How to get the result of Table 2 and Table 3 about hdr-expandnet HOT 5 CLOSED

Hi there,

That's about right, the only thing is that for display-referred the factor is 1000 (corresponding to 1000 nits) and not 1024. For scene referred it's the original image maximum.

Also, in the paper it's stated that "The scaling is done to match the 0.1 and 99.9 percentiles of the predictions with the corresponding percentiles of the HDR test images". This is done to better avoid outliers (in all model predictions and in the original HDR images).

This means that, e.g. for display referred, instead of matching 0 - >0 and 1 - > 1000 you match perc(prediction, 0.1) -> 1 and perc(prediction, 99.9) -> 999.
For scene-referred you use the original image percentiles instead of 1 and 999.

This matching is simple linear interpolation.

Hope this helps.
Best,
Demetris

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So, for display referred

low, high = np.percentile(x, (0.1, 99.9))   // x could be in range of 0-1 or 0-n
y=np.interp(x, [low, high], [1, 999]).astype(np.float32)

for scene-referred

low, high = np.percentile(x, (0.1, 99.9))   // x could be in range of 0-1 or 0-n
gt_low, gt_high = np.percentile(gt, (0.1, 99.9))
y=np.interp(x, [low, high], [gt_low, gt_high]).astype(np.float32)

Do these code look good to you?

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from hdr-expandnet.

Got it! Thank you for helping me out!

from hdr-expandnet.

No worries, glad to help!

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