Technology Category
Computer Vision: Computational Photography, Image & Video SynthesisMachine Learning: Information TheoryCognitive Modeling & Cognitive Systems: Neural Spike Coding
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
🤖 AI Summary
Traditional rate–distortion theory struggles to account for perceptual quality in compression, while existing rate–distortion–perception (RDP) frameworks lack a solid theoretical foundation. This work addresses this gap by introducing a semantic perspective based on synonymous information, defining perceptual reconstruction as the recovery of any sample within the ideal synonymous set to which the source signal belongs. It proposes a synonymous source coding architecture and a synonymous variational inference (SVI) analytical framework. Leveraging the principle of synonymy–perception consistency, the study theoretically establishes, for the first time, the alignment between perceptual optimization and semantic recognition, naturally yielding a distribution divergence term. This leads to a synonymous rate–distortion–perception trade-off theory that subsumes both classical rate–distortion theory and current RDP formulations, demonstrating the theoretical advantages of synonymous coding in perceptual compression.
Technology Category
Application Category
📝 Abstract
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 rather than a theoretically-derived 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 correspondingly 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 its derivation result, we prove a synonymous RDP tradeoff for the proposed synonymous source coding. 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.
Problem
Research questions and friction points this paper is trying to address.
rate-distortion-perception
perceptual quality
semantic information
synonymous set
theoretical foundation
Innovation
Methods, ideas, or system contributions that make the work stand out.
synonymous source coding
rate-distortion-perception tradeoff
semantic information
variational inference
perceptual quality
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