Investigating Tradeoffs in Real-World Video Super-Resolution
We propose a stochastic degradation scheme that reduces up to 40\% of training time without sacrificing performance.
We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference.
To facilitate fair comparisons, we propose the new videolq dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns.
Our dataset can serve as a common ground for benchmarking.