Feature-Driven Joint Source–Channel Coding for Robust 3D Image Transmission

Emerging applications like augmented reality (AR) demand efficient wireless transmission of high-resolution three-dimensional (3D) images, yet conventional systems struggle with the high data volume and vulnerability to noise. This paper proposes a novel feature-driven framework that integrates sema...

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Vydáno v:	Electronics (Basel) Ročník 14; číslo 19; s. 3907
Hlavní autoři:	Liu, Yinuo, Xu, Hao, Bowman, Adrian, Chen, Weichao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.10.2025
Témata:	Algorithms Augmented Reality Bandwidths Coding Deep learning Error-correcting codes Image reconstruction Image resolution Image transmission Information management Methods Robustness Semantics Signal to noise ratio Three dimensional imaging Virtual reality Luxembourg
ISSN:	2079-9292, 2079-9292
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Abstract	Emerging applications like augmented reality (AR) demand efficient wireless transmission of high-resolution three-dimensional (3D) images, yet conventional systems struggle with the high data volume and vulnerability to noise. This paper proposes a novel feature-driven framework that integrates semantic source coding with deep learning-based Joint Source–Channel Coding (JSCC) for robust and efficient transmission. Instead of processing dense meshes, the method first extracts a compact set of geometric features—specifically, the ridge and valley curves that define the object’s fundamental structure. This feature representation which is extracted by the anatomical curves is then processed by an end-to-end trained JSCC encoder, mapping the semantic information directly to channel symbols. This synergistic approach drastically reduces bandwidth requirements while leveraging the inherent resilience of JSCC for graceful degradation in noisy channels. The framework demonstrates superior reconstruction fidelity and robustness compared to traditional schemes, especially in low signal-to-noise ratio (SNR) regimes, enabling practical and efficient 3D semantic communications.
AbstractList	Emerging applications like augmented reality (AR) demand efficient wireless transmission of high-resolution three-dimensional (3D) images, yet conventional systems struggle with the high data volume and vulnerability to noise. This paper proposes a novel feature-driven framework that integrates semantic source coding with deep learning-based Joint Source–Channel Coding (JSCC) for robust and efficient transmission. Instead of processing dense meshes, the method first extracts a compact set of geometric features—specifically, the ridge and valley curves that define the object’s fundamental structure. This feature representation which is extracted by the anatomical curves is then processed by an end-to-end trained JSCC encoder, mapping the semantic information directly to channel symbols. This synergistic approach drastically reduces bandwidth requirements while leveraging the inherent resilience of JSCC for graceful degradation in noisy channels. The framework demonstrates superior reconstruction fidelity and robustness compared to traditional schemes, especially in low signal-to-noise ratio (SNR) regimes, enabling practical and efficient 3D semantic communications.
Audience	Academic
Author	Liu, Yinuo Bowman, Adrian Chen, Weichao Xu, Hao
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Cites_doi	10.1109/JSAC.2022.3191354 10.1007/978-3-642-15014-2 10.1016/B978-012119792-6/50124-8 10.1109/JSAIT.2020.2987203 10.1109/ICMEW59549.2023.00075 10.1109/COMST.2015.2444095 10.1002/j.1538-7305.1948.tb01338.x 10.1109/TIT.2013.2238657 10.1109/LWC.2022.3204837 10.1109/TCCN.2019.2919300 10.1109/18.370119 10.1109/TVCG.2003.1175093 10.1109/JSAC.2022.3223408 10.1109/TWC.2021.3090048 10.1109/MWC.2019.1800601 10.1016/0262-8856(92)90076-F 10.1109/ACCESS.2022.3187837 10.1111/joa.12407 10.1145/1015706.1015768 10.1109/ICRA.2011.5980382 10.1145/1409060.1409098 10.1109/LWC.2022.3232946 10.1145/383259.383266 10.1111/j.1467-8659.2007.01103.x 10.1214/19-AOAS1267 10.1109/TCCN.2022.3151935
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SubjectTerms	Algorithms Augmented Reality Bandwidths Coding Deep learning Error-correcting codes Image reconstruction Image resolution Image transmission Information management Methods Robustness Semantics Signal to noise ratio Three dimensional imaging Virtual reality
Title	Feature-Driven Joint Source–Channel Coding for Robust 3D Image Transmission
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