Pixel Binning, does it work?

Intro

Pixel binning is the process of combining the data in a group of pixels into a single pixel, such as a 2×2 or a 3×3 block. Doing so can increase the effective sensitivity or reduce the noise present in the resultant pixel. The trade off is of course reduced resolution in the final images.

To illustrate this consider the case of of the 2×2 block being combined to increase sensitivity. If the camera exposes an image for a quarter of the time that it normally would, each pixel is underexposed by 2EV. However when the pixel values are added up, the resulting single pixel will have the correct exposure. Therefore preforming this binning has created a single image with a resolution a quarter of the original, but shot at an ISO 2 stops more sensitive.

Motivation

Pixel binning to average values (for reduced appearance of noise) is uninteresting. This averaging is performed by the majority of image editing software when an image is scaled down, which is done anyway to any image when prepared for the web.

Pixel binning for addition to simulate higher ISO values however is more interesting, this potentially unlocks a little extra performance in low light. The shorter exposures could be key in freezing motion in dimly lit areas such as at concerts or some sports.

But would this pixel binning really be any better than underexposing the image then correcting exposure in a raw processor? Does it really gain anything?

Test images

Searching doesn’t readily produce any software that will perform pixel binning, but fortunately Python facilitated the creation of a script that would take in a 16bit grey-scale image and bin the pixels by addition in just 9 lines.

pixel binning samples

Pixel Binning test images.

The image above shows the results. Three test images were captured of the same scene at ISO6400 at 0,-2 and -3EV. The underexposed images were processed with the python script to bin the pixels up to the correct exposure (2×2 for -2EV, 3×3 for -3EV), simulating an ISO of 26K and 52K. The same images were also treated in adobe camera raw by adjusting exposure value, then scaling the image to match the binned image. For comparison the 0EV image has also been scaled to match. The images above are 100% crops from the images.

Analysis

It’s quite easy to see that there is effectively no difference between the images that have been binned, compared to the images manipulated in Photoshop. And neither manages to match the level of shadow detail present in the image correctly exposed in the first place. A moment considering the maths behind the two processes shows the reason for the similar appearance.

Consider the case of 2×2 pixel binning. Label the 4 pixels A-D, and the binned pixel X. The additive binning method provides the straightforward equation:

X=A+B+C+D

Whereas the photoshop method multiplies each value by 4 (+2EV), then averages them:

X=(4A+4B+4C+4D)/4

Which clearly is identical. So does additive binning really provide any advantage over binning to average? Well in this case no, they are the same thing according to the equations above when done in post. The problem with the additive method is that the noise is also summed. Once the light incident upon a pixel falls below the noise floor, the data is pretty much lost and adding another three pixels at the noise floor just gives you more noise, rather than aiding recovery of shadows.

Can it still work?

So while you can do pixel binning in post if you want, it doesn’t particularly gain much. So is it still worth doing? Yes, when done during capture.

The key issue is overcoming the noise floor, and once the noise is present, no amount of post work with remove it. Read noise is one of the problems, which is the noise generated when the pixels are read from the sensor. If pixel binning is done on sensor, i.e the sets of pixels are combined before being read, then the read noise will be greatly reduced (one portion of noise per final pixel instead of 4/9/etc). This means that additive binning really can provide some benefit in this case by reducing that source of noise. This is performed in some point and shoot cameras where high ISO images come out with reduced resolution, but also on high end cameras such as the Phase One medium format backs with the Sensor+ setting.

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