VHS to HDTV Video Translation using Multi-task Adversarial Learning

TL;DR

This paper introduces an unsupervised multi-task adversarial learning model that translates VHS videos into HDTV quality, enhancing resolution and color while preserving content, marking the first computational approach for this task.

Contribution

The work presents a novel multi-task GAN-based model incorporating super-resolution and color transfer for VHS to HDTV translation, pioneering in this specific application.

Findings

Effective qualitative and quantitative results

First computational solution for VHS to HDTV translation

Demonstrates improved video quality and color fidelity

Abstract

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.