Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications
Traditional rate–distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate–distortion–perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, h...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/27/4/373 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850143842941009920 |
|---|---|
| author | Xueyan Niu Bo Bai Nian Guo Weixi Zhang Wei Han |
| author_facet | Xueyan Niu Bo Bai Nian Guo Weixi Zhang Wei Han |
| author_sort | Xueyan Niu |
| collection | DOAJ |
| description | Traditional rate–distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate–distortion–perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research. |
| format | Article |
| id | doaj-art-981e086567674aeeb8137e7e6e05e6cf |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-981e086567674aeeb8137e7e6e05e6cf2025-08-20T02:28:33ZengMDPI AGEntropy1099-43002025-03-0127437310.3390/e27040373Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent CommunicationsXueyan Niu0Bo Bai1Nian Guo2Weixi Zhang3Wei Han4Theory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, ChinaTheory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, ChinaTheory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, ChinaTheory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, ChinaTheory Lab, 2012 Labs, Huawei Technologies Co., Ltd., No. 3 Xinxi Rd., Beijing 100085, ChinaTraditional rate–distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate–distortion–perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research.https://www.mdpi.com/1099-4300/27/4/373rate–distortion–perception trade-offperceptual fidelitylossy compressionAI-empowered communication |
| spellingShingle | Xueyan Niu Bo Bai Nian Guo Weixi Zhang Wei Han Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications Entropy rate–distortion–perception trade-off perceptual fidelity lossy compression AI-empowered communication |
| title | Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications |
| title_full | Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications |
| title_fullStr | Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications |
| title_full_unstemmed | Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications |
| title_short | Rate–Distortion–Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications |
| title_sort | rate distortion perception trade off in information theory generative models and intelligent communications |
| topic | rate–distortion–perception trade-off perceptual fidelity lossy compression AI-empowered communication |
| url | https://www.mdpi.com/1099-4300/27/4/373 |
| work_keys_str_mv | AT xueyanniu ratedistortionperceptiontradeoffininformationtheorygenerativemodelsandintelligentcommunications AT bobai ratedistortionperceptiontradeoffininformationtheorygenerativemodelsandintelligentcommunications AT nianguo ratedistortionperceptiontradeoffininformationtheorygenerativemodelsandintelligentcommunications AT weixizhang ratedistortionperceptiontradeoffininformationtheorygenerativemodelsandintelligentcommunications AT weihan ratedistortionperceptiontradeoffininformationtheorygenerativemodelsandintelligentcommunications |