Nowadays, the demand for image transmission over wireless networks has surged significantly. To meet the need for swift delivery of high-quality images through time-varying channels with limited bandwidth, the development of efficient transmission strategies and techniques for preserving image quality is of importance. This paper introduces an innovative approach to Joint Source-Channel Coding (JSCC) tailored for wireless image transmission. It capitalizes on the power of Compressed Sensing (CS) to achieve superior compression and resilience to channel noise. In this method, the process begins with the compression of images using a block-based CS technique implemented through a Convolutional Neural Network (CNN) structure. Subsequently, the images are encoded by directly mapping image blocks to complex-valued channel input symbols. Upon reception, the data is decoded to recover the channel-encoded information, effectively removing the noise introduced during transmission. To finalize the process, a novel CNN-based reconstruction network is employed to restore the original image from the channel-decoded data. The performance of the proposed method is assessed using the CIFAR-10 and Kodak datasets. The results illustrate a substantial improvement over existing JSCC frameworks when assessed in terms of metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across various channel Signal-to-Noise Ratios (SNRs) and channel bandwidth values. These findings underscore the potential of harnessing CNN-based CS for the development of deep JSCC algorithms tailored for wireless image transmission.

翻译：近年来，无线图像传输需求显著增长。为满足通过时变有限带宽信道快速传输高质量图像的需求，开发兼顾传输效率与图像质量保持的策略与技术至关重要。本文提出一种专为无线图像传输设计的联合源信道编码（JSCC）创新方法，该方法利用压缩感知（CS）实现优异压缩性能与信道噪声鲁棒性。具体而言，该方法首先通过基于卷积神经网络（CNN）结构的分块压缩感知技术对图像进行压缩，随后将图像块直接映射为复数域信道输入符号实现编码。接收端对数据进行解码以恢复信道编码信息，有效消除传输过程中引入的噪声。最终阶段，一种新型CNN重建网络被用于从信道解码数据中恢复原始图像。基于CIFAR-10与Kodak数据集的评估结果表明，在不同信道信噪比（SNR）与信道带宽条件下，所提方法在峰值信噪比（PSNR）与结构相似性指数（SSIM）等指标上均显著优于现有JSCC框架。这些发现揭示了基于CNN的压缩感知技术用于开发面向无线图像传输的深度JSCC算法的巨大潜力。