论文标题
DCNGAN：一种可变形的基于卷积的GAN，具有QP改编，以增强压缩视频的感知质量
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
论文作者
论文摘要
在本文中，我们提出了一个基于可变形的生成对抗网络（DCNGAN），以增强压缩视频的感知质量。 DCNGAN还适应量化参数（QPS）。与光流相比，可变形的卷积比对齐帧更有效。可变形的卷积可以在多个框架上运行，从而利用更多的时间信息，这有助于提高压缩视频的感知质量。可变形的卷积不是以成对的方式对齐帧，而是可以同时处理多个帧，从而导致降低计算复杂性。实验结果表明，拟议的DCNGAN优于其他最先进的压缩视频质量增强算法。
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.
