一种基于双阶段学习的新型视频压缩方法
Dan Shao1, Ning Wang1, Pu Chen1
1School of Computer Science and Technology, Changchun University, Changchun 130022, China.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
DeepBiVC是一种新的双向视频压缩模型,可以提高存储和传输效率. 这种深度学习方法显著提高了使用可逆神经网络和光流估计的压缩性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 数字信号处理 数字信号处理
背景情况:
- 视频数据的指数增长给存储和传输带来了重大挑战.
- 视频压缩技术对于高效管理大量视频内容至关重要.
- 现有的方法往往难以平衡压缩比和视觉质量.
研究的目的:
- 介绍DeepBiVC,一种新的双向视频压缩模型.
- 利用深度学习来提高视频压缩效率.
- 为了解决当前最先进的视频压缩技术的局限性.
主要方法:
- 视频数据分成五个连续的组.
- 阶段1:使用可逆神经网络 (INN) 来压缩第一和最后的图像.
- 第二阶段:通过双向光流估计压缩中间.
主要成果:
- 与现有方法相比,DeepBiVC表现出优越的性能.
- 实现了高峰信号噪声比 (PSNR) 和多尺度结构相似性指数测量 (MS-SSIM) 的指标.
- 在0.3bpp的VUG数据集中,DeepBiVC的PSNR达到了37.16和MS-SSIM的0.98.
结论:
- DeepBiVC 为高效的视频压缩提供了一个有前途的解决方案.
- 双向方法有效地利用时间冗余来实现更好的压缩.
- 该模型取得了最先进的结果,表明其实际适用性.
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