Dynamically Expanded CNN Array for Video Coding
This addresses video streaming efficiency for users and platforms, but is incremental as it builds on existing codecs.
The paper tackled improving video coding by using convolutional neural networks to refine standard codec outputs, resulting in enhanced quality and compression efficiency.
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.