What Is Image Quality? – Vision Campus

Welcome back to our Vision Campus! Today let’s talk about brightness, sharpness,
quantum efficiency, sensor and pixel size, resolution, dynamic range,
noise and signal-to-noise-ratio, and contrast, or in other words image quality. What does image quality mean? For the amateur photographer, good image quality
mostly means a sharp, bright image with high contrast. In industrial image processing, however, the
definition of image quality goes far beyond brightness and sharpness. Imagine you find two camera models that might
be of interest for your requirements. They both look pretty much the same from the outside. How do you know which one will capture the better pictures for your application? Let’s look more closely into their EMVA1288
datasheets. The EMVA1288 standard has been developed by
the European Machine Vision Association with the goal of standardizing image quality and
sensitivity measurements for machine vision cameras and sensors. You see lots of data and terms. Many of the values in the tables vary greatly. But do higher values mean better images for
your application? A large factor in your consideration is the
sensor and pixel figures. The sensor is the heart of the camera and
hence its most important component. The sensor consists of pixels with photodiodes
that convert the energy of the incoming photons into an electrical charge. That electrical charge is then converted and
processed to generate the image. The number of electrons you get per number
of photons is a ratio which is called Quantum Efficiency. Clearly, the greater the number of electrons
produced for a given number of photons, the higher the Quantum Efficiency. The higher the Quantum Efficiency, the more
information is available in an image. Think of a DSLR camera with a Full Frame sensor
and compare it to a compact camera with a smaller sensor. In perfect conditions both cameras deliver
good images. When it gets darker you will notice that the
DSLR camera delivers much better images. It has a larger sensor and hence can capture
much more light in order to collect more information. The compact camera, by contrast, quickly activates
the flash to compensate for the reduced amount of scene light. Or – if you’ve set your camera to no flash – it will adjust the ISO or gain, which in turn will increase the noise level in the image. In very simple terms you could say that the
bigger the sensor, the higher the image quality. Here you can see some of the most common
sensor sizes. Then again, it’s not only the sensor size,
but also the number of pixels and their size on the sensor surface that play a vital role
in this context Imagine the DSLR camera with a large sensor and the compact camera with a smaller sensor both having a resolution of 15 megapixels It’s obvious that if the sensor size is not
enlarged, the pixels on the sensor of the compact camera must be smaller in order
to get the same resolution. But if there are too many pixels on the sensor
surface, you also get higher noise and less sensitivity to light, which in turn, has a
negative effect on image quality. However, the actual resolution is always determined
by the interaction between sensor, lens and distance to the object. The size of a pixel is important with regard
to the quantity of light it can collect. You could compare this process to millions
of tiny buckets collecting rain water. The brighter the captured area, the more photons
are collected. After the exposure, the level in each bucket
is assigned a discrete digital value. In a system where we have 256 different gray
values, a value of “0” represents pure black and “255” pure white, as perceived by the
sensor. Once the buckets – the pixels – are full,
they overflow. What flows over is lost and the values of
these buckets all become 255, when they actually should have been different. In other words, image information was lost. In this image the shadow area was captured
at the expense of the brighter area. The shadow buckets need a long exposure to
collect sufficient photons. As a consequence, the buckets for the brighter
area overflow. On the other hand, if you reduce the exposure
time then many of the pixels which correspond to the darker areas of the scene may not have
had enough time to capture any photons at all. They all might have the value zero while in
reality there are differences. Now, it is easy to understand why the DSLR
delivers a better image quality. Practically, cameras with large pixels are
able to capture both bright and dark image details. How well or badly a camera captures both types
of details within the same image, is described as the dynamic range. In this example, the camera’s dynamic range
is large enough to allow it to capture both dark and bright details correctly. A typical machine vision application requiring
a large dynamic range would be a traffic camera catching speeders. The license plate is usually very bright whereas
the area around the driver is usually rather dark. A large dynamic range allows you to depict
the details in the bright portion of the image just as well as in the dark portion. A smaller dynamic range would only provide
good detail for one of them. Noise is another key image quality factor. See the graininess in the image? That’s noise. Since it arises from basic physics, it`s always
present. The signal-to-noise ratio or SNR measures
the relationship between the signal the image – and the noise Imagine you’re in your car, listening to the
radio. When you hear the music clearly, that’s a
high SNR and low noise. The worse the sound signal the music in this
case becomes, the lower the SNR gets. The sound signal is too weak to overcome
the noise. Now remember our example. The DSLR camera does have a clear advantage
in terms of signal-to-noise ratio. It collects more light and produces less noise The higher the signal-to-noise ratio in the
datasheet, the fewer disturbances you see in your image, and the better the image quality. Also the sensor technology plays a crucial
role. CMOS sensors made a major technological step
forward during recent years. They are very powerful, sensitive, fast, and
affordable. But only if the camera manufacturer has paid
careful attention to many critical details, will you be able to exploit the full potential
of a CMOS sensor. Image quality is not only about all these
technological camera and sensor properties we’ve just described. It’s also the vision system as a whole that
must be considered. What more do you need from a good image? You need it to be sharp and rich in contrast,
of course. Imagine you are operating a packaging application
in which your camera has to read barcodes. The better you can identify the transition
from the dark to the bright areas, the higher the contrast and the sharper the image appears. Whatever your requirements, you must be careful
to create comparable conditions when you want to assess two or more different cameras. You are well set if the sensor sizes of the
cameras are the same. This gives you an ideal basis for a comparison. However, there are a few more conditions that
must be considered. You need to create similar conditions with
regard to – the object you want to capture
– the illumination – the distance between camera and object
– the chosen exposure time – the region of interest
– the gain – the lens
– and the f-number of the lens. If the sensors of the cameras you want to
compare have different sizes, things aren’t so simple. Then you must either change the distance between
camera and object, the lens, or the region of interest. So when comparing two camera models
– ask your vendor about the EMVA 1288 datasheets – and test the camera performance under real-life
conditions for your system. All these criteria we’ve just discussed should
give you an overview on how to approach the task of assessing the image quality of different
cameras. Obviously, there are other tools and methods to assess image quality quality. But with the help of these essential criteria
brightness, sharpness, quantum efficiency, sensor and pixel size, resolution, dynamic
range, noise and signal-to-noise-ratio and contrast you have a useful dataset on hand
to clearly differentiate a good from a bad image Thanks for watching and stay tuned for more
from the Vision Campus!

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