基于同义变分视角的率-失真-感知权衡
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications（信息与通信技术联合实验室，教育部，北京邮电大学）
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出同义变分框架，通过重构理想同义集内的样本实现感知重建，建立同义源编码架构，证明了同义性与感知一致性原则，揭示了同义率-失真-感知权衡的理论基础。
AI中文摘要：
自然信号压缩的基本限制传统上通过经典率-失真（RD）理论通过编码率与重构失真之间的权衡来表征，而率-失真-感知（RDP）框架引入了基于分歧的感知质量度量作为建模原则而非理论推导原则，其理论来源尚不明确。本文受基于同义性的语义信息视角启发，将感知重建重新表述为在与源相关的理想同义集（synset）内恢复任何可接受的样本，而非源样本本身，并相应地建立了同义源编码架构。在此基础上，我们开发了同义变分推断（SVI）分析框架，包含同义变分下界（SVLBO）以对面向synset的压缩进行可处理分析。在此框架内，我们建立了同义性-感知一致性原则，证明最优识别语义信息在理论上与感知优化一致。基于其推导结果，我们证明了所提出的同义源编码的同义RDP权衡。这些分析结果表明，分布分歧项自然源于基于synset的重构目标，澄清了其与现有RDP公式和经典RD理论的兼容性，并指出了同义源编码的潜在优势。
英文摘要：
The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff between coding rate and reconstruction distortion, while the rate-distortion-perception (RDP) framework introduces a divergence-based measure of perceptual quality as a modeling principle, leaving its theoretical origin unclear. In this paper, motivated by a synonymity-based semantic information perspective, we reformulate perceptual reconstruction as recovering any admissible sample within an ideal synonymous set (synset) associated with the source, rather than the source sample itself, and establish a synonymous source coding architecture. On this basis, we develop a synonymous variational inference (SVI) analysis framework with a synonymous variational lower bound (SVLBO) for tractable analysis of synset-oriented compression. Within this framework, we establish a synonymity-perception consistency principle, showing that optimal identification of semantic information is theoretically consistent with perceptual optimization. Based on this result, we further derive a tight-bound synonymous source coding rate characterization and show that its Jensen-limit relaxation leads to a synonymous rate-distortion-perception form for practical optimization. These analytical results show that the distributional divergence term arises naturally from the synset-based reconstruction objective, clarify its compatibility with existing RDP formulations and classical RD theory, and suggest the potential advantages of synonymous source coding.
