Dr. Yongxiong Ren Profile

Dr. Yongxiong Ren

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Proceedings Article | 31 May 2022 Presentation + Paper

A generative adversarial network for video compression

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

Proceedings Volume 12097, 120970E (2022) https://doi.org/10.1117/12.2618714

KEYWORDS: Video coding, Image compression, Video compression, Video, Motion models, Distortion, Image quality, Machine learning, Computer vision technology, Visual communications

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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.

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