Modified Joint Source–Channel Trellises for Transmitting JPEG Images over Wireless Networks
Source abstract
Abstract
This paper presents a joint source–channel image transmission model based on a modified trellis construction using variable-length and fixed-length codes. The model employs linear block trellis codes and a modified Bahl–Cocke–Jelinek–Raviv algorithm for decoding in the source channel. Additionally, this model utilizes a hierarchical tree and a parity-check matrix to combine the source and channel codes, reducing errors in the decoding process and enabling the decoding of longer sequences without substantial loss of quality.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Data Compression Techniques · Advanced Wireless Communication Techniques · Video Coding and Compression Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Funding
National Science and Technology Council, Taiwan
References (21)
Show source-provided references
1. Error detecting and error correcting codes10.1002/j.1538-7305.1950.tb00463.x ↗
2. A mathematical theory of communication10.1002/j.1538-7305.1948.tb01338.x ↗
3. Turbo codes for image transmission—A Joint channel and source decoding approach10.1109/49.848241 ↗
4. Joint source-channel turbo coding for binary Markov sources10.1109/TWC.2006.1633359 ↗
5. Joint source-channel turbo techniques for discrete-valued sources: From theory to practice10.1109/JPROC.2007.896491 ↗
6. Bovik, A. (2005). Handbook of Image and Video Processing, Academic Press. [2nd ed.].10.1201/9781420027822.ch14 ↗
7. Balle, J., Laparra, V., and Simoncelli, E.P. (2017, January 24–26). End-to-end optimized image compression. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France.
8. Bit-Interleaved Coded Modulation10.1561/0100000019 ↗
9. On the Computation of the Discrete Cosine Transform10.1109/TCOM.1978.1094144 ↗
10. Chui, C.K. (1992). An Introduction to Wavelets, Academic Press.10.1063/1.4823126 ↗
11. Theoretical limitations on the transmission of data from analog sources10.1109/TIT.1965.1053821 ↗
12. Sparsecast: Hybrid digital-analog wireless image transmission exploiting frequency-domain sparsity10.1109/LCOMM.2018.2877316 ↗
13. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., and Weyand, T. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
14. Chiou, P.T., Sun, Y., and Young, G.S. (2017, January 27–29). A complexity analysis of the JPEG image compression algorithm. Proceedings of the 9th Computer Science and Electronic Engineering (CEEC), Colchester, UK.10.1109/CEEC.2017.8101601 ↗
15. Ding, M., Li, J., Ma, M., and Fan, X. (2021, January 6–11). SNR-adaptive deep joint source-channel coding for wireless image transmission. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.10.1109/ICASSP39728.2021.9414037 ↗
16. Jankowski, M., Gündüz, D., and Mikolajczyk, K. (2020, January 4–8). Deep joint source-channel coding for wireless image retrieval. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain.10.1109/ICASSP40776.2020.9054078 ↗
17. Huang, Q., Jiang, M., and Zhao, C. (2019, January 20–23). Learning to design constellation for AWGN channel using auto-encoders. Proceedings of the IEEE International Workshop on Signal Processing Systems, Nanjing, China.10.1109/SiPS47522.2019.9020501 ↗
18. Semantic communications: Overview open issues and future research directions10.1109/MWC.101.2100269 ↗
19. High-accuracy CSI feedback with super-resolution network for massive MIMO systems10.1109/LWC.2021.3122462 ↗
20. Lin, S., Kasami, T., Fujiwara, T., and Fossorier, M. (1998). Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes, Springer.10.1007/978-1-4615-5745-6 ↗
21. Soleymani, M.R., Gao, Y., and Vilaipornsawai, U. (2002). Turbo Coding for Satellite and Wireless Communications, Kluwer Academic.