Abstract: Currently, multi-spectral image transmission faces challenges such as high storage costs and low transmission efficiency. Although various technologies are attempted to solve these problems recently, such as improving encoding methods in some algorithms, there are still issues such as insufficient compression ratio and slow processing speed. Therefore, the research focuses on optimizing the Joint Photographic Experts Group Lossless Standard (JPEG-LS) algorithm and constructing a multi-spectral image processing system. Regarding the JPEG LS algorithm process, improvements are made to the conventional encoding method by adopting sub-block compression strategy and block compression algorithm based on dynamic image bit width. The results show that the optimized JPEG LS algorithm has an average compression ratio of 5.81, which is higher than the comparison algorithm. The average compression time is 0.35 seconds, the average peak signal-to-noise ratio (PSNR) is 43.6, and the average structural similarity (SSIM) is 0.97, all of which are better than the comparison algorithm. In terms of system performance, stability testing of each module shows that the overall system tends to be stable, and the resource utilization rate of the image compression module is low, with a large resource margin that can meet practical application needs.

Huanping Hu and Xing Wang, “The Application of Optimized JPEG-LS Algorithm in Efficient Transmission of Multi-Spectral Images” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160361

@article{Hu2025,
title = {The Application of Optimized JPEG-LS Algorithm in Efficient Transmission of Multi-Spectral Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160361},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160361},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Huanping Hu and Xing Wang}
}