DCS-JSCC: Leveraging Deep Compressed Sensing into JSCC for Wireless Image Transmission

This paper presents a novel approach that integrates deep compressed sensing (DCS) into joint source-channel coding (JSCC) for efficient image transmission. Leveraging the capabil- ities of DCS, the proposed method offers enhanced compression and resilience to channel noise in wireless image transmission systems. A key component of the method is the utilization of a convolutional neural network (CNN) structure to implement a block-based DCS technique for image compression. The proposed encoder utilizes a well-designed CNN-based structure to capture structural information and then map it to complex-valued signals. The proposed decoder deals with channel noise and reconstructs the original image. Using the CNN-based sampling matrices and reconstruction capabilities helps the proposed algorithm enhance image compression and reconstruction in wireless transmission systems. The CIFAR-10 and Kodak datasets are used to evaluate the performance of the suggested technique, showing a significant improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), across different channel Signal-to-Noise Ratios (SNRs) and channel bandwidth values in comparison with state-of-art JSCC frameworks. Experimental evaluations demonstrate the effectiveness of the proposed method in achieving superior compression ratios and maintaining image quality under varying channel conditions.