Data binning is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall in a given small interval, a bin, are replaced by a value representative of that interval, often the central value. It is a form of quantization.
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In the context of image processing, binning is the procedure of combining a cluster of pixels into a single pixel. As such, in 2x2 binning, an array of 4 pixels becomes a single larger pixel,[1] reducing the overall number of pixels. This aggregation reduces the impact of read noise on the processed image at the cost of a lower resolution.
For example, data binning may be used when small instrumental shifts in the spectral dimension from MS or NMR experiments will be falsely interpreted as representing different components, when a collection of data profiles is subjected to pattern recognition analysis. A straightforward way to cope with this problem is by using binning techniques in which the spectrum is reduced in resolution to a sufficient degree to ensure that a given peak remains in its bin despite small spectral shifts between analyses. For example, in NMR the chemical shift axis may be discretized and coarsely binned, and in MS the spectral accuracies may be rounded to integer atomic mass unit values.
Also, several digital camera systems incorporate an automatic pixel binning function to allow the display of a brighter preview image.[2]