Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of av...

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Veröffentlicht in:IEEE journal on selected areas in communications Jg. 41; H. 8; S. 2628 - 2644
Hauptverfasser: Sun, Lunan, Yang, Yang, Chen, Mingzhe, Guo, Caili, Saad, Walid, Poor, H. Vincent
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Channel coding
Decoding
Distortion
Image coding
Image communication
Image quality
Image reconstruction
Image transmission
Information bottleneck
joint source and channel coding
Lower bounds
Mutual information
Noise measurement
Robustness (mathematics)
ISSN:0733-8716, 1558-0008
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Zusammenfassung:Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2023.3288238