Jarrahi, Mohammadamin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Joint Source-Channel Coding for Wireless Image Transmission: A Deep Compressed-Sensing Based Method. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024-04-21 - 2024-04-24, Dubai, UAE.
Jarrahi, Mohammadamin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Joint Source-Channel Coding for Wireless Image Transmission: A Deep Compressed-Sensing Based Method. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024-04-21 - 2024-04-24, Dubai, UAE.
Jarrahi, Mohammadamin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Joint Source-Channel Coding for Wireless Image Transmission: A Deep Compressed-Sensing Based Method. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024-04-21 - 2024-04-24, Dubai, UAE.
Abstract
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.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: _not provided_ |
Uncontrolled Keywords: | Wireless image transmission; joint source-channel coding; compressed sensing; deep learning |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 02 Aug 2024 10:32 |
Last Modified: | 02 Aug 2024 10:32 |
URI: | http://repository.essex.ac.uk/id/eprint/37740 |
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