Jarrahi, Mohammad Amin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Rate-Adaptive Joint Source Channel Coding Using Deep Block-based Compressed Sensing. In: IEEE 26th International Workshop on Multimedia Signal Processing, 2024-10-02 - 2024-10-04, Purdue University, West Lafayette, IN, USA. (In Press)
Jarrahi, Mohammad Amin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Rate-Adaptive Joint Source Channel Coding Using Deep Block-based Compressed Sensing. In: IEEE 26th International Workshop on Multimedia Signal Processing, 2024-10-02 - 2024-10-04, Purdue University, West Lafayette, IN, USA. (In Press)
Jarrahi, Mohammad Amin and Bourtsoulatze, Eirina and Abolghasemi, Vahid (2024) Rate-Adaptive Joint Source Channel Coding Using Deep Block-based Compressed Sensing. In: IEEE 26th International Workshop on Multimedia Signal Processing, 2024-10-02 - 2024-10-04, Purdue University, West Lafayette, IN, USA. (In Press)
Abstract
This paper introduces a novel Rate-Adaptive Com- pressed Sensing-based Joint Source-Channel Coding scheme, termed RACS-JSCC, which leverages deep block-based CS to dynamically adjust the encoding rate based on available channel bandwidth and input image statistics. RACS-JSCC selects the encoding rate using both local and global statistics of the input image, alongside channel state information, prior to the feature extraction stage. This approach eliminates the trans- mission of redundant features and ensures the input image is encoded at an optimal rate. By training a deep learning-based JSCC encoder-decoder pair to operate across multiple rates and channel conditions, the proposed method reduces the necessity for multiple models and enhances practical applicability in diverse communication environments. Our experimental results demonstrate that RACS-JSCC achieves superior performance in terms of image quality and robustness against varying channel conditions, making it a highly efficient solution for real-world wireless image transmission.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: _not provided_ |
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 Oct 2024 14:35 |
Last Modified: | 17 Dec 2024 01:17 |
URI: | http://repository.essex.ac.uk/id/eprint/38906 |
Available files
Filename: Accepted.pdf