A generative adversarial network for video compression

Pengli Du; Ying Liu; Nam Ling; Lingzhi Liu; Yongxiong Ren; Ming Kai Hsu

doi:10.1117/12.2618714

31 May 2022 A generative adversarial network for video compression

Pengli Du, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming Kai Hsu

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970E (2022) https://doi.org/10.1117/12.2618714
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Video data has occupied people’s daily professional and entertainment activities. It imposes a big pressure on the Internet bandwidth. Hence, it is important to develop effective video coding techniques to compress video data as much as possible and save the transmission bandwidth, while still providing visually pleasing decoded videos. In conventional video coding such as the high efficiency video coding (HEVC) and the versatile video coding (VVC), signal processing and information theory-based techniques are mainstream. In recent years, thanks to the advances in deep learning, a lot of deep learning-based approaches have emerged for image and video compression. In particular, the generative adversarial networks (GAN) have shown superior performance for image compression. The decoded images are usually sharper and present more details than pure convolutional neural network (CNN)-based image compression and are more consistent with human visual system (HVS). Nevertheless, most existing GAN-based methods are for still image compression, and truly little research investigates the potential of GAN for video compression. In this work, we propose a novel inter-frame video coding scheme that compresses both reference frames and target (residue) frames by GAN. Since residue signals contain less energy, the proposed method effectively reduces the bit rates. Meanwhile, since we adopt adversarial learning, the perceptual quality of decoded target frames is well-preserved. The effectiveness of our proposed algorithm is demonstrated by experimental studies on common test video sequences.

Conference Presentation

Citation Download Citation

Pengli Du, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, and Ming Kai Hsu "A generative adversarial network for video compression", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970E (31 May 2022); https://doi.org/10.1117/12.2618714

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available