  We present an end-to-end trainable framework for P-frame compression in this
paper. A joint motion vector (MV) and residual prediction network MV-Residual
is designed to extract the ensembled features of motion representations and
residual information by treating the two successive frames as inputs. The prior
probability of the latent representations is modeled by a hyperprior
autoencoder and trained jointly with the MV-Residual network. Specially, the
spatially-displaced convolution is applied for video frame prediction, in which
a motion kernel for each pixel is learned to generate predicted pixel by
applying the kernel at a displaced location in the source image. Finally, novel
rate allocation and post-processing strategies are used to produce the final
compressed bits, considering the bits constraint of the challenge. The
experimental results on validation set show that the proposed optimized
framework can generate the highest MS-SSIM for P-frame compression competition.
