VHS to HDTV Video Translation using Multi-task Adversarial Learning
This work provides a computational solution for preserving and enhancing valuable VHS video archives, benefiting historians, archivists, and general consumers with old home videos.
This paper addresses the problem of translating low-quality VHS video to high-definition television (HDTV) quality. The authors developed an unsupervised multi-task adversarial learning model that combines super-resolution and color transfer, demonstrating its effectiveness both qualitatively and quantitatively.
There are large amount of valuable video archives in Video Home System (VHS) format. However, due to the analog nature, their quality is often poor. Compared to High-definition television (HDTV), VHS video not only has a dull color appearance but also has a lower resolution and often appears blurry. In this paper, we focus on the problem of translating VHS video to HDTV video and have developed a solution based on a novel unsupervised multi-task adversarial learning model. Inspired by the success of generative adversarial network (GAN) and CycleGAN, we employ cycle consistency loss, adversarial loss and perceptual loss together to learn a translation model. An important innovation of our work is the incorporation of super-resolution model and color transfer model that can solve unsupervised multi-task problem. To our knowledge, this is the first work that dedicated to the study of the relation between VHS and HDTV and the first computational solution to translate VHS to HDTV. We present experimental results to demonstrate the effectiveness of our solution qualitatively and quantitatively.