Why is it Important to Test in Low-Light?

It is important to test your camera system in environments which reproduce lighting conditions similar to where you intend to use the camera in the real world. Failure to test a camera under low light conditions may lead to overstating the camera’s performance.

Image sensors collect light (signal) into pixel wells, then convert the resulting analog voltage levels into digital numbers. Dark current is where electrons are released from thermal activity which becomes indistinguishable from electrons released via photoresponse. The dark current that exists in uncooled image sensors leads to dark noise. At low light levels, exposures are longer to gather more light, which gives more time for dark-current electrons to be gathered. This leads to dark noise and read noise representing a larger portion of the overall response, which reduces the signal to noise ratio (SNR). Low-light conditions are most challenging for higher resolution sensors with small pixel pitches where the particle and wave nature of light can seriously impact the performance of your camera.

The definition of “low light” depends on the application. For mobile devices (compact camera modules) low light is defined by the IEEE CPIQ standard as 25 lux, which resembles a dimly lit indoor space representative of the worst cases where people expect their phones to function properly. As mobile devices get better in low light scenarios, customer expectations may shift. For security or automotive industries, the levels are much lower based on how dark the outdoor environment may get[1]:


Condition Illumination (Lux)
Sunlight 107527
Full Daylight 10752
Overcast Day


Very Dark Day 107
Low-Light (CPIQ) 25
Twilight 10.8
Deep Twilight 1.08
Full Moon 0.108
Quarter Moon 0.0108
Starlight 0.0011
Overcast Night 0.0001

In order to achieve light levels similar to the darkest scenes, most lab lighting setups that contain fluorescent or LED sources may not be able to be directly dimmed to very low levels, so the dim light levels may be achieved by a variety of methods shown in the following table. Ultra-low-light levels can be achieved by combining these approaches:

Method Light Reduction Notes
Increasing distance (d) Between 1 / d and  1 / d2 Difficult to move large lighting setups, labs have limited available space
Reflecting off Munsel N5 painted walls 0.18 Most labs are painted with these sorts of walls, but rotating lights back and forth may be difficult to perform repeatably without a mechanical motion stage.
Reflecting off black painted walls

Fresnel reflection from beam splitter


Apparatus could impinge FoV and would also require repeatable rotation of lights.
Imatest Low-light filter (mask + neutral density filter) 0.0125 Manually attaches to these LED lights

Eliminating additional sources of light within your lab is also important: Putting felt around doors to block light from other rooms, covering exit signs, covering indicator LEDs and power strips and installing baffling and light traps to block unwanted reflections.

Note: Verifying the low light levels can be challenging as many illuminance meters do not go to extreme low light levels.

Once you can reproduce low-light levels in your lab, you can perform tests to answer a number of questions about your system performance:

  • Does the radiometric calibration for black level successfully reduce the effects of dark current without also eliminating useful signal that might impact the dynamic range of your system?
  • Does the black level compensation work across the range of nominal operating temperatures that your sensor will experience?
  • Does your ISP react appropriately to dark scenes by increasing the sensitivity (gain)?
  • How consistently does your autofocus system (if you have one) perform under low light?
  • What happens to moving objects captured under low light conditions?
  • For security cameras with near-infrared ‘night-vision’ support: does the Infrared cutoff filter engage and disengage at the appropriate light level?
  • Do the applied gamma curve(s) and tone mapping help to boost the perceived quality of the dark scene or assist the observer’s ability to recognize objects in the dark?
  • Does the noise reduction strike the right balance of reducing visual noise without also blurring the appearance of relevant textures (skin and foliage) within the image?

A comprehensive tuning and testing regimen will involve performing a full battery of objective tests in a wide range of lighting conditions. By increasing the range of light levels you can reproduce in your lab, you can provide the most challenging condition in which you can validate the performance of your camera system.

Photo by Tuur Tisseghem from Pexels

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How to Test Dynamic Range:
A step-by-step use case with the Pixel 2 XL


Intro   |   Setup   |   Capture Procedure   |   Analysis   |   Results   |   Conclusion


Google Pixel 2 XL


In this post, we will be using the Contrast Resolution Chart and Imatest Master to measure the dynamic range of a Google Pixel 2 XL. The dynamic range of a camera is the reproducible tonal range in an imaging system. Put simply, it is the range between the darkest black and the brightest white of an image and is typically measured in decibels (dB). It is an important image quality factor in many applications from machine vision to mobile cameras and more recently, automotive camera systems. In this use case, we will be evaluating both the HDR+ (default) and HDR off modes of the Pixel 2 XL; however the procedure can be used to test any camera system’s dynamic range. You can read more details about Imatest’s dynamic range test solutions.



Testing a camera’s dynamic range can be difficult and requires careful setup. In this section, we will review all of the relevant components of performing a sufficient dynamic range test. 




How to Test Dynamic Range - Contrast Resolution Chaart

Contrast Resolution Chart


One of the major components to testing dynamic range and the first consideration is the chart. There are a variety of dynamic range test charts available but the one we will be using for this test is the Contrast Resolution chart. There are several unique features to this chart that make it ideal for testing a system like the Pixel 2 XL. The first is the radial arrangement of the patches. This is important because it helps control for lens fall off. Second is the dynamic range of the target itself. The chart must have a higher dynamic range than the system or else you end up measuring the chart and not the camera. The Contrast Resolution chart has a dynamic range of ~95 dB which is sufficient for almost all systems with a lens. The third and most important reason for choosing this chart is the Contrast resolution dynamic range calculation. The inner light and dark gray patches on this target allow for a more robust calculation of the dynamic range of tone mapped images; which can adversely affect the quality of other dynamic range calculations.




ITI Lightbox with Contrast Resolution Chart


The next component to consider is the light source. For our test, we will be using the 10,000 lux ITI Lightbox 5100K. This lightbox is a good choice for our purposes because of its uniformity (>95%). The analysis of the chart assumes a constant amount of light being transmitted across and through the chart. The contrast resolution dynamic range calculation works because the chart has known density steps between the patches. If the light source has low uniformity than the assumption is not true and the results can be impacted.




Test setup


The third consideration is the environment you will test in. It is important to test dynamic range in a sufficiently dark environment limiting the amount of stray light that might enter the camera system as much as possible. The extra light entering the system can increase the amount of flare light (sometimes called veiling glare). Flare is the stray light that fogs images and is caused by reflections between surfaces of lens components and the inside barrel of the lens. Stray light can cause reduced contrast in dark parts of the image which in turn can make for erroneous dynamic range measurements. See our recent study: “Measuring the impact of flare light on dynamic range” for more information. Our testing environment will have only one controlled light source to illuminate the chart and no other sources of light (e.g., overhead lighting, LEDs on electronics, etc.).

A common source of erroneous measurements due to the testing environment are reflections on the chart itself. Because the darkest patches of the target let through such little light, it is easy for a non-negligible amount of stray light to be reflected off the front side of the chart. This extra light will make it seem as though more light is present in the patch area than is actually being transmitted through the chart and will adversely influence the analysis. To mitigate the reflections, we use a custom shroud to block surfaces that can reflect light from the lightbox. Unfortunately, the camera itself is the most likely culprit for unwanted light due to the potential for light to bounce off of it. The Pixel 2 XL in our case, is no exception. The particular model we are using has a white back which reflects lots of light back at the chart’s front surface. Since we are aware of this, we have taken extra care to shield all of the phone except for the lens from the light to stop any unwanted light from bouncing back at the chart.


Example of a development board reflection off the front of the chart


More on the shroud

Depending on the system, and the environment of the test, a shroud may not be necessary. However, it provides a consistent lighting environment for every test and should be considered for benchmarking and comparing different systems. For more information on reflections and shroud materials, see this post. For information on custom testing environments, please contact sales@imatest.com .


Capture Procedure


Contrast Resolution chart viewed through the native camera app


Ten images were captured with the Pixel 2 XL with the above setup. The phone was set to HDR+ mode. For each image, the exposure was adjusted on the device by tapping the area on the screen where the upper gray background of the chart was present (directly to the left of the second row). We did this to ensure similar exposure in all of the images. The acrylic shroud with matte black interior was placed around the lightbox with the phone placed as close as possible to the opening on the shroud. For this test, the lightbox was set to the 5100K color temperature setting and minimum brightness (~30 lux). We chose these settings because they are representative of a realistic low-light environment in which the camera phone may be used. Please note, the light settings could vary greatly depending on the application. Once enough images were taken, we inputted them into our software, Imatest Master, for proper analysis.



The following analysis of these images was done in Imatest Master 5.0.

Each image was analyzed in the Multitest module. Opening the Multitest Setup, we made sure to check that “3. Contrast Resolution” was selected for the chart type.

The plot we were most interested in was the Noise/SNR plot. Under this section in the setup dialog, we selected “3. SNR dB vs. Inp Density (RGBY)” and “3. Exposure in DB (-20*target density) DR in dB” from the drop downs. We also checked “Image plot” so we could visualize the results on the images themselves. See here for more information analyzing the Contrast Resolution chart.

The data was saved in JSON format and the relevant metrics were then parsed and averaged. The data we are interested was saved in the field “Contrast_Resolution_Dyn_Rng_dB”. The field contains 4 values, the Contrast Resolution dynamic range in decibels at SNR = 0, 6, 12, 20.



The plot below shows the Contrast Resolution Dynamic Range for the two modes averaged over ten captures. The standard error is included. We aggregated this data from several runs of the Multitest module in Imatest Master. As you can see in the plot below, there was a significant increase in dynamic range when using HDR+ mode versus HDR off.


Chart comparison of the HDR-off vs. HDR+ results


NOTE: If you are using Imatest software, this information can be viewed for individual images using the Noise Plot in the Multicharts module.

Since we checked “Image Plot”, we can also visualize the results on the image of the Contract Resolution chart. The two plots below show an exaggerated image plot so that the noise in each patch can be visually examined.


HDR Off Image Plot (Constant xyY)


HDR+ Image Plot (Constant xyY)


A notable difference in the plots can be seen in patch 11 (isolated images below). We can see that in the HDR+ plot (right), the details of the inner gray patches are still visible where they are not in the HDR off plot (left).


Patch 11 visual comparison (Left: HDR Off, Right: HDR+)



By following this procedure, using the Contrast Resolution chart and Imatest Master, we were able to analyze the dynamic range of our camera effectively. As we can see in the above diagrams, the summary plot results indicate that the HDR+ mode increased the dynamic range in the images by 5 dB on average. We are also able to visually see the increased range by looking at patches in the Image Plots view.

Ideally, we want to tailor our test setup as much as possible to the application being tested. In this particular case, the subject of our testing is the different HDR processing of the Pixel 2 XL. We have limited information on the specific algorithms being used when HDR is turned off versus HDR+ and their impact on image quality under different lighting situations and with different target subjects, so we are limited in the conclusions we can draw. Thus, the results reported above do not represent all use cases. A more exhaustive study would need to be done to produce results that would reflect the broad range of possible use cases.

For more information, or to learn more about how to properly set up your testing environment, talk to a solution expert.


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