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Guaranteed Image Classification via Goal-oriented Joint Semantic Source and Channel Coding

Wenchao Wu, Min Qiu, Yansha Deng, Jinhong Yuan

TL;DR

A goal-oriented joint semantic source and channel coding (G-JSSCC) framework that applies various levels of source coding compression and channel coding protection across image regions based on their semantic importance and defines a new metric, termed coding efficiency, to evaluate the effectiveness of the source and channel coding in the classification task.

Abstract

To enable critical applications such as remote diagnostics, image classification must be guaranteed under bandwidth constraints and unreliable wireless channels through joint source and channel coding (JSCC) design. However, most existing JSCC methods focus on minimizing image distortion, implicitly assuming that all image regions contribute equally to classification performance, thereby overlooking their varying importance for the task. In this paper, we propose a goal-oriented joint semantic source and channel coding (G-JSSCC) framework that applies \emph{various} levels of source coding compression and channel coding protection across image regions based on their semantic importance. Specifically, we design a semantic information extraction method that identifies and ranks various image regions based on their contributions to classification, where the contribution is measured by the shapely value from explainable artificial intelligence (AI). Based on that, we design a semantic source coding and a semantic channel coding method, which allocates higher-quality compression and stronger error protection to image regions of great semantic importance. In addition, we define a new metric, termed coding efficiency, to evaluate the effectiveness of the source and channel coding in the classification task. Simulations show that our proposed G-JSSCC framework improves classification probability by 2.70 times, reduces transmission cost by 38%, and enhances coding efficiency by 5.91 times, compared to the benchmark scheme using uniform compression and an idealized channel code to uniformly protect the whole image.

Guaranteed Image Classification via Goal-oriented Joint Semantic Source and Channel Coding

TL;DR

A goal-oriented joint semantic source and channel coding (G-JSSCC) framework that applies various levels of source coding compression and channel coding protection across image regions based on their semantic importance and defines a new metric, termed coding efficiency, to evaluate the effectiveness of the source and channel coding in the classification task.

Abstract

To enable critical applications such as remote diagnostics, image classification must be guaranteed under bandwidth constraints and unreliable wireless channels through joint source and channel coding (JSCC) design. However, most existing JSCC methods focus on minimizing image distortion, implicitly assuming that all image regions contribute equally to classification performance, thereby overlooking their varying importance for the task. In this paper, we propose a goal-oriented joint semantic source and channel coding (G-JSSCC) framework that applies \emph{various} levels of source coding compression and channel coding protection across image regions based on their semantic importance. Specifically, we design a semantic information extraction method that identifies and ranks various image regions based on their contributions to classification, where the contribution is measured by the shapely value from explainable artificial intelligence (AI). Based on that, we design a semantic source coding and a semantic channel coding method, which allocates higher-quality compression and stronger error protection to image regions of great semantic importance. In addition, we define a new metric, termed coding efficiency, to evaluate the effectiveness of the source and channel coding in the classification task. Simulations show that our proposed G-JSSCC framework improves classification probability by 2.70 times, reduces transmission cost by 38%, and enhances coding efficiency by 5.91 times, compared to the benchmark scheme using uniform compression and an idealized channel code to uniformly protect the whole image.
Paper Structure (22 sections, 11 equations, 14 figures, 2 algorithms)

This paper contains 22 sections, 11 equations, 14 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model & Problem Formation
  3. System Model
  4. Traditional communication framework
  5. Overview of our proposed G-JSSCC framework
  6. Wireless Channel Model
  7. Performance Metrics
  8. Classification Probability of Specific Class
  9. Coding Efficiency
  10. Problem Formulation
  11. The Proposed G-JSSCC framework
  12. Image Pre-segmentation
  13. Semantic Information Extraction
  14. Shapley value-based segmentation
  15. Semantic information-based importance ranking
  16. ...and 7 more sections

Figures (14)

  • Figure 1: Frameworks.
  • Figure 2: Image pre-segmentation.
  • Figure 3: An example of preliminary simulations.
  • Figure 4: Semantic information extraction.
  • Figure 5: Shapley value calculation.
  • ...and 9 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2