Some things about the results presented in the corresponding paper

[ShihuaHuang95]

Hi, this is a great job!
I have some confusions about this paper:
Table 3 shows the results over UDM10 dataset. I can not achieve such higher results (average about 28+ and 0.84+ for PSNR and SSIM, respectively), though our model which trained over Vimeo90K is outperformed all SOTA methods except yours when evaluated over Vid4.
In the last, could you please provide the codes for PSNR and SSIM calculation?

Looking for your reply! Many thanks for your great work!

[ShihuaHuang95]

One more thing, "Further, we have collected another 20 video sequences for evaluation during training" in Part 4.1 Implementation Details, what does it mean "for evaluation during training"? Does the results presented in 4.2, 4.3 and 4.4 are achieved when tested over such val dataset?

[psychopa4]

Hi, this is a great job!
I have some confusions about this paper:
Table 3 shows the results over UDM10 dataset. I can not achieve such higher results (average about 28+ and 0.84+ for PSNR and SSIM, respectively), though our model which trained over Vimeo90K is outperformed all SOTA methods except yours when evaluated over Vid4.
In the last, could you please provide the codes for PSNR and SSIM calculation?

Looking for your reply! Many thanks for your great work!

Models shown in the paper are all trained over MM522 dataset, you may as well try to train your models on this dataset.
PSNR and SSIM are computed using Matlab

[psychopa4]

One more thing, "Further, we have collected another 20 video sequences for evaluation during training" in Part 4.1 Implementation Details, what does it mean "for evaluation during training"? Does the results presented in 4.2, 4.3 and 4.4 are achieved when tested over such val dataset?

It means we use another 20 video sequences to evaluate the performance of the model during training.
Yes, they are.

[ShihuaHuang95]

Many thanks for the provided metrics!

[wenchen4321]

Hi, this is a great job!
I have some confusions about this paper:
Table 3 shows the results over UDM10 dataset. I can not achieve such higher results (average about 28+ and 0.84+ for PSNR and SSIM, respectively), though our model which trained over Vimeo90K is outperformed all SOTA methods except yours when evaluated over Vid4.
In the last, could you please provide the codes for PSNR and SSIM calculation?

What downsampling method of your training dataset Vimeo90K? I trained the model with Vimeo90K with bicubic downsampling, the results are very bad.

[suxi-du]

can u release your psnr and ssim evaluation code in matlab? The link is losing its efficacy. I got much worse result according to my understanding. it's just 20db.

[psychopa4]

can u release your psnr and ssim evaluation code in matlab? The link is losing its efficacy. I got much worse result according to my understanding. it's just 20db.

I upload the PSNR and SSIM calculation code in Matlab, please see ./matlab/ .