There was a time when cameras on mobile devices were less than 1 itty-bitty megapixel. But fast-forward, people have smartphones with multiple cameras, each with huge megapixels.
A megapixel consists of one million pixels (picture element), The unit has been used for image sensing capacity in digital cameras. In general, the more megapixels a digital camera has, the better the resolution when printing an image in a given size.
With more and more megapixels packed into a camera sensor, what more can companies do to further improve the quality of pictures?
The answer is many. And one of them, is called 'pixel binning'.
This implementation is designed to reduce noise, and at the same time, improve signal-to-noise ration and frame rates of digital cameras. The result is improved pictures when taken in low-light conditions.
The process is done by combining the electric charges from adjacent CMOS or CCD sensor pixels into one "super-pixel". Typically, the binning happens on groups of neighboring four pixels (2 x 2) that form a quad, while some other sensors can merge a block of consisting of even more pixels.
To understand pixel binning, we must first explain what pixel really is.
As previously mentioned, pixel is a unit. Each pixel is programmable, and for its size, its usually measured in microns (one millionth of a meter). For a digital camera to capture more light in order to create better quality pictures, its need pixels that are large.
Simply put, a larger pixel can capture more light than a smaller pixel.
With more light a camera sensor can catch, the better it can take pictures in dark environments.
But for smartphones, bigger sensor may not be the best option, as smartphones need to be as compact as possible so they can fit on users' hands. Because having small pixel cameras is not an option, what this means, smartphones either have to increase the size of the sensor and deal with a camera bump, or shrink the pixels even more.
Shrinking the pixels down even more will have a bad effect on low-light capabilities. But this is where pixel binning can make a difference.
In an example, a camera sensor with tiny 0.9 micron pixels, can produce results equivalent to 1.8 micron pixels when taking a pixel-binned shot.
The reason for binning is to increase the signal-to-noise ratio (SNR or noise reduction), which is a key metric in analog applications (such as image sensing).
In modern digital cameras, binning is particularly useful to obtain higher brightness in extreme low-light conditions.
With less noise from the analog data, the image can be subjected to higher levels of gains/amplifications during the post-processing phase.
Binning array sizes are controlled by the CCD clock, bias voltages, and video processing signal timing, and are usually adjustable from 2 x 2 pixels to a maximum that can include almost the entire CCD array.
However, in the binning mode, both the serial shift register and output node will accumulate a significantly larger charge than in normal operation and must contain sufficient electron charge capacity to prevent saturation. And if dark noise happens, binning can correct this by cooling the CCD to low temperatures.
Binning is useful in a variety of applications, especially where fast throughput times (frame rates) are desired.
Modern cameras on smartphones can do pixel binning well due to their huge megapixels. That is because pixel binning has one big disadvantage.
The disadvantage is the expense of resolution.
This is because pixel binning essentially divide the final resolution by the number of binning group. So for example, a 2 x 2 (4 pixels) pixel binning, the resolution is divided by 4.
What this means, if a binned shot is taken on a 48MP camera, the result is actually 12MP, while a binned shot on a 16MP camera is only a mere 4MP. So if a camera doesn't have enough megapixels to start with, pixel binning will essentially reduce the quality of pictures, as details are degraded.
While this fact is inevitable, in some situations, the quality reduction is minimal if compared to the benefits of reduction in disk space needed, or computation cost on the images in the future.
The second downside is rather minimal.
Pixel binning lacks RAW output. Since RAW output is the encapsulation of the raw sensor data, a pixel-binned version cannot exist. If the JPG algorithms being used by a smartphone are aggressive in favor of reducing file size, the compression would result in even more detail degradation. Only if the manufacturer allows it, users can either record a RAW image file (without pixel binning) or a pixel-binned JPG.
So here, pixel binning does bring some benefits, but not a big blow to those who don't use it.
But as smartphones are keeping their pace in increasing megapixel counts to woe more people into buying them, this pixel binning technique should become more prominent in digital photography.
And with the help of AI on smartphones, pictures taken should be increasingly closer to SLR professional cameras, if not better in fooling the eyes.
However, it should be noted that the higher the spatial resolution of the image, the more degradation pixel binning would have on the image quality.