TY - GEN
T1 - Low Complexity Rate-Adaptive Deep Joint Source Channel Coding for Wireless Image Transmission Using Tensor-Train Decomposition
AU - Xu, Man
AU - Liang, Yuanhui
AU - Lam, Chan Tong
AU - Ng, Benjamin
AU - Im, Sio Kei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We propose a low complexity deep joint source channel coding (DJSCC) with adaptive rate control model for wireless image transmission using Tensor-Train (TT) decomposition. TT is used to decompose the weights of the convolutional layers in the model in order to reduce the number of parameters and the amount of computation. According to the characteristics of the convolution layer and that of TT decomposition, the optimal decomposition ranks and the corresponding weight distribution of the network for the DJSCC with adaptive rate control model are obtained. We evaluated the performance of the proposal low complexity DJSCC with adaptive rate control model using CIFAR-10 and found that compared with the model without TT decomposition, the proposed Rate-Adaptive DJSCC model using TT decomposition can reduce the number of parameters to 52.91%, and multiplication calculation to 63.61%, with a PSNR performance degradation of about 1 dB.
AB - We propose a low complexity deep joint source channel coding (DJSCC) with adaptive rate control model for wireless image transmission using Tensor-Train (TT) decomposition. TT is used to decompose the weights of the convolutional layers in the model in order to reduce the number of parameters and the amount of computation. According to the characteristics of the convolution layer and that of TT decomposition, the optimal decomposition ranks and the corresponding weight distribution of the network for the DJSCC with adaptive rate control model are obtained. We evaluated the performance of the proposal low complexity DJSCC with adaptive rate control model using CIFAR-10 and found that compared with the model without TT decomposition, the proposed Rate-Adaptive DJSCC model using TT decomposition can reduce the number of parameters to 52.91%, and multiplication calculation to 63.61%, with a PSNR performance degradation of about 1 dB.
KW - adaptive rate control image transmission
KW - deep joint source channel coding
KW - tensor train decomposition
UR - http://www.scopus.com/inward/record.url?scp=85139426896&partnerID=8YFLogxK
U2 - 10.1109/ICSIP55141.2022.9886385
DO - 10.1109/ICSIP55141.2022.9886385
M3 - Conference contribution
AN - SCOPUS:85139426896
T3 - 2022 7th International Conference on Signal and Image Processing, ICSIP 2022
SP - 707
EP - 712
BT - 2022 7th International Conference on Signal and Image Processing, ICSIP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Signal and Image Processing, ICSIP 2022
Y2 - 20 July 2022 through 22 July 2022
ER -