  In the blind single image super-resolution (SISR) task, existing works have
been successful in restoring image-level unknown degradations. However, when a
single video frame becomes the input, these works usually fail to address
degradations caused by video compression, such as mosquito noise, ringing,
blockiness, and staircase noise. In this work, we for the first time, present a
video compression-based degradation model to synthesize low-resolution image
data in the blind SISR task. Our proposed image synthesizing method is widely
applicable to existing image datasets, so that a single degraded image can
contain distortions caused by the lossy video compression algorithms. This
overcomes the leak of feature diversity in video data and thus retains the
training efficiency. By introducing video coding artifacts to SISR degradation
models, neural networks can super-resolve images with the ability to restore
video compression degradations, and achieve better results on restoring generic
distortions caused by image compression as well. Our proposed approach achieves
superior performance in SOTA no-reference Image Quality Assessment, and shows
better visual quality on various datasets. In addition, we evaluate the SISR
neural network trained with our degradation model on video super-resolution
(VSR) datasets. Compared to architectures specifically designed for the VSR
purpose, our method exhibits similar or better performance, evidencing that the
presented strategy on infusing video-based degradation is generalizable to
address more complicated compression artifacts even without temporal cues.
