通过随机分布式函数计算的低延迟现实主义:香农理论方法
Onur Günlü1,2, Maciej Skorski3, H Vincent Poor4
1Lehrstuhl für Nachrichtentechnik, Technische Universität Dortmund, 44227 Dortmund, Germany.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
语义通信通过传达数据意义来提高效率. 本研究描述了图像压缩的速率-扭曲-感知权衡,考虑了侧信息和安全性,为低延迟系统提供了指导.
科学领域:
- 信息理论 信息理论
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
背景情况:
- 语义通信优先考虑数据的意义,而不是精确的复制,以提高效率.
- 图像压缩在平衡精度,感知忠实度和资源限制 (延迟/带宽) 方面面临挑战.
研究的目的:
- 用语义通信框架分析图像压缩中的速率-扭曲-感知 (RDP) 权衡.
- 在各种实际条件下建立RDP区域的有限块长特征.
主要方法:
- 利用随机分布式函数计算 (RDFC) 框架来合成随机性和塑造输出分布.
- 导出了RDP区域的有限块长特征,量化了速率,扭曲和感知之间的相互作用.
- 集成的扩展用于共享侧面信息和强有力的保密对抗对手,包括量子能力.
主要成果:
- 量化了非对称体制中的RDP区域,揭示了通信速率,扭曲和感知忠实度之间的相互作用.
- 在完美的现实主义约束下确定了非对称的RDP区域.
- 证明了副信息,有限块长效应和保密要求对可实现性能的影响.
结论:
- 该研究为开发低延迟,安全和现实意识的图像压缩系统提供了可操作的见解.
- 这些发现适用于需要感知忠实性的生成建模系统的进步.
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