PT Unknown
AU Saiping Zhang
   Luis Herranz
   Marta Mrak
   Marc Gorriz Blanch
   Shuai Wan
   Fuzheng Yang
TI DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
BT 47th International Conference on Acoustics, Speech, and Signal Processing
PY 2022
DI 10.1109/ICASSP43922.2022.9746702
AB 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.
ER