How does smartphone pixel binning compare to larger native pixels?

Asked 3/9/2020

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Some smartphone cameras use pixel binning to switch between full-resolution capture and a lower-resolution low-light mode. For example, a 108MP sensor may combine a 3×3 block of 0.8µm pixels into a 12MP output, and marketing may describe this as becoming 2.4µm pixels.

How does that work in practice? Is pixel binning more than simple software downsampling, and are there trade-offs? If you shoot at the full 108MP resolution and later resize to 12MP, should that be effectively identical to a binned 12MP capture? Also, is a 3×3 group of 0.8µm pixels really equivalent to one native 2.4µm pixel?

Originally by Photography Stack Exchange contributor. Source · Licensed CC BY-SA 4.0

Photography Stack Exchange contributor

6y ago

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But how does this work in practice? Are there trade-offs involved?

There are always trade-offs involved. In the case of quad-pixel and now nona-pixel sensors, the amount of color resolution is reduced from what one would expect to get from the full number of pixels.

For 48MP quad-pixel sensors that output 12MP when using binning, their color resolution is equal to about 27MP when using algorithms to rearrange the subpixels among the logical pixels. It interpolates from this (on the left) to this (on the right):

enter image description here

Each group of four same-colored pixels is virtually "stretched" to 1.5 their linear size.

If I've done the math correctly (using 1/1.5 cubed instead of 1/1.5 squared), a 108MP nona-pixel sensor that gives 12MP image when binned will yield color resolution equal to 32 MP when interpolating in the above method. Or maybe I should have used the square of 1/2 instead of the square of 1/1.5? In which case you'd be right back to 27MP effective color resolution.

This article from xda-developers.com explains the effect with quad-pixel sensors in more detail.

Before 2018, essentially every smartphone camera had a Bayer color filter array. The Huawei P20 Pro and the Huawei Mate 20 Pro were the first phones to use Quad Bayer sensors. Simply put, a Quad Bayer sensor has less color resolution than a sensor with a standard Bayer layout. On the IMX586, for example, the physical color filters on the camera sensor only have an effective resolution of 12MP. The ISP of such sensors is able to achieve a virtual 48MP Bayer result out of the sensor by re-arranging the subpixels among the logical pixels. It should be clear that this approach isn’t as good as using a standard Bayer filter. What is the specific difference? According to AnandTech, the 48MP IMX586 has closer to 27MP of spatial resolution as it’s only able to increase spatial resolution half-way.

The link in the above quote includes this about quad-pixel sensors:

This means that the physical colour filters on the camera sensor only have an effective resolution of 12MP. Sony’s sensor ISP is able to achieve a virtual 48MP bayer result out of the sensor by rearranging the subpixel-data among the logical pixels. It’s to be noted that this method would result in an effective spatial resolution increase of only half-way to 48MP, and actual results would be of clarity somewhere in the range of a true 27MP bayer sensor.

Samsung themselves hype their tetracell (quad-pixel) technology, along with other sensor innovations here. But Dieter Bohn at The Verge wasn't very impressed by the actual performance that 108 MP promises for the ability to zoom.

Originally by user15871. Source · Licensed CC BY-SA 4.0

user15871

6y ago

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Pixel binning combines data from neighboring small pixels to produce a lower-resolution image with better signal-to-noise performance. In that sense, a 3×3 group of 0.8µm pixels gathers light over roughly the same area as one 2.4µm pixel, but it is not perfectly identical to a native larger pixel.

The main trade-off is color/detail handling. Many of these high-resolution phone sensors use repeated same-color subpixel groupings, so when binned, luminance noise can improve, but color resolution is lower than the headline megapixel count suggests.

A full-resolution shot resized later to 12MP also combines information from multiple pixels, so for the same final output size, low-light performance can be broadly similar. That’s why sensors of similar generation and total size can end up looking close when images are normalized to the same output size.

So yes: binning helps, but it’s not magic. It improves noise performance by combining data, yet it doesn’t turn a small-pixel sensor into exactly the same thing as a sensor built with fewer, larger native pixels.

UniqueBot

AI

6y ago

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