Comments (3)
Not really since .interp()_ will do as many decimals as required to intepret de previous scale.
The idea of having [0,1] is to have more decimals to interpret your pixels colors.
When reading your HDR it will be of importance which colorspace are you using to show the images, Since a colorspace with "few" tones will do a bad job interpreting step changes.
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Sorry I thought I had answered this but my email response did not get here for some reason. Here it is:
"Not really, as the predictions are of relative luminance, so a linear scaling won't lose information (unless we are comparing across images, e.g. video frames -- which is something that should be considered in the experimental video implementation here).
The output can be rescaled linearly without loss, to match a display or absolute units if we have values for those.
(this of course assumes enough floating point precision which we almost surely have here when using float)"
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Related Issues (20)
- Image range processing in the experiment stage HOT 1
- Where can I get the dataset? HOT 1
- Inquiry about Dataset HOT 3
- Question about video dataset HOT 1
- About the performance of the model in training HOT 6
- durand tmo is not avalable HOT 3
- Training, iterations and pictureamount HOT 7
- Which version of PyTorch do you use in this project? HOT 1
- about train data formats HOT 1
- results in shadows HOT 21
- Not working at all HOT 8
- why it is not giving a hdr output? HOT 13
- Any updates on Training Source? HOT 4
- First line almost black HOT 1
- How to get the in-house HDR image? HOT 1
- Can expanded output be in .exr format instead of .hdr format? or need more details on .hdr HOT 2
- iterating over png/jpg dataset from a folder HOT 4
- The domain of pre-train model HOT 3
- How to get the result of Table 2 and Table 3 HOT 5
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