CGVC-T: Contextual Generative Video Compression With Transformers

IF 3.7 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-04-10 DOI:10.1109/JETCAS.2024.3387301
Pengli Du;Ying Liu;Nam Ling
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Abstract

With the high demands for video streaming, recent years have witnessed a growing interest in utilizing deep learning for video compression. Most existing neural video compression approaches adopt the predictive residue coding framework, which is sub-optimal in removing redundancy across frames. In addition, purely minimizing the pixel-wise differences between the raw frame and the decompressed frame is ineffective in improving the perceptual quality of videos. In this paper, we propose a contextual generative video compression method with transformers (CGVC-T), which adopts generative adversarial networks (GAN) for perceptual quality enhancement and applies contextual coding to improve coding efficiency. Besides, we employ a hybrid transformer-convolution structure in the auto-encoders of the CGVC-T, which learns both global and local features within video frames to remove temporal and spatial redundancy. Furthermore, we introduce novel entropy models to estimate the probability distributions of the compressed latent representations, so that the bit rates required for transmitting the compressed video are decreased. The experiments on HEVC, UVG, and MCL-JCV datasets demonstrate that the perceptual quality of our CGVC-T in terms of FID, KID, and LPIPS scores surpasses state-of-the-art learned video codecs, the industrial video codecs x264 and x265, as well as the official reference software JM, HM, and VTM. Our CGVC-T also offers superior DISTS scores among all compared learned video codecs.
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CGVC-T:使用变形器的上下文生成式视频压缩
近年来,随着视频流的高需求,人们对利用深度学习进行视频压缩的兴趣与日俱增。现有的神经视频压缩方法大多采用预测残差编码框架,这种框架在消除帧间冗余方面不够理想。此外,纯粹最小化原始帧与解压缩帧之间的像素差异也无法有效改善视频的感知质量。本文提出了一种带变换器的上下文生成式视频压缩方法(CGVC-T),它采用生成式对抗网络(GAN)来提高感知质量,并应用上下文编码来提高编码效率。此外,我们在 CGVC-T 的自动编码器中采用了混合变换器-卷积结构,学习视频帧内的全局和局部特征,以消除时间和空间冗余。此外,我们还引入了新颖的熵模型来估计压缩潜在表示的概率分布,从而降低了传输压缩视频所需的比特率。在 HEVC、UVG 和 MCL-JCV 数据集上的实验表明,我们的 CGVC-T 在 FID、KID 和 LPIPS 分数方面的感知质量超过了最先进的学习视频编解码器、工业视频编解码器 x264 和 x265,以及官方参考软件 JM、HM 和 VTM。在所有比较过的学习视频编解码器中,我们的 CGVC-T 在 DISTS 分数上也更胜一筹。
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来源期刊
CiteScore
8.50
自引率
2.20%
发文量
86
期刊介绍: The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.
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