在All Intra HEVC中用于CTU分区的高级ANN架构
Jakub Kwaśniak1, Mateusz Majtka1, Mateusz Lorkiewicz1
1Institute of Multimedia Telecommunications, Poznan University of Technology, pl. M. Skłodowskiej-Curie 5, 60-965 Poznań, Poland.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究加速了使用人工神经网络 (ANN) 的视频编码. ResNet和DenseNet架构显著减少了HEVC CTU分区的编码时间,平衡了效率和速度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 视频压缩 视频压缩
背景情况:
- 视频编码器的复杂性需要参数优化.
- 神经网络越来越多地用于加速视频编码过程.
研究的目的:
- 评估ResNet和DenseNet架构,以加速在HEVC (高效视频编码) 中的CTU分区算法.
- 分析压缩效率,网络大小和编码时间缩短之间的权衡.
主要方法:
- 实施和评估各种ResNet和DenseNet类型的神经网络架构.
- 在CTU分区的HEVC All Intra模式上进行测试.
- 详尽的绩效分析.
主要成果:
- 通过使用拟议的神经网络架构,显著减少了编码时间.
- 评估了压缩效率和网络大小的影响.
- 在不同的架构中量化性能改进.
结论:
- ResNet和DenseNet架构为加速HEVC CTU分区提供了可行的解决方案.
- 架构选择会影响压缩效率,网络大小和速度.
- 讨论了硬件受限环境和小型设备的考虑因素.
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