论文标题
基于多级小波的生成对抗网络，用于压缩视频的感知质量增强
Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video
论文作者
论文摘要
过去的几年，通过深度学习见证了视频质量增强的快速发展。现有方法主要集中于提高压缩视频的客观质量，同时忽略其感知质量。在本文中，我们专注于提高压缩视频的感知质量。我们的主要观察结果是，提高感知质量主要依赖于恢复小波域中的高频子带。因此，我们提出了一种基于多级小波数据包转换（WPT）的新型生成对抗网络（GAN），以增强压缩视频的感知质量，该视频称为多级小波小波（MW-GAN）。在MW-GAN中，我们首先使用金字塔结构进行运动补偿以获取时间信息。然后，我们提出了一个带有小波致密残留块（WDRB）的小波重建网络，以恢复高频细节。此外，通过WPT添加了MW-GAN的对抗性损失，以进一步鼓励视频帧的高频细节恢复。实验结果证明了我们方法的优势。
The past few years have witnessed fast development in video quality enhancement via deep learning. Existing methods mainly focus on enhancing the objective quality of compressed video while ignoring its perceptual quality. In this paper, we focus on enhancing the perceptual quality of compressed video. Our main observation is that enhancing the perceptual quality mostly relies on recovering high-frequency sub-bands in wavelet domain. Accordingly, we propose a novel generative adversarial network (GAN) based on multi-level wavelet packet transform (WPT) to enhance the perceptual quality of compressed video, which is called multi-level wavelet-based GAN (MW-GAN). In MW-GAN, we first apply motion compensation with a pyramid architecture to obtain temporal information. Then, we propose a wavelet reconstruction network with wavelet-dense residual blocks (WDRB) to recover the high-frequency details. In addition, the adversarial loss of MW-GAN is added via WPT to further encourage high-frequency details recovery for video frames. Experimental results demonstrate the superiority of our method.
