Regarding point 1:
In that case it would still be useful with low-noise photos. Some random deviation in exposure would probably be preferrable to consistent overexposure. With 0.1% setting I didnt notice noise being a problem, so it's probably worthwile to push this further until problems with noise become obvious. I just wonder if one would even conciously notice such noise problems. Maybe they are already there and i'm just not noticing it?
I have some experience using this algorithm (ETTR + some fixed shift) for data visualisation. In that application I sometimes do exposure compensation for every pixel column individually, making the influence of noise an important factor because random column-wise exposure variations make the image have strong banding. I did indeed notice noise to be a problem in that setting. I found the noise problems could be alleviated by doing the following: instead of computing the exposure compensation value for a single pixel % value, compute it for a (narrow) range of % values, then take the average of them.
Regarding point 1:
Regarding point 1:
In that case it would still be useful with low-noise photos. Some random deviation in exposure would probably be preferrable to consistent overexposure. With 0.1% setting I didnt notice noise being a problem, so it's probably worthwile to push this further until problems with noise become obvious. I just wonder if one would even conciously notice such noise problems. Maybe they are already there and i'm just not noticing it?
I have some experience using this algorithm (ETTR + some fixed shift) for data visualisation. In that application I sometimes do exposure compensation for every pixel column individually, making the influence of noise an important factor because random column-wise exposure variations make the image have strong banding. I did indeed notice noise to be a problem in that setting. I found the noise problems could be alleviated by doing the following: instead of computing the exposure compensation value for a single pixel % value, compute it for a (narrow) range of % values, then take the average of them.
Regarding point 2:
Oh, great!