  In this paper, we propose a deformable convolution-based generative
adversarial network (DCNGAN) for perceptual quality enhancement of compressed
videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared
with optical flows, deformable convolutions are more effective and efficient to
align frames. Deformable convolutions can operate on multiple frames, thus
leveraging more temporal information, which is beneficial for enhancing the
perceptual quality of compressed videos. Instead of aligning frames in a
pairwise manner, the deformable convolution can process multiple frames
simultaneously, which leads to lower computational complexity. Experimental
results demonstrate that the proposed DCNGAN outperforms other state-of-the-art
compressed video quality enhancement algorithms.
