The document discusses new metrics for measuring CCTV image quality called pixels per foot (ppf), which accounts for camera resolution, field of view, and distance to the target. It provides guidelines for different levels of image quality based on ppf thresholds and recommends CCTV system designers represent expected ppf lines on drawings to manage stakeholder expectations. Calculators and BIM modeling can help estimate ppf to facilitate CCTV system design.

1. Get the Picture: New Metrics for CCTV Image Quality
By Santiago Beron, RCDD, CTS
One of the most challenging aspects of designing a closed-circuit television (CCTV) system is
balancing expectations about system performance and image quality among all the project’s
stakeholders. Frequently, I have recognized the disappointment of an owner after seeing the
images of a newly installed CCTV system, because their expectations of image quality were not
at the same level as the designer. Several factors contribute to this. One is the unrealistic
performance of these systems as seen in popular TV shows. Another factor is the lack of
industry-accepted metrics for CCTV system performance.
While CCTV equipment has a number of measurable specifications (including imager resolution,
focal length, and frame rate), none of them, on their own, can be tied directly to the final
performance of the system. It is the interaction of the different parts of the system that produces
the image quality, making it very difficult to predict and explain the final results without looking
at a physical image.
Video analytics have traditionally used pixels on target (defined as the minimum amount of
pixels required to for a video analytic algorithm to be able to work properly) to define the limits
on CCTV system performance. But because pixels on target does not account for specific
distance covered, it is not representative enough to serve as the overall metric of the system. To
remedy this problem, a new metric for defining image quality—pixels per foot (ppf) [or pixels

2. per meter (ppm)]—has been introduced. Ppf is a function not only of the resolution of the
camera, but also of the camera’s field of view and the distance to the target.
For example, a 3 megapixel (MP) camera with a 6 millimeter (mm) lens will produce different
ppf measurements depending on how far you locate the camera from the target. Conversely, the
same camera located a fixed distance from a target will produce a different ppf whenever the
field of view is changed by adjusting the lens. Figure 1 indicates the distances to different ppf
lines from a 3 MP camera with a 2.8 mm lens.
Figure 1: Distances of ppf lines
Consider All the Variables
For a clear illustration of this concept, Figure 2 presents a series of pictures, each with the same
field of view but with a different ppf. A quick glance would seem to indicate that 120 ppf should
be the metric for all CCTV systems. However, if you are covering a large area, you will require a
large number of cameras installed a few feet apart to get this level of quality across the whole
area. While this is feasible for some installations, like casinos, where architecture and budget
allows for significant amount of cameras located in close proximity, it is not possible for most
projects because of difficulty in architecture or budget reasons.

3. 20 PPF 30 PPF 40 PPF
60 PPF 80 PPF 120 PPF
Figure 2: Pictures that each have the same field of view but with a different ppf.
Recently, the industry adopted a classification range for image quality to establish guidelines for
the use of video depending on the desired quality of the image. The ranges are:
„ < 40 ppf for general surveillance
„ > 40 ppf for forensic detail
„ > 80 pff for high detail.
Some video analytics, such as those for facial recognition, require a number of pixels on target
that, if translated to ppf, would result in numbers higher than 120 ppf. By comparison, license
place recognition analytics required by most software manufacturers are less than 40 ppf.
To balance expectations among the project’s stakeholders, design drawings should include a
field of view drawing for each camera, indicating the camera’s horizontal angle of view and
specific ppf line agreed to as the quality baseline for the video. I have found that the field of view
should not extend beyond the 20 ppf line, because video below this metric is too blurry to be

4. used in any type of forensic analysis. Also by representing the 80 ppf line in the same field of
view it provides a limit of the high detailed images covered by the camera’s field of view. A
drawing for a field of view should look something like the one shown in Figure 3.
Figure 3: Design drawings should include a field of view drawing for each camera, indicating the camera’s
horizontal angle of view and specific ppf line agreed to as the quality baseline for the video.
Calculating PPF
Estimating ppf lines is not very difficult. There are a number of on-line calculators that you can
use, or you can build your own by using the formula below:
= /(2 ∗ ∗ tan( /2))
where M = number of horizontal pixels in the imager (e.g., 1600 for a 2 MP camera)
D = distance to the target in feet (or meters)
= horizontal field of view of the camera/lens combination (e.g., 89⁰ for a 2.8 mm lens).

5. These ppf measurements are estimated in horizontal distance only. What about the vertical field
of view? The short answer is that it does not matter. CCTV imagers, regardless of whether they
are a charge-coupled device (CCD) or complementary metal–oxide semiconductor (CMOS), are
built with equal spacing between pixels in the horizontal and vertical plane. Therefore, the ppf
estimated in the vertical plane for a given imager/lens/distance will be the same as the ppf
estimated in the horizontal plane. This is the same reason that measuring pixel density in units of
area (square inches or square meters) does not offer a better understanding of the performance of
the camera.
Unlike distance, however, the field of view of a camera in the vertical plane is not the same as
the field of view in the horizontal plane. In some cases, you might want to show the vertical field
of view for a camera, especially if it is located in an area with very high ceilings, to double-check
the coverage of the camera. Some camera/lens manufacturers do not list the vertical field of view
of the camera, while others list both horizontal and vertical (as well as diagonal) fields of view.
If the vertical field of view of the camera is not listed, you can estimate it using the horizontal
field of view, the aspect ratio selected for the camera, and the formula below:
= 2 ∗ (
2 ∗ ∗
)
Where M = number of horizontal pixels in the imager (e.g., 1600 for a 2 MP camera)
D = distance to the target in feet (or meters)
AR = aspect ratio selected for the camera.
Aspect ratio is defined as the ratio between horizontal pixels and vertical pixels. In our industry,
we typically use ratios of 4:3 (1.33) and 16:9 (1.77). Almost all imagers are manufactured with a
4:3 aspect ratio, but most cameras can produce images with either ratio. To change between the
two aspect ratios, the cameras typically do not read (or they simply ignore) some pixels at the
edge of the imager.
Estimating Distance and Field of View
Bear in mind that changing the aspect ratio of a camera in the electronic settings will change the
fields of view even if you do not touch the lens or change the distance to the target. Additionally,
using a camera at a resolution different from the natural resolution of the imager might cause the

6. field of view to change. For example, if you have a 3 MP camera but you want to use it at 1 MP,
the field of view might change even if the lens and target remain the same.
Some camera manufacturers simply ignore pixels at the edge of the imager to change the
resolutions settings of the camera (in what’s called the pixel elimination method), causing a
change in both fields of view. Other camera manufacturers use a scaling process (reading
selective pixels along the complete imager, not just the edge) and changing camera resolutions
do not change the field of view. But even cameras with good scaling algorithms have limits on
operability. When you make drastic resolution changes (i.e., using a 3 MP camera at 640X480
resolution only), this causes the fields of view to change anyway, because the manufacturer will
use a combination of scaling/pixel elimination to get a good-looking picture.
Even if the manufacturer discloses the vertical field of view of the camera/lens, you should
double-check this metric using the formula given above. Sometimes they list this angle only for
one aspect ratio, and that could be a different aspect ratio from what you intend to use the camera
for, resulting in a different field of view.
The diagonal field of view listed by some camera/lens manufacturer is an industry mystery. The
math does not work when trying to prove these measurements, and I have not been able to get
any reason why from manufacturers or from colleges. The bottom line: Don’t use diagonal fields
of view for any calculations.
Finally, the distance to the target (D) can be estimated only in the horizontal plane. If you feel
comfortable with math, you can estimate a true distance by incorporating the vertical difference
between camera and target using the Pythagorean theorem. Unless the vertical difference
between camera and target is significant, however, you will get satisfactory results by just using
horizontal distance.
The use of building information modeling (BIM) in current design projects allows the use of
smart objects that facilitate estimating all these distances and angles. Camera manufacturers are
coming out with BIM models capable of showing fields of view and ppf lines, compensating for
vertical distances, and providing more accurate information. BIM enables quick renderings of the
cameras’ fields of view and easy of adjustment for final camera locations. These tools are
proving very useful in setting expectation among project stakeholders.
Summary

7. Expect to see more use of ppf in the design of CCTV systems to level expectations among
project stakeholders. The use of BIM modeling will facilitate the calculations of this metric and
will make it easy for end users to “see” the expected camera field of views, solving a very old
problem in our industry.
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About the Author:
Santiago Beron, RCDD, CTS-D, is a principal and systems project manager for TLC Engineering for Architecture in
Tampa, Fla. He has been designing voice, data, security and AV systems for more than 19 years. For more
information on CCTV metrics, Santiago can be reached at Santiago.beron@tlc-eng.com.
Pull quotes:
Pixels per foot is a function not only of the resolution of the camera, but also of the camera’s field of view and the
distance to the target.
Recently, the industry adopted a classification range for image quality to establish guidelines for the use of the video
depending on the desired quality of the image.