality: Signal

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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 →