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

arXiv: https://arxiv.org/abs/2202.04147 · HTML: https://mlsanity.com/paper/2202.04147/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017)
- [Deep Generative Models for Distribution-Preserving Lossy Compression](https://mlsanity.com/paper/1805.11057/) (2018)
- [Universal Rate-Distortion-Perception Representations for Lossy Compression](https://mlsanity.com/paper/2106.10311/) (2021)
- [A coding theorem for the rate-distortion-perception function](https://mlsanity.com/paper/2104.13662/) (2021)
- [On the advantages of stochastic encoders](https://mlsanity.com/paper/2102.09270/) (2021)
- [Neural Networks Optimally Compress the Sawbridge](https://mlsanity.com/paper/2011.05065/) (2020)

## Built on by (most cited first, 21 of 21)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Lossy Compression with Gaussian Diffusion](https://mlsanity.com/paper/2206.08889/) (2022)
- [Wasserstein Distortion: Unifying Fidelity and Realism](https://mlsanity.com/paper/2310.03629/) (2023)
- [Rate-Distortion-Perception Tradeoff Based on the Conditional-Distribution Perception Measure](https://mlsanity.com/paper/2401.12207/) (2024)
- [Conditional Perceptual Quality Preserving Image Compression](https://mlsanity.com/paper/2308.08154/) (2023)
- [The Rate-Distortion-Perception Trade-off: The Role of Private Randomness](https://mlsanity.com/paper/2404.01111/) (2024)
- [On the Computation of the Gaussian Rate-Distortion-Perception Function](https://mlsanity.com/paper/2311.09190/) (2023)
- [Output-Constrained Lossy Source Coding With Application to Rate-Distortion-Perception Theory](https://mlsanity.com/paper/2403.14849/) (2024)
- [Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization](https://mlsanity.com/paper/2409.02388/) (2024)
- [Rate-Distortion-Perception Theory for Semantic Communication](https://mlsanity.com/paper/2312.05437/) (2023)
- [Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models](https://mlsanity.com/paper/2401.07957/) (2024)
- [Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/2503.17558/) (2025)
- [Rate-Distortion-Perception Theory for the Quadratic Wasserstein Space](https://mlsanity.com/paper/2504.17236/) (2025)
- [Characterization of the Distortion-Perception Tradeoff for Finite Channels with Arbitrary Metrics](https://mlsanity.com/paper/2402.02265/) (2024)
- [On the Rate-Distortion-Complexity Tradeoff for Semantic Communication](https://mlsanity.com/paper/2602.14481/) (2026)
- [Training-Free Rate-Distortion-Perception Traversal With Diffusion](https://mlsanity.com/paper/2603.04005/) (2026)
- [Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism](https://mlsanity.com/paper/2604.20245/) (2026)
- [Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization](https://mlsanity.com/paper/2606.13366/) (2026)
- [Rate-Distortion-Perception Theory: Redefining the Fundamental Limits of Information Representation](https://mlsanity.com/paper/2607.17232/) (2026)
- [On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators](https://mlsanity.com/paper/2609.24377/) (2026)
- [The Hidden Perception Constraint in Task-Aware Compression](https://mlsanity.com/paper/2609.35684/) (2026)

## 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.