Tag Archives: FWC

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

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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

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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.

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IQ, Sensors

Sensor IQ’s Simple Model

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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.

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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.

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