在信息理论,生成模型和智能通信中的速率-扭曲-感知权衡
Xueyan Niu1, Bo Bai1, Nian Guo1
1Theory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, China.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
将感知质量整合到生成模型的速率-扭曲-感知 (RDP) 框架中,可能会增加数据速率. 然而,即使是小幅加息,也会显著提高智能通信网络的重建质量.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 通信系统 通信系统
背景情况:
- 传统的速率扭曲 (RD) 理论平衡了压缩速率和重建扭曲.
- 速率-扭曲-感知 (RDP) 框架包含感知忠实性,对于通信中的生成模型至关重要.
- 当前的RDP模型面临着平衡扭曲约束与基本感知忠实性的挑战.
研究的目的:
- 澄清感知约束对通信系统中的数据速率的影响.
- 分析RDP下的生成模型中随机性的作用.
- 展示整合RDP与智能通信生成模型的好处.
主要方法:
- 信息理论分析率-扭曲-感知权衡的权衡.
- 在通信网络约束下对生成模型的检查.
- 评估感知质量提升与适度的增速.
主要成果:
- 纳入感知约束不会减少,而且可能增加所需的数据速率.
- 生成模型可以显著提高感知质量,适度的增速.
- 智能通信网络预计将因感知质量考虑而增加流量.
结论:
- 乡村发展计划框架对于推进压缩和语义通信研究至关重要.
- 由信息理论指导的生成建模和通信之间的协同作用突出了智能系统的好处.
- 该研究主张在未来的通信研究和开发中采用RDP框架.
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