TY - JOUR
T1 - Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission
AU - Sun, Lunan
AU - Yang, Yang
AU - Chen, Mingzhe
AU - Guo, Caili
AU - Saad, Walid
AU - Poor, H. Vincent
N1 - Funding Information:
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2021XD-A01-1, in part by the Key Program of the National Natural Science Foundation of China under Grant 92067202, in part by the Beijing Natural Science Foundation under Grant L222043, and in part by the U.S. National Science Foundation under Grant CNS-2128448.
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - 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.
AB - 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.
KW - Information bottleneck
KW - image transmission
KW - joint source and channel coding
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U2 - 10.1109/JSAC.2023.3288238
DO - 10.1109/JSAC.2023.3288238
M3 - Article
AN - SCOPUS:85162889835
SN - 0733-8716
VL - 41
SP - 2628
EP - 2644
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 8
ER -