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In this paper, we propose a deformable convolution-based generativeadversarial network (DCNGAN) for perceptual quality enhancement of compressedvideos. DCNGAN is also adaptive to the quantization parameters (QPs). Comparedwith optical flows, deformable convolutions are more effective and efficient toalign frames. Deformable convolutions can operate on multiple frames, thusleveraging more temporal information, which is beneficial for enhancing theperceptual quality of compressed videos. Instead of aligning frames in apairwise manner, the deformable convolution can process multiple framessimultaneously, which leads to lower computational complexity. Experimentalresults demonstrate that the proposed DCNGAN outperforms other state-of-the-artcompressed video quality enhancement algorithms.

DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video