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RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-off

arXiv 2509.24805 ↗First posted 29 Sep 2025eess.SP cs.AI cs.ITcited by 0

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Abstract (arXiv)

Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly detection. However, compression can potentially impair the detector's ability to distinguish between regular and irregular patterns due to information loss. Here we extend the information-theoretic framework introduced in [1] to simultaneously address the trade-off between the three features on which the effectiveness of the system depends: the effectiveness of compression, the amount of distortion it introduces, and the distinguishability between compressed normal signals and compressed anomalous signals. We leverage a Gaussian assumption to draw curves showing how moving on a Pareto surface helps administer such a trade-off better than simply relying on optimal rate-distortion compression and hoping that compressed signals can be distinguished from each other.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 74 references that are in the map. First posted on arXiv 29 Sep 2025.

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References · 22

  1. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  2. 2017The Perception-Distortion Tradeoffcited by 587
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  4. 2017Lossy Image Compression with Compressive Autoencoderscited by 294
  5. 2019Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoffcited by 211
  6. 2017Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representationscited by 152
  7. 2018Conditional Probability Models for Deep Image Compressioncited by 133
  8. 2020Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analyticscited by 80
  9. 2020Learning End-to-End Lossy Image Compression: A Benchmarkcited by 60
  10. 2018Towards Image Understanding from Deep Compression without Decodingcited by 52
  11. 2020End-to-end Learning of Compressible Featurescited by 43
  12. 2018Deep Convolutional AutoEncoder-based Lossy Image Compressioncited by 42
10 more
  1. 2023QARV: Quantization-Aware ResNet VAE for Lossy Image Compressioncited by 32
  2. 2016Disciplined Convex-Concave Programmingcited by 29
  3. 2021Towards End-to-End Image Compression and Analysis with Transformerscited by 26
  4. 2021Adversarially Robust One-class Novelty Detectioncited by 24
  5. 2020The Rate-Distortion-Accuracy Tradeoff: JPEG Case Studycited by 22
  6. 2023VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Visioncited by 13
  7. 2018Real-Time Nonparametric Anomaly Detection in High-Dimensional Settingscited by 10
  8. 2020Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detectioncited by 10
  9. 2024A Rate-Distortion-Classification Approach for Lossy Image Compressioncited by 3
  10. 2021Anomaly Detection based on Compressed Data: an Information Theoretic Characterizationcited by 2

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