The work characterizes the scalar Gaussian Rényi rate-distortion-perception-privacy tradeoff under indirect observation and introduces a conditional privacy measure that avoids penalizing legitimate semantic recovery.
On the rate-distortion-perception function
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Collision fiber sizes determine precise zero-error compression bounds and rate-distortion laws for semantic identity, establishing symbolic mechanisms as necessary complements to non-injective neural representations.
A synonymous source coding architecture and variational inference framework derive the rate-distortion-perception tradeoff by treating perception as recovery of any admissible synonymous sample.
A training-free diffusion-based method with RCC module and score-scaled PF-ODE decoder achieves optimal RDP in the Gaussian case and allows empirical traversal of the ternary tradeoff surface.
citing papers explorer
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R\'enyi Rate-Distortion-Perception-Privacy Tradeoff under Indirect Observation
The work characterizes the scalar Gaussian Rényi rate-distortion-perception-privacy tradeoff under indirect observation and introduces a conditional privacy measure that avoids penalizing legitimate semantic recovery.
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Semantic Identity Compression: Zero-Error Laws, Rate-Distortion, and Neurosymbolic Necessity
Collision fiber sizes determine precise zero-error compression bounds and rate-distortion laws for semantic identity, establishing symbolic mechanisms as necessary complements to non-injective neural representations.
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A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
A synonymous source coding architecture and variational inference framework derive the rate-distortion-perception tradeoff by treating perception as recovery of any admissible synonymous sample.
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Training-Free Rate-Distortion-Perception Traversal With Diffusion
A training-free diffusion-based method with RCC module and score-scaled PF-ODE decoder achieves optimal RDP in the Gaussian case and allows empirical traversal of the ternary tradeoff surface.