Multi-Level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video

Abstract

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 videos while ignoring its perceptual quality. In this paper, we focus on enhancing the perceptual quality of compressed videos. Our main observation is that enhancing the perceptual quality mostly relies on recovering the 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 videos, which is called multi-level wavelet-based GAN (MW-GAN). In the 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 over state-of-the-art methods in enhancing the perceptual quality of compressed videos.