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

Saiping Zhang; Luis Herranz; Marta Mrak; Marc Górriz Blanch; Shuai Wan; Fuzheng Yang

ICASSP 2022accepted0 citations

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

Saiping Zhang, Luis Herranz, Marta Mrak, Marc Górriz Blanch, Shuai Wan, Fuzheng Yang

Abstract

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.

BibTeX

@inproceedings{icassp2022_dcnganadeformabl,
  title = {DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video},
  author = {Saiping Zhang and Luis Herranz and Marta Mrak and Marc Górriz Blanch and Shuai Wan and Fuzheng Yang},
  booktitle = {ICASSP 2022},
  year = {2022}
}