  We propose a novel frame prediction method using a deep neural network (DNN),
with the goal of improving video coding efficiency. The proposed DNN makes use
of decoded frames, at both encoder and decoder, to predict textures of the
current coding block. Unlike conventional inter-prediction, the proposed method
does not require any motion information to be transferred between the encoder
and the decoder. Still, both uni-directional and bi-directional prediction are
possible using the proposed DNN, which is enabled by the use of the temporal
index channel, in addition to color channels. In this study, we developed a
jointly trained DNN for both uni- and bi- directional prediction, as well as
separate networks for uni- and bi-directional prediction, and compared the
efficacy of both approaches. The proposed DNNs were compared with the
conventional motion-compensated prediction in the latest video coding standard,
HEVC, in terms of BD-Bitrate. The experiments show that the proposed joint DNN
(for both uni- and bi-directional prediction) reduces the luminance bitrate by
about 4.4%, 2.4%, and 2.3% in the Low delay P, Low delay, and Random access
configurations, respectively. In addition, using the separately trained DNNs
brings further bit savings of about 0.3%-0.5%.
