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Integrating perceptual quality into rate-distortion-perception (RDP) frameworks for generative models may increase data rates. However, even a small rate increase significantly boosts reconstruction quality in intelligent communication networks.

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Area of Science:

  • Information Theory
  • Machine Learning
  • Communication Systems

Background:

  • Traditional rate-distortion (RD) theory balances compression rate and reconstruction distortion.
  • The rate-distortion-perception (RDP) framework incorporates perceptual fidelity, crucial for generative models in communication.
  • Current RDP models face challenges in balancing distortion constraints with essential perceptual fidelity.

Purpose of the Study:

  • To clarify the impact of perceptual constraints on data rates within communication systems.
  • To analyze the role of randomness in generative models under RDP.
  • To demonstrate the benefits of integrating RDP with generative modeling for intelligent communication.

Main Methods:

  • Information-theoretic analysis of rate-distortion-perception trade-offs.
  • Examination of generative models within communication network constraints.
  • Evaluation of perceptual quality enhancement with modest rate increases.

Main Results:

  • Incorporating perceptual constraints does not decrease, and may increase, the required data rate.
  • Generative models can significantly improve perceptual quality with a modest rate increase.
  • Intelligent communication networks are projected to experience increased traffic due to perceptual quality considerations.

Conclusions:

  • The RDP framework is vital for advancing compression and semantic communication research.
  • Synergies between generative modeling and communication, guided by information theory, highlight the benefits of intelligent systems.
  • The study advocates for the adoption of the RDP framework in future communication research and development.