Blurry Video Compression: A Trade-off between Visual Enhancement and
  Data Compression
Dawit Mureja Argaw Junsik Kim In So Kweon
Existing video compression (VC) methods primarily aim to reduce the spatial
and temporal redundancies between consecutive frames in a video while
preserving its quality. In this regard, previous works have achieved remarkable
results on videos acquired under specific settings such as instant (known)
exposure time and shutter speed which often result in sharp videos. However,
when these methods are evaluated on videos captured under different temporal
priors, which lead to degradations like motion blur and low frame rate, they
fail to maintain the quality of the contents. In this work, we tackle the VC
problem in a general scenario where a given video can be blurry due to
predefined camera settings or dynamics in the scene. By exploiting the natural
trade-off between visual enhancement and data compression, we formulate VC as a
min-max optimization problem and propose an effective framework and training
strategy to tackle the problem. Extensive experimental results on several
benchmark datasets confirm the effectiveness of our method compared to several
state-of-the-art VC approaches.