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  3. Rate-distortion Limits For Task-oriented Compression With Side Information.
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  3. Rate-distortion Limits For Task-oriented Compression With Side Information.

Related Experiment Video

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Rate-Distortion Limits for Task-Oriented Compression with Side Information.

Tao Guo1, Zhangyao Song1, Huihui Wu2,3

  • 1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China.

Entropy (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces semantic rate-distortion theory for task-oriented compression with side information. It establishes information-theoretic limits, demonstrating how side information improves compression and accuracy.

Keywords:
rate–distortion functionsemantic compressionside information

Related Experiment Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Area of Science:

  • Information Theory
  • Data Compression
  • Machine Learning

Background:

  • Task-oriented data compression often involves semantic information influencing observations indirectly.
  • Existing models typically lack side information or explicit handling of semantic segments.

Purpose of the Study:

  • To analyze the semantic rate-distortion problem with side information and two semantic segments.
  • To establish information-theoretic limits for compression rates and distortions.
  • To validate theoretical findings with deep learning-based image compression.

Main Methods:

  • Characterizing the rate-distortion function for semantic information.
  • Deriving rate-distortion functions under specific Markov conditions.
  • Implementing and evaluating a deep learning model for classification-oriented lossy image compression.

Main Results:

  • The information-theoretic limits for the semantic rate-distortion tradeoff were established.
  • Explicit rate-distortion functions were derived for binary classification and Gaussian-correlated scenarios.
  • Deep learning validation confirmed the effectiveness of side information on distortion and classification accuracy.

Conclusions:

  • The study provides a theoretical framework for semantic rate-distortion with side information.
  • Side information significantly enhances both data compression and task performance.
  • Semantic segmentation is rational and beneficial in lossy compression tasks.