mlsanityData to 7 Oct 2026

The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness

arXiv 2202.04147 ↗First posted 8 Feb 2022cs.IT cs.LG eess.IVcited by 21

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Abstract (arXiv)

A rate-distortion-perception (RDP) tradeoff has recently been proposed by Blau and Michaeli and also Matsumoto. Focusing on the case of perfect realism, which coincides with the problem of distribution-preserving lossy compression studied by Li et al., a coding theorem for the RDP tradeoff that allows for a specified amount of common randomness between the encoder and decoder is provided. The existing RDP tradeoff is recovered by allowing for the amount of common randomness to be infinite. The quadratic Gaussian case is examined in detail.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

10 of 29 references.

References · 10

  1. 2017Improved Training of Wasserstein GANscited by 2,223
  2. 2017The Perception-Distortion Tradeoffcited by 582
  3. 2018Generative Adversarial Networks for Extreme Learned Image Compressioncited by 213
  4. 2019Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoffcited by 207
  5. 2017Real-Time Adaptive Image Compressioncited by 127
  6. 2018Deep Generative Models for Distribution-Preserving Lossy Compressioncited by 59
  7. 2021Universal Rate-Distortion-Perception Representations for Lossy Compressioncited by 46
  8. 2021A coding theorem for the rate-distortion-perception functioncited by 30
  9. 2021On the advantages of stochastic encoderscited by 28
  10. 2020Neural Networks Optimally Compress the Sawbridgecited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 6; 2025: 2; 2026: 7.

21 papers.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2022Lossy Compression with Gaussian Diffusioncited by 87
  3. 2023Wasserstein Distortion: Unifying Fidelity and Realismcited by 8
  4. 2024Rate-Distortion-Perception Tradeoff Based on the Conditional-Distribution Perception Measurecited by 8
  5. 2023Conditional Perceptual Quality Preserving Image Compressioncited by 6
  6. 2024The Rate-Distortion-Perception Trade-off: The Role of Private Randomnesscited by 6
  7. 2023On the Computation of the Gaussian Rate-Distortion-Perception Functioncited by 5
  8. 2024Output-Constrained Lossy Source Coding With Application to Rate-Distortion-Perception Theorycited by 5
  9. 2024Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantizationcited by 4
  10. 2023Rate-Distortion-Perception Theory for Semantic Communicationcited by 2
  11. 2024Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Modelscited by 2
  12. 2025Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoffcited by 2
9 more
  1. 2025Rate-Distortion-Perception Theory for the Quadratic Wasserstein Spacecited by 2
  2. 2024Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metricscited by 1
  3. 2026On the Rate-Distortion-Complexity Tradeoff for Semantic Communicationcited by 1
  4. 2026Training-Free Rate-Distortion-Perception Traversal With Diffusioncited by 0
  5. 2026Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realismcited by 0
  6. 2026Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimizationcited by 0
  7. 2026Rate-Distortion-Perception Theory: Redefining the Fundamental Limits of Information Representationcited by 0
  8. 2026On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generatorscited by 0
  9. 2026The Hidden Perception Constraint in Task-Aware Compressioncited by 0