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Image Processing & Correction

Image bit depth

How many distinct intensity values your image can hold

Definition
Bit depth is the number of bits used to store each pixel's intensity value. 8-bit images hold 256 distinct values per pixel (0 to 255). 16-bit images hold 65,536 (0 to 65,535). 32-bit images hold over four billion (or arbitrary floating-point values for processed data). The depth sets a ceiling on how finely you can quantify intensity — and a floor on how much noise you can detect. Camera sensors typically acquire at 12 or 16 bits. Some downstream engines and viewers assume 8. The Convert 16-bit to 8-bitLoading... engine bridges them, with a tradeoff that's not always visible until you look at the histogramLoading....
Dynamic range - 16bit to 8bit normalization
Video · Primary
Supporting
Dynamic range - 16bit to 8bit normalization 6m
Video · Supporting
User Interface - Dimensions (8bit FL)
Video · Supporting
256 vs 65,536 vs billions
Each step up multiplies headroom by 256
Downcasting throws information away
16→8 is lossy, even when it looks fine
Look at the histogram before downcasting
If the data is bunched at the low end, you'll lose it
32-bit is for math, not for storage
Floating-point intermediates

Numeracy: what 256 vs 65,536 actually means

A pixel in an 8-bit grayscale image carries one of 256 possible values. That's eight binary digits — eight bits — used to encode intensity. The smallest detectable difference between two pixels is one of those 256 steps: about 0.4% of the full range.

A pixel in a 16-bit image carries one of 65,536 possible values. The smallest detectable difference is 0.0015% of the full range — 256 times finer. For samples where the bright signal is, say, 100 times stronger than the dim signal you care about, 8-bit may not even resolve the dim signal as a distinct value from the noise floor; 16-bit will. This is why scientific cameras almost always acquire at 16-bit, and why downcasting at the start of a pipeline is rarely a good idea.

The flip side: 16-bit images take twice the disk space, and most casual viewers display them by linearly compressing to 8-bit anyway. So the gain is purely quantitative — it shows up in measurements, not in what the eye sees in a quick preview.

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