Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. BVI-DVC is a new extensive and representative video database for training CNN-based coding tools, which contains 800 sequences at various spatial resolutions from 270p to 2160p. Experimental results show that the database produces significant improvements in terms of coding gains over three existing (commonly used) image/video training databases.
Date made available31 Mar 2020
PublisherUniversity of Bristol


  • video compression
  • machine learning

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