QP-自适应式双通道残余集成频率变压器用于VVC中的数据驱动的循环内波器
Cheng-Hsuan Yeh1, Chi-Ting Ni1, Kuan-Yu Huang1
1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
驱动增强了多功能视频编码 (VVC/H.266) 通过减少压缩文物适应性. 这种新的网络可以提高AI支持系统的视频质量,特别是在带宽有限的多媒体物联网 (M-IoT) 应用中.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 支持人工智能的嵌入式系统需要高效的视频处理,使多功能视频编码 (VVC/H.266) 对多媒体物联网 (M-IoT) 至关重要.
- VVC的基于区块的编码可能会导致压缩工件,现有的卷积神经网络 (CNN) 方法在不同的量化参数 (QP) 中与性能作斗争.
研究的目的:
- 提出DRIFT,一个新的QP适应性循环内过网络,旨在减少VVC中的压缩工件.
- 为了提高人工智能系统和M-IoT应用程序的视频处理效率和质量.
主要方法:
- 驱动集成一个轻量级的频率融合CNN (LFFCNN) 进行本地增强和一个Swin变压器用于全球环境.
- LFFCNN使用了八度卷曲和一种新型的残留区块 (FFRB),包括多尺度提取,QP适应性,频率融合和注意力机制.
- 引入了一个量化参数估计器 (QPE),以防止在相互编码的中过度增强.
主要成果:
- DRIFT实现了显著的BD率降低:内部编码的6.56%和内部编码的4.83%.
- 篮球训练序列显示高达10.90%的性能提升.
- 与以前的方法相比,LFFCNN减少了32%的模型大小,同时保持或提高了编码性能.
结论:
- 驱动有效地解决VVC压缩文物,在各种QP提供强大的性能.
- 拟议的LFFCNN和QPE有助于为现代AI应用程序提供高效和高质量的视频处理.
- DRIFT 代表了 VVC 的 QP 适应性过的重大进步,其性能优于现有的解决方案.
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