# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks  
[PERSON]  
1  
[PERSON]  
2 Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
1  
[PERSON]  
1 Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 41170, Taiwan; [EMAIL_ADDRESS]  
Footnote 1: email: {1,3,**,**,**}  
Footnote 2: email: {1,3,  
a novel approach to error correction employing a modified trellis, general variable length codes (VLCs), and a parity-check matrix. This approach constructs a JSCC image coder that is capable of generating output bits according to their relative importance [10]. Such a modified trellis model offers several advantages over traditional image encoding models. First, the quality of the reconstructed image improves as the number of decoded bits increases, a desirable property in many applications, including progressive transmission and image browsing. Second, image encoding can be terminated as soon as a target bit rate is met, and the resulting coded bit stream is optimized for that bit rate. Third, images can be encoded once at a high bit rate and decoded at any desired lower bit rate by truncating the bit stream. Fourth, this method employs an optimized encoding algorithm that reduces errors and is highly efficient.  
Furthermore, our approach to information encoding is considerably more robust and less error-prone than conventional image encoding techniques. Specifically, we combine the VLCs of a traditional source encoder with a modified trellis of block Hamming codes in a parity-check matrix. We correct errors by applying an optimized form of the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The algorithm uses expurgation techniques to achieve a considerable minimum Hamming distance by precisely selecting the subsets of Hamming codes.  
Our proposed model applies serial concatenation forward error correction to block codes separated in a trellis with JPEG encoding [11; 12; 13; 14]. The model employs two simultaneous trellis-based source encodings--VLC and linear block code--for two separate constituent codes to preserve the quality of scalable image sequences in the JSCC system. Such a system permits incorporating multilevel codes with various asymptotic and practical properties.  
Moreover, for different values of SNR, ref. [15] proposed a model that adapts to changes in SNR for image transmission. By the channel model under different SNRs, good performance is achieved. The auto-encoder (AE)-based JSCC model has also been applied in the security field [16], and a higher-order modulation method was implemented using fully convolutional neural networks (FCNN) in [17]. Driven by deep learning technology, semantic communications are compatible with various multimedia sources, offering the advantages of reducing communication bandwidth and improving communication robustness [18]. Ref. [19] has also been introduced to CSI compression, serving as the source coding module in the separate source channel coding scheme.  
The remainder of this paper is organized as follows: Section 2 outlines the construction of the model; Section 3 details an experimental application of the model; Section 4 provides a discussion of the results; and Section 5 presents the conclusion.