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Summary
This summary is machine-generated.

Semantic communication enhances efficiency by conveying data significance. This study characterizes the rate-distortion-perception trade-off for image compression, considering side information and security, offering guidance for low-latency systems.

Keywords:
randomized distributed function computation (RDFC)rate–distortion–perception (RDP)realism for generative modelingrealism for image compressionultra-efficient semantic communications

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Area of Science:

  • Information Theory
  • Computer Vision
  • Data Compression

Background:

  • Semantic communication prioritizes data significance over exact reproduction for efficiency.
  • Image compression faces challenges in balancing accuracy, perceptual fidelity, and resource constraints (latency/bandwidth).

Purpose of the Study:

  • To analyze the rate-distortion-perception (RDP) trade-off in image compression using a semantic communication framework.
  • To establish finite-blocklength characterizations of the RDP region under various practical conditions.

Main Methods:

  • Utilized the randomized distributed function computation (RDFC) framework for synthesizing randomness and shaping output distributions.
  • Derived finite-blocklength characterizations of the RDP region, quantifying the interplay between rate, distortion, and perception.
  • Incorporated extensions for shared side information and strong secrecy against adversaries, including quantum-capable ones.

Main Results:

  • Quantified the RDP region in non-asymptotic regimes, revealing interactions between communication rate, distortion, and perceptual fidelity.
  • Identified the asymptotic RDP region under a perfect realism constraint.
  • Demonstrated the influence of side information, finite blocklength effects, and secrecy demands on achievable performance.

Conclusions:

  • The study provides actionable insights for developing low-latency, secure, and realism-aware image compression systems.
  • The findings are applicable to advancements in generative modeling systems requiring perceptual faithfulness.