The contrast sensitivity function (CSF) is a model of how the human eye's sensitivity to contrast varies with spatial frequency — the scale of a pattern. The eye is not equally sensitive to all detail: very fine, high-frequency texture and very coarse, low-frequency variation are both harder to perceive than mid-range patterns, and the CSF captures that curve. This makes it a foundational building block of perceptual quality measurement, because it tells a metric how visible an error or contrast step actually is rather than how large it is in raw pixels. Perceptually weighted PSNR variants like PSNR-HVS use it to discount errors the eye barely registers, and Netflix's CAMBI banding detector uses the CSF directly to weight each candidate contour by how likely a viewer is to see it at a given brightness and scale — the contrast-awareness that lets it flag the steps the eye catches while ignoring harmless detail. The CSF also depends on viewing conditions, so brighter displays and shorter distances change what is visible.
