基于全局相关语义重要性的语义压缩算法

doi:10.3785/j.issn.1008-973X.2025.04.015

浙江大学学报(工学版)

2025, Vol. 59

Issue (4): 795-803 DOI: 10.3785/j.issn.1008-973X.2025.04.015

计算机技术与控制工程

基于全局相关语义重要性的语义压缩算法

李勇1,2(

),刘志强1,田茂幸2,*(

),贾松霖3

1. 西北工业大学网络空间安全学院，陕西西安 710072
2. 兴唐通信科技有限公司，北京 100191
3. 航天东方红卫星有限公司，北京 100094

Semantic compression algorithm based on global correlated semantic importance

Yong LI1,2(

),Zhiqiang LIU1,Maoxing TIAN2,*(

),Songlin JIA3

1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
2. Xingtang Communication Technology Limited Company, Beijing 100191, China
3. Aerospace DFH Satellite Limited Company, Beijing 100094, China

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摘要：

为了改善传统压缩方法在保留深层语义信息方面的不足，提出新型语义压缩算法. 将全局相关语义重要性（GCSI）作为语义重要性度量参数，综合考虑语义任务相关性和语义内在相关性指标，全面评估语义特征的重要性，实现有效的语义压缩. 实验结果表明，在不同信道条件下，相比传统方法，所提算法的压缩性能提升超过30%；在低带宽和低信噪比环境中，所提算法的分类准确度提升超过10%. 在相同带宽和性能要求下，相较于现有基于语义任务相关性的语义压缩方法，所提算法的噪声稳定性更好，显著降低了网络传输压力，提升了任务处理性能，有能力面对未来逐步增加的数据传输需求挑战.

关键词： 语义通信; 图片分类; 语义重要性; 语义压缩; 语义相似度

Abstract:

A novel semantic compression algorithm was proposed, aiming to address the inadequacies of traditional compression methods in retaining deep semantic information. The global correlated semantic importance (GCSI) was used as a semantic importance measurement parameter, and the semantic task relevance and semantic intrinsic relevance metrics were integrated to assess the importance of semantic features and achieve effective semantic compression. Experimental results show that the compression performance of the proposed algorithm is improved by more than 30% compared with traditional methods under different channel conditions, and the classification accuracy of the proposed algorithm is enhanced by more than 10% in low bandwidth and low signal-to-noise ratio (SNR) environments. Under the same bandwidth and performance requirements, the proposed algorithm exhibits superior noise stability compared to existing semantic compression methods based on semantic task relevance. The proposed algorithm significantly alleviates network transmission pressure, enhances task performance, and can meet the increasing data transmission requirements.

Key words: semantic communication image classification semantic importance semantic compression semantic similarity

收稿日期: 2024-02-27 出版日期: 2025-04-25

CLC:

TP 301.6

通讯作者: 田茂幸 E-mail: liy61152024@163.com;tmx7142024@163.com

作者简介: 李勇（1975—），男，硕士生，从事网络信息安全研究. orcid.org/0009-0002-6993-6475. E-mail：liy61152024@163.com

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引用本文:

李勇,刘志强,田茂幸,贾松霖. 基于全局相关语义重要性的语义压缩算法[J]. 浙江大学学报(工学版), 2025, 59(4): 795-803.

Yong LI,Zhiqiang LIU,Maoxing TIAN,Songlin JIA. Semantic compression algorithm based on global correlated semantic importance. Journal of ZheJiang University (Engineering Science), 2025, 59(4): 795-803.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.04.015 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I4/795

图 1 语义重要性度量网络

图 2 基于全局相关语义重要性的语义压缩算法网络结构

图 3 加性白高斯噪声信道模型

表 1 不同压缩算法在STL-10数据集中的性能测试结果

图 4 不同压缩算法在不同信噪比下的分类准确度(STL-10数据集)

图 5 不同压缩算法在不同信道带宽下的分类准确度

表 2 不同压缩算法在CIFAR-10数据集中的性能测试结果

图 6 不同压缩算法在不同信噪比下的分类准确度(CIFAR-10数据集)

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