@inproceedings{69a2f4fb6f53444b807501190ad864d9,
title = "DCNGAN: A DEFORMABLE CONVOLUTION-BASED GAN WITH QP ADAPTATION FOR PERCEPTUAL QUALITY ENHANCEMENT OF COMPRESSED VIDEO",
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.",
keywords = "Compressed video perceptual quality enhancement, Deformable convolution, GAN, QP adaptation",
author = "Saiping Zhang and Luis Herranz and Marta Mrak and Blanch, {Marc G{\'o}rriz} and Shuai Wan and Fuzheng Yang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICASSP43922.2022.9746702",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2035--2039",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
}