Another way to look at our threshold matrix is as a kind of probability matrix. Instead of offsetting the input pixel by the value given in the threshold matrix, we can instead use the value to sample from the cumulative probability of possible candidate colours, where each colour is assigned a probability or weight . Each colour’s weight represents it’s proportional contribution to the input colour. Colours with greater weight are then more likely to be picked for a given pixel and vice-versa, such that the local average for a given region should converge to that of the original input value. We can call this the N-candidate approach to palette dithering.
Most digital images intended for viewing are generally assumed to be in sRGB colour space, which is gamma-encoded. This means that a linear increase of value in colour space does not correspond to a linear increase in actual physical light intensity, instead following more of a curve. If we want to mathematically operate on colour values in a physically accurate way, we must first convert them to linear space by applying gamma decompression. After processing, gamma compression should be reapplied before display. The following C code demonstrates how to do so following the sRGB standard:
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But didn’t you explain that you have to use rpm-ostree to install packages and not dnf?