基于深度压缩感知的联合信源信道编码方法研究

董浩; 李劭辉; 阚诺文; 郑紫阳; 戴文睿; 熊红凯

doi:10.12263/DZXB.20250002

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基于深度压缩感知的联合信源信道编码方法研究

编码理论与技术的进展与未来 | 更新时间：2025-12-10

- 基于深度压缩感知的联合信源信道编码方法研究
- Research on Joint Source-Channel Coding Method Based on Deep Compressive Sensing
- 电子学报 2025年53卷第7期页码：2178-2192
- 作者机构：
  
  1.上海交通大学电子信息与电气工程学院，上海 200240
  2.清华大学深圳国际研究生院，广东深圳 518055
- 作者简介：
  
  [ "董浩男，2002年2月出生于辽宁省葫芦岛市.2024年获得北京航空航天大学电子信息工程专业学士学位.现为上海交通大学集成电路学院硕士研究生.主要研究方向为图像与视频编码.E-mail: 124034910027@sjtu.edu.cn" ]
  [ "李劭辉男，1994年5月出生于山东省兰陵县.2016年获得大连理工大学电子信息工程专业学士学位，并于2019年和2022年分别获得上海交通大学信息与通信工程专业硕士和博士学位.现为清华大学深圳国际研究院助理研究员.主要研究方向为图像视频编码、信号处理.E-mail: lishaohui@sz.tsinghua.edu.cn" ]
  [ "阚诺文男，1996年12月出生于湖南省邵阳市.2024年获得上海交通大学计算机科学与技术博士学位.现为上海交通大学信息与通信工程流动站博士后.主要研究方向为多媒体通信、深度强化学习和基于深度学习的网络优化.中国电子学会会员编号：E190187389M.E-mail: kannw_1230@sjtu.edu.cn" ]
  [ "郑紫阳男，1996年1月出生于安徽省蒙城县.2016年获得西安交通大学应用物理学士学位，2024 年获得上海交通大学信息与通信工程专业博士学位.现为上海交通大学集成电路学院助理教授.主要研究方向为多媒体信号处理、物理神经网络等.中国电子学会会员编号：E190186001M.E-mail: zhengziyang@sjtu.edu.cn" ]
  [ "戴文睿男，1983年5月出生于上海市.上海交通大学教授、博士生导师.主要研究方向为多媒体信号处理与编码、机器学习.中国电子学会会员编号：E190014727M.E-mail: daiwenrui@sjtu.edu.cn" ]
  [ "熊红凯男，1973年7月出生于江西省南昌市.上海交通大学特聘教授、博士生导师.主要研究方向为信号表示与小波分析、图像视频编码、多媒体网络通信、计算机视觉、机器学习.中国电子学会会员编号：E190013028F.E-mail: xionghongkai@sjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62320106003)
- DOI：10.12263/DZXB.20250002
  中图分类号： TN919.8;
- 收稿：2025-01-01，
  
  修回：2025-06-24，
  
  纸质出版：2025-07-25
- 稿件说明：
移动端阅览
董浩, 李劭辉, 阚诺文, 等. 基于深度压缩感知的联合信源信道编码方法研究[J]. 电子学报, 2025, 53(07): 2178-2192.

DONG Hao, LI Shao-hui, KAN Nuo-wen, et al. Research on Joint Source-Channel Coding Method Based on Deep Compressive Sensing[J]. Acta Electronica Sinica, 2025, 53(07): 2178-2192.
董浩, 李劭辉, 阚诺文, 等. 基于深度压缩感知的联合信源信道编码方法研究[J]. 电子学报, 2025, 53(07): 2178-2192. DOI：10.12263/DZXB.20250002

DONG Hao, LI Shao-hui, KAN Nuo-wen, et al. Research on Joint Source-Channel Coding Method Based on Deep Compressive Sensing[J]. Acta Electronica Sinica, 2025, 53(07): 2178-2192. DOI：10.12263/DZXB.20250002

摘要

无线图像传输面临着带宽和计算资源的双重挑战，在节点计算能力有限的物联网等应用场景中尤为突出.联合信源信道编码（Joint Source-Channel Coding，JSCC）能够同时优化信源和信道编码，逐渐成为无线图像传输中一个重要研究方向.近年来，基于深度学习的JSCC方法受到广泛关注，其通过端到端训练实现编码器与解码器的联合优化.然而，大多数基于深度学习的JSCC方法的编码器涉及大量的线性与非线性运算，导致计算复杂度较高，难以应用于物联网边缘计算节点等计算资源受限的设备.为实现轻量化的编码过程，本文提出了一种基于深度压缩感知的联合信源信道编码方法BCS-JSCC（Block Compressive Sensing-Joint Source Channel Coding），实现对于编解码器的端到端优化.该方法在编码端设计可学习尺度二值化测量的压缩感知采样，实现噪声环境下匹配解码器的轻量化编码方法；在解码端，基于MMSE（Minimum Mean Squared Error）准则求解测量值传输的线性逆问题，获得信道噪声敏感的初始重建，抑制噪声对参数复用重建网络的影响.与现有的基于深度学习的JSCC方法相比，在保持编码端每像素浮点计算次数（FLOating Point operations per pixel，FLOPs per pixel）相同的条件下，本文所提出的BCS-JSCC方法在高信噪比条件下可以取得更好的传输性能.在低算力（0.10 K FLOPs/pixel）情况下，优势更为明显.本文提出的BCS-JSCC方法编码器构造简单、计算量低，适用于物联网边缘计算节点等低算力设备部署.

Abstract

Wireless image transmission faces the dual challenges of bandwidth and computing resources

which is particularly prominent in application scenarios such as the Internet of Things where node computing power is limited. Joint source-channel coding (JSCC) can optimize both source and channel coding

and has gradually become an important research direction in wireless image transmission. In recent years

deep learning-based JSCC methods have received widespread attention

which achieve joint optimization of encoders and decoders through end-to-end training. However

most encoders of deep learning-based JSCC methods involve a large number of linear and nonlinear operations

resulting in high computational complexity and difficulty in application to devices with limited computing resources such as edge computing nodes in the Internet of Things. In order to achieve a lightweight coding process

this paper proposes a joint source channel coding method BCS-JSCC (Block Compressive Sensing-Joint Source Channel Coding) based on deep compressed sensing to achieve end-to-end optimization of the codec. This method designs a compressed sensing sampling of learnable scale binary measurement at the encoding end to realize a lightweight encoding method that matches the decoder in a noisy environment; at the decoding end

the linear inverse problem of measurement value transmission is solved based on the MMSE (Minimum Mean Squared Error) criterion to obtain the initial reconstruction sensitive to channel noise and suppress the influence of noise on the parameter reuse reconstruction network. Compared with the existing JSCC method based on deep learning

the BCS-JSCC method proposed in this paper can achieve better transmission performance under high signal-to-noise ratio conditions while keeping the number of floating point operations per pixel (FLOPs per pixel) at the encoding end the same. The advantage is more obvious under low computing power (0.10 K FLOPs/pixel). The encoder of the BCS-JSCC method proposed in this paper has a simple structure and low computational complexity

and is suitable for deployment on low computing power devices such as edge computing nodes of the Internet of Things.

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