一种基于深度变压器的快速CU分区方法,用于间模式VVC
概括
本研究介绍了基于变压器的模型TCP-Net,以加速多功能视频编码 (VVC) 之间的编码. 这种方法显著减少了编码时间,对速率扭曲性能的影响最小.
科学领域:
- 视频压缩标准 视频压缩标准
- 机器学习用于视频处理.
背景情况:
- 多功能视频编码 (VVC) 提供了提高效率,但面临着高计算复杂性.
- 四树加多类型树 (QTMT) 分区是VVC编码中的一个主要瓶.
研究的目的:
- 开发用于VVC互编码的快速编码单元 (CU) 分区方法.
- 为了加快VVC编码而没有显著的速率扭曲损失.
主要方法:
- 从各种视频序列建立了一个大型VVC模式间数据库.
- 提出了基于变压器的深度神经网络TCP-Net,用于CU分区预测.
- 为TCP-Net.net设计了一个两阶段结构化的输出和双监督的优化.
主要成果:
- 实现了46.89%至55.91%的VVC编码时间的减少.
- 与现有方法相比,证明了微不足道的速率扭曲退化.
- TCP-Net的性能优于其他最先进的快速CU分区方法.
结论:
- 拟议的数据驱动的TCP-Net有效地加快了VVC之间的编码.
- 变压器模型为优化视频压缩算法提供了一个有希望的方向.
- 这种方法为减少VVC编码复杂性的实际解决方案.
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