  Video super-resolution (VSR) techniques, especially deep-learning-based
algorithms, have drastically improved over the last few years and shown
impressive performance on synthetic data. However, their performance on
real-world video data suffers because of the complexity of real-world
degradations and misaligned video frames. Since obtaining a synthetic dataset
consisting of low-resolution (LR) and high-resolution (HR) frames are easier
than obtaining real-world LR and HR images, in this paper, we propose
synthesizing real-world degradations on synthetic training datasets. The
proposed synthetic real-world degradations (SRWD) include a combination of the
blur, noise, downsampling, pixel binning, and image and video compression
artifacts. We then propose using a random shuffling-based strategy to simulate
these degradations on the training datasets and train a single end-to-end deep
neural network (DNN) on the proposed larger variation of realistic synthesized
training data. Our quantitative and qualitative comparative analysis shows that
the proposed training strategy using diverse realistic degradations improves
the performance by 7.1 % in terms of NRQM compared to RealBasicVSR and by 3.34
% compared to BSRGAN on the VideoLQ dataset. We also introduce a new dataset
that contains high-resolution real-world videos that can serve as a common
ground for bench-marking.
