  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.
