  The diversity and complexity of degradations in real-world video
super-resolution (VSR) pose non-trivial challenges in inference and training.
First, while long-term propagation leads to improved performance in cases of
mild degradations, severe in-the-wild degradations could be exaggerated through
propagation, impairing output quality. To balance the tradeoff between detail
synthesis and artifact suppression, we found an image pre-cleaning stage
indispensable to reduce noises and artifacts prior to propagation. Equipped
with a carefully designed cleaning module, our RealBasicVSR outperforms
existing methods in both quality and efficiency. Second, real-world VSR models
are often trained with diverse degradations to improve generalizability,
requiring increased batch size to produce a stable gradient. Inevitably, the
increased computational burden results in various problems, including 1)
speed-performance tradeoff and 2) batch-length tradeoff. To alleviate the first
tradeoff, 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. Code,
models, and the dataset will be made publicly available.
