In the next instalment of everything tech explained, I today bring you everything you need to know to get to grips with pixel binning. 😄
Now you know why and how a picture is of that particular resolution.
What 4, 9 and 16 pixel binning looks like.
Let's start with the basics. What is a pixel? It is the smallest area of illumination on a display. You can think of it as the analogue of an atom, but in display terminology.
Now, when we talk about the resolution of photos, we talk in terms of megapixels, or MP. Ever wondered how this name came by? Well, in the simplest of terms, we're talking area here.
When we take a thousand pixels lengthwise and a thousand pixels widthwise to form a picture, the image would constitute of 1,000,000 pixels, or a million pixels, or, 1 MP. Boom! You now have instant bragging rights! But hey, calm down. There's lots more to cover. So hang tight. 😁
Fun fact : Smartphones currently have the capacity to resolve within the range of megapixels. However, extreme cameras used in space research laboratories are capable of resolving in gigapixels. Now that's a whole lot of pixels.
Anyway, moving on. So, with megapixels and resolution out of the way, let's head to another important aspect, pixel size.
Having an imaging sensor that can resolve at a high resolution, say, upwards of 48MP, is always a great thing! Or is it? Let me break it down for you.
Things start to change drastically in low-light scenarios. Here, a high resolution sensor will struggle to pull in light (and hence resolve details) as the pixel size would be rather small to accommodate all of that resolving power, as the size of the sensor itself is limited at the end of the day.
I'll be making a correlation between my explanation and a plot of land for you to understand better. Remember, I spoke about talking in terms of area in the introduction? Yes. I shall carry that forward.
Now think you have a plot, 100 square metres in area. How difficult would it be, if you were to cover the entire area with tiles just 1 square centimetre across? Now compare that to covering the land with tiles 1 square metre in size. Piece of cake.
Hence, having a large sensor size (analogous to a large plot) is great unless you are shooting in low-light conditions (analogous to covering the plot with small tiles).
Okay Dhruv, I've understood the problem. What's the fix?
Well, this is where pixel binning comes into the picture (pun intended). 😋
Pixel binning combines pixels together, resulting in one larger pixel. You now simply have a larger tile size to fill the same plot. I hope you're following the analogy here.
So, how does this larger pixel help?
It helps the sensor gather extra light and hence extra details. It also helps cut the noise, making the picture cleaner.
For example, the pixel size on the 108MP sensor on the S20 Ultra is 0.8 microns or micrometres. This, by mobile photography standards, is on the lower side of things due to the large megapixel count. But when pixel binned, results in a pixel size of 2.4 microns, which leads to some incredible night time / low-light shots! This sensor uses what is called 9:1 pixel binning, or nona-binning, which means 9 pixels are combined to yield 1 large pixel. However, this isn't just limited to the sensor at the rear. The S20 Ultra's front facing 40MP sensor uses 4:1 pixel binning, combining 4 pixels together instead of 9.
Hence, the resulting image from the 108MP sensor after 9:1 binning and that from the 40MP sensor after 4:1 binning are 12MP and 10MP respectively. But don't let the numbers fool you. These aren't just any other 12MP or 10MP images. These pictures will have far more detail and quality as a whole compared to pictures taken on other sensors that are clocked at 12/10MP by default.
So there you have it! Pixel binning explained! Pretty simple, right? 😃
Thank you for your time.
Dhruv Jain.
