  Video coding is a critical step in all popular methods of streaming video.
Marked progress has been made in video quality, compression, and computational
efficiency. Recently, there has been an interest in finding ways to apply
techniques form the fast-progressing field of machine learning to further
improve video coding.
  We present a method that uses convolutional neural networks to help refine
the output of various standard coding methods. The novelty of our approach is
to train multiple different sets of network parameters, with each set
corresponding to a specific, short segment of video. The array of network
parameter sets expands dynamically to match a video of any length. We show that
our method can improve the quality and compression efficiency of standard video
codecs.
