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

Source: [Guaranteed Image Classification via Goal-oriented Joint Semantic Source and Channel Coding](<https://www.sota2.com/research/paper/69a66c522d603e6de3e3d064>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-04T05:00:19.274Z

Authors: Wenchao Wu, Min Qiu, Yansha Deng, Jinhong Yuan

Published: 2026-03-02T13:51:12.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.01872>)

[PDF](<https://arxiv.org/pdf/2603.01872.pdf>)