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.

翻译：近些年来,以学习为基础的录相压缩方法有了重大发展,其目的是优化目标或感知质量和比特率。在本文中,我们采用了深层视频压缩方法,采用感知优化(DVC-P),目的是提高解码视频的感知质量。我们提议的DVC-P基于深视频压缩(DVC)网络,但以感知优化(DVC)改进了它。具体地说,使用歧视网络和混合损失来帮助我们的网络交换扭曲、感知和率。此外,使用近邻的内推法来消除能够出现在DVC框架编码序列中的检查板文物。由于这两项改进,解码序列的感知质量得到了改进。实验结果表明,与基线DVC相比,我们拟议的方法可以产生更高感知质量的视频,平均在感知性BD率上减少了12.27 %。