Tag Archives: DR

How does a Raw Image Get Rendered?

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 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

Continue reading How does a Raw Image Get Rendered?

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

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.

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

Engineering Dynamic Range in Photography

Dynamic Range (DR) in Photography usually refers to the working tone 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

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

Comparing Sensor SNR

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


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

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