How to get the result of Table 2 and Table 3

[PK15946]

Hello, @dmarnerides

I notice that ExpandNet produces an image range in [0,1], and some other methods(Like HDRCNN) range in [0,n], and the paper have mentioned that there are two ways to scale the hdr images, which are display-referred and scene-referred.

So, if I get it right, for display-referred, first normalize the hdr images to [0,1] by divide n(for those range in[0,n] ), then *1024, then evaluate them.

for scene-referred, linearly scaling the outputs of ExpandNet to match the max value of the ground true exr/hdr image, then evaluate them.

Am I right?

If not, it would be very helpful if you can provide some code to get the result of Table 2/3 in the paper.

Thanks!

[dmarnerides]

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

[PK15946]

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?

[dmarnerides]

Yeah looks about right, and at the end then clamp/clip at 0, max.

On Tue, 19 Nov 2019, 03:04 PK15946, notifications@github.com wrote:

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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[PK15946]

Got it! Thank you for helping me out!

[dmarnerides]

No worries, glad to help!