Wide dynamic range

Scenes that contain both very dark and very bright areas are challenging for a camera. Typical examples of such wide dynamic range (WDR) scenes in security include entrance doors, parking garages, and tunnels, where there is a large contrast between the light from outside and the darker interior. Outdoor scenes with direct sunlight and deep shadows are also problematic.

Several methods have been developed for cameras to be able to better recreate the full scene content. No single technique is optimal for all scenes and situations, and every method has its drawbacks, including the introduction of various visual anomalies referred to as artifacts.

Axis offers different WDR solutions, including two forensic solutions that constitute a revolutionary improvement in the imaging of difficult scenes. Their ability to make details visible in the dark parts of a scene, without over-exposing the bright parts, is unparalleled, and provides images with exceptional forensic value.

Axis WDR solutions:

• Forensic WDR is a combination of dual exposure and a local contrast enhancement method. It provides images that are tuned for maximal forensic usability. Employing the latest generation of image processing algorithms, this technology effectively reduces visible noise and artifacts. Forensic WDR is suitable also in scenes with motion and in ultra high resolution cameras.

• WDR - forensic capture is a combination of dual exposure and a local contrast enhancement method. It provides an image that is tuned for maximal forensic usability.

• WDR - dynamic capture uses a dual-exposure method for merging images with different exposure times. The dynamic range is limited by artifacts, for example related to motion and flickering.

• WDR - dynamic contrast uses a contrast enhancement method with fairly limited dynamic range but with very few artifacts. Since only one exposure is used, this solution performs well in scenes with a lot of motion.

Some Axis cameras use unspecified combinations of the various methods for extending dynamic range. In these cameras, the WDR solution is tailored to each specific camera and is denoted ”WDR” in datasheets and other product information.

The dynamic range capability of a camera is usually specified as a dB value, but actual WDR performance is difficult to measure and depends also on other factors, such as the complexity of the scene, the amount of movement in the scene, and the image processing capability of the camera.

Axis prioritizes forensic usability and image quality instead of a high dB value. Therefore, an Axis camera with a certain specified dynamic range could very well outperform a competing camera that has a higher dB value.

Cameras traditionally struggle with scenes of wide dynamic range, that is, scenes that have a lot of variation in light levels. This white paper explains the technology behind the limited dynamic range of a camera, describes the available general methods for achieving good WDR performance, and presents Axis WDR solutions for video with maximal forensic value and usability.

Dynamic range refers to the difference in light levels between the darkest and the brightest areas of a scene or an image. A scene with a wide dynamic range thus simultaneously contains both very bright and very dark areas. Typical examples in surveillance include:

• Entrance doors with daylight outside and a darker indoor environment.

• Parking garages or tunnels, with daylight outside and low light levels indoors.

• Outdoor scenes with direct sunlight and deep shadows.

• Office buildings or shopping malls with a lot of reflected light from windows.

Below is an example of a scene with a wide dynamic range, captured using a surveillance camera without WDR capability.

The camera can, depending on which exposure time is used, either make visible the well-lit entrance and bright exterior, or the dark interior of the garage. A camera without WDR cannot capture the full scene content in one image.

The images below have insets from the short-exposure image in the long-exposure image, and vice versa. It is evident that important objects in the scene were missed by the camera without WDR.

To be able to capture the full scene content, a WDR-capable surveillance camera is needed. It can – in one image – capture both extremes, i.e. clearly show details both in the well-lit entrance, and the dark shadows inside the parking garage. Without WDR capability, however, the dynamic range is limited by a number of factors.

The main reasons for the limited dynamic range of a camera are related to how light is captured in the camera sensor, how the images are processed, and also the nature of light itself. Described in more practical terms, the dynamic range depends on pixel size, exposure time, noise, and bit depth.

Light is made up of discrete bundles of energy, called photons. When the light intensity in a scene is increased, it means that a higher number of photons will travel toward the camera. However, a camera, or rather its image sensor, can detect only a limited number of photons per exposure interval.

The image sensor is made up of millions of photo sensitive spots, called pixels, which are capable of converting captured photons to electrons. When the camera is forming an image, the electron count for each pixel is measured, providing information about the light levels in different parts of the captured scene.

Each pixel has a set size and can hold only a set number of electrons before it is saturated. In a modern camera, we want to maximize the number of pixels but, for cost reasons, keep the total sensor size down, effectively limiting the pixel size.

For a wide dynamic range scene, a long exposure time will make pixels saturated in the brighter parts of the image. By reducing the exposure time, and collecting photons over a shorter time, photon overflooding in the brighter parts can be avoided. However, a shorter exposure time may also mean that only very few photons will be captured in the darker areas. Due to the particle characteristics of light, and a phenomenon referred to as photon shot noise, these areas of the image will be visibly noisy. The correct exposure time for a pixel is one that maximizes the signal-to-noise ratio (SNR), and therefore, it is shorter for pixels located in brighter parts of the image than for pixels in darker regions.

At pixel level, dynamic range is defined to be the maximum signal divided by the noise floor. The noise floor determines the lowest signal intensity that can be discerned above the intensity of the total sum of all noise sources. Some of the noise originates in imperfections in the analog-to-digital converter that is counting the electrons and generating a reading per pixel. Another type of noise is the photon shot noise, which is impossible to avoid even with perfect equipment. All noise results in pixel values that do not reflect the true intensities of the real scene.

The bit depth depicts the number of bits used to capture the information in one pixel, determining the number of possible light levels that can be detected. Security cameras will typically have a bit depth of 10 bits. A larger bit depth would theoretically increase the number of light levels that can be detected, but in reality, it will increase the image quality only if the sensor pixels are large enough and the noise is low enough. If the sensor data is noisy, there is not much to win by increasing the number of bits.

Regarding bit depth, it is also important to keep in mind that the typical monitor, on which a security professional will view the surveillance video, has a bit depth of only 8 bits per color channel. This means that the algorithm to translate from 10 bits in the sensor to the 8 bits in the monitor is critical in achieving good WDR performance.

Different methods have been developed for circumventing the dynamic range limitations in a camera, and achieve WDR imaging. The methods are sometimes combined to create a better result. No single method is optimal for all applications, since each method introduces different visual anomalies, so called artifacts. Artifacts that may not be visible in one application, can be a complete showstopper for another. See Chapter 7 for descriptions of frequently occurring artifacts.

Using a merging algorithm, multiple images captured with different exposure times can be combined to form one single image. This is the most common method for extending the dynamic range. However, due to the sequential capture, this method introduces artifacts related to motion in the scene. Typically, flickering light sources and rapid motion may be problematic, since objects may have moved between the captures. Image processing may also cause banding noise. Artifacts thus include:

• Flickering

• Motion blur and ghosting

• Noise

With this method, the camera uses an image sensor that contains two, or more, types of pixels, with different light sensitivities. Thereby, one single exposure can essentially create two images, one darker and one lighter, one for each set of pixels. The final WDR image is created by combining these images. There are usually restrictions, for example a fixed sensitivity ratio, on how different the sensitivities of adjacent pixels can be, limiting the dynamic range that can be achieved with this method. Owing to the simultaneous exposure, artifacts related to motion and flickering are avoided, but other types of artifacts may be introduced instead. For instance, the decreased resolution caused by this method (since fewer pixels can be used to form the image), can create moiré patterns and staircase effects in the image. Also, the processing to combine the two sets of pixels may be complicated and, in some cases, cause other problems. Typical artifacts:

• Moiré patterns and staircase effects

• Noise

• Color deviations

• Blurriness

This is a digital method using an under-exposed image, and digitally making the darkest areas lighter. The method does not truly extend the captured dynamic range, but enables improved detection possibilities in the final image, especially in otherwise over-exposed areas. It is very useful for scenes with limited dynamic range and a lot of motion. Typical artifacts introduced include:

• Banding noise in the darker areas

• Very few grey levels in some areas

• Unnatural colors

Traditionally, cameras use global methods to adjust the tone curve, which means that the same transform is used for all pixels in the image. It is also possible to use a local method, adjusting the tone curve differently in different areas of the sensor. This does not truly extend the captured dynamic range, but provides a powerful visualization tool by moderating the contrast, creating a better look on a screen with low dynamic range. Typical artifacts depend on how intensely the method is used, and can include:

• Ghosting

• Cartooning

• Lack of contrast

• Excessive colors

Axis offers several solutions for WDR imaging, combining some of the general methods described in the previous chapter with state-of-the-art image processing and procedures to reduce artifacts.

At Axis, we have selected a few key aspects for rating our WDR solutions. When determining which solution is suitable for a specific surveillance case, the aspects should be weighted differently according to the circumstances of the case. The evaluation of the aspects is based on real usage and subjective judgement.

The motion aspect rating summarizes the solution’s capability of capturing a scene with motion, without introducing artifacts that are related to the sampling technique. Treatment of flicker is one important factor in this aspect, another is avoiding merging artifacts.

The reach aspect rating summarizes how large the difference in brightness can be between the brightest and the darkest part of the image, while the surveillance usability of the image is maintained.

The appearance aspect rating summarizes the solution’s capability of reproducing the difficult light conditions, but still render an image that is usable for viewing by security staff on a computer monitor. The aim is not to reproduce the scene with as high fidelity as possible, since that would hide details for the viewer.

A camera’s dynamic range is usually specified by a dB value, which is related to the reach aspect described in the previous section. In order to provide usability and detail in typical surveillance scenes, however, Axis WDR solutions prioritize the motion and appearance aspects over the reach aspect. This priority means that Axis cameras may deliver better dynamic range imaging than their dB values imply. Considering the reduced artifacts and the improved usability, an Axis camera with a lower dB value could very well outperform a camera with higher dB value from another company. See Chapter 6 for more details about dB values.

Axis WDR solutions are listed below.

• Forensic WDR is a combination of dual exposure and a local contrast enhancement method. It provides images that are tuned for maximal forensic usability. Employing the latest generation of image processing algorithms, this technology effectively reduces visible noise and artifacts. Forensic WDR is suitable also in scenes with motion and in ultra high resolution cameras.

• WDR - forensic capture is a combination of dual exposure and a local contrast enhancement method. It provides images that are tuned for maximal forensic usability.

• WDR - dynamic capture uses a dual-exposure method for merging images with different exposure times. The dynamic range is limited by artifacts, for example related to motion and flickering.

• WDR - dynamic contrast uses a contrast enhancement method with fairly limited dynamic range but with very few artifacts. Since only one exposure is used, this solution performs well in scenes with a lot of motion.

Some Axis cameras use unspecified combinations of the various methods for extending dynamic range. In these cameras, the WDR solution is tailored to each specific camera and is denoted ”WDR” in the product information documents.

The table below shows the rating of Axis WDR solutions according to the performance aspects.

According to the rating the generally best performing WDR solution is Forensic WDR, improving both the motion aspect and the appearance aspect compared to WDR - forensic capture. However, both these forensic solutions constitute a revolutionary improvement in the imaging of difficult scenes. Their ability to make details visible in the dark parts of a scene, without over-exposing the bright parts, is unparalleled, and provides images with exceptional forensic value.

Since the purpose of the forensic solutions is to prioritize forensic usability, all shadows are brightened and details enhanced, giving the image a look and feel that is very different from what we are used to in, for example, broadcast video. With a Forensic WDR camera, the dynamic range of the scene is compressed into a much lower dynamic range, without losing the details. This optimizes the video for viewing, without eyestrain, in a security center where professional staff work with reviewing live and recorded video.

The figure below compares a scene captured with two different cameras: a camera without WDR capability on the left, and an Axis camera with Forensic WDR on the right. With Forensic WDR, details are clear and visible both in the backlit interior and outside.

The dynamic range capability of a camera is normally specified as a dB value, related to the reach aspect presented in Chapter 5.

The dB value is a measure of the ratio between the radiance of the brightest object and the radiance of the dimmest object that can be captured by the camera. If the ratio is 1000:1, the dB value is 60 dB, calculated as the logarithm of the ratio (in this case, 3), multiplied by 20.

The dimmest detectable level can be defined as the noise floor of the sensor pixel, since any signal below this level is drowned in noise. With this definition, a good image sensor can normally reach a dynamic range of about 70 dB. Using WDR techniques we can, while not altering the actual dB value of the camera, increase the practical dynamic range, or the reach.

However, neither the dB value nor the reach expresses the full dynamic range capability of a camera. The quality of a WDR image also depends on which WDR method was used, on whether any visible artifacts remain, and on the quality of the image processing. Some of these factors are summarized by the appearance aspect and the motion aspect specified in Chapter 5.

The image to the right below was taken with a camera with a lower specified dB value than the image to the left. In this wide dynamic range scene, the camera with the lower dB value clearly produced an image better qualified for video surveillance, contrary to what might be expected. The lower-dB camera obviously had other features, such as better image processing, that improved its WDR capability.

This chapter describes some of the most common visual artifacts and their causes.

• Motion blur
  Motion blur can occur when the image being recorded changes during a single frame, either due to rapid movement in the scene or because the exposure time is simply too long.

• Ghosting
  When multiple exposures are used to create one image, a moving object may be captured in different locations. While this in itself may give the image a ghost-like appearance, the imaging may become even more indistinct due to the fact that the motion blur will be different for objects of different brightnesses. A moving object will, for example, look more smeared in its darker parts than in its brighter parts.

• Artifacts caused by flickering
  Artifacts caused by flickering light can appear in all types of cameras. Since a constant illumination is normally assumed, modulated light sources, such as fluorescent lighting, present a challenge. Depending on the camera type, the introduced artifacts may look like stripes or visible pulsing.

• Banding noise
  A certain amount of randomly distributed noise in an image is often acceptable. In digital processing, however, technical difficulties in the readout of pixel values can sometimes cause lines of visible noise to appear.

• Cartooning and over-sharpening
  A WDR image may be so rich in reproduced tones and enhanced details that it is difficult to display on a standard monitor. As a result, the displayed image might have a distinctly cartoonish and unnatural style.

• Color deviations
  Methods that do not treat all pixels the same can introduce artifacts in the color reproduction, for instance, the wrong color, or too much color.

• Purple fringing
  Purple fringing, or blue fringing, is the effect where purple fringes are visible near sharp edges in the image, due to chromatic aberration in the lens. Chromatic aberration is when different colors of light are not refracted equally in the lens, and therefore end up slightly displaced, or out of focus, on the sensor. This effect is usually stronger near the edge of the sensor. WDR cameras are more sensitive to chromatic aberration than cameras without WDR capability are, because dark parts of the image are more tone mapped.

• Lens flares and haze
  When light enters any optical lens system, some light will not be collected properly but instead scattered, or flared, in the lens system. Some of this light will be collected by internal baffles designed to reduce bouncing light, but some of it will also reach the image sensor at the wrong spot, causing different types of artifacts. The most common artifact is the lens flare that is visible in most cameras facing a strong light source, like the sun. Another effect, called haze, reduces the contrast and color saturation on larger areas in the image. Both these effects will be particularly disturbing with strong light sources in the image, wide dynamic range scenes, dirty front glass, or dust in the lens system. By installing a weathershield on the camera, both flares and haze may be reduced, but WDR cameras aiming for a large reach will still be limited by the scattered light in the optical system.