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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.

Alternative title A Training Database for Deep Video Compression
Creator(s) Fan Zhang, Di Ma, David Bull
Publication date 01 Apr 2020
Language eng
Publisher University of Bristol
Licence Non-Commercial Government Licence for public sector information
DOI 10.5523/bris.3hj4t64fkbrgn2ghwp9en4vhtn
Citation Fan Zhang, Di Ma, David Bull (2020): BVI-DVC.
Total size 85.6 GiB