  Recent years have witnessed the significant development of learning-based
video compression methods, which aim at optimizing objective or perceptual
quality and bit rates. In this paper, we introduce deep video compression with
perceptual optimizations (DVC-P), which aims at increasing perceptual quality
of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC)
network, but improves it with perceptual optimizations. Specifically, a
discriminator network and a mixed loss are employed to help our network trade
off among distortion, perception and rate. Furthermore, nearest-neighbor
interpolation is used to eliminate checkerboard artifacts which can appear in
sequences encoded with DVC frameworks. Thanks to these two improvements, the
perceptual quality of decoded sequences is improved. Experimental results
demonstrate that, compared with the baseline DVC, our proposed method can
generate videos with higher perceptual quality achieving 12.27% reduction in a
perceptual BD-rate equivalent, on average.
