Tag Archives: ISO

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

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Opening Raspberry Pi High Quality Camera Raw Files

The Raspberry Pi Foundation recently released an interchangeable lens camera module based on the Sony  IMX477, a 1/2.3″ back side illuminated sensor with 3040×4056 pixels of 1.55um pitch.  In this somewhat technical article we will unpack the 12-bit raw still data that it produces and render it in a convenient color space.

still life raw capture data file raspberry pi high quality hq cam f/8 1/2s base analog gain iso adobe rgb
Figure 1. 12-bit raw capture by Raspberry Pi High Quality Camera with 16 mm kit lens at f/8, 1/2 s, base ISO. The image was loaded into Matlab and rendered Half Height Nearest Neighbor in the Adobe RGB color space with a touch of local contrast and sharpening.  Click on it to see it in its own tab and view it at 100% magnification. If your browser is not color managed you may not see colors properly.

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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.

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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

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

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