# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
## 6 Conclusions  
This paper designs a new low-complexity lossless compression algorithm for 14-bit line-scan infrared images. This paper proposes a method for constructing a Huffman code table, replacing the pixel probability statistical step in entropy coding, thereby improving the compression speed. For more bit images, the method proposed in this paper can be used to find a new probability model to compute a new code table. Additionally, this paper designs an improved Huffman coding scheme to handle the longer codes of 14-bit images, truncating long codes with low complexity and minimal compression ratio loss, ultimately realizing a low-complexity lossless image compression algorithm. The method proposed in this paper achieves an average compression ratio of 3.3 for infrared line-scan images, which is 5% lower than the theoretical value. Compared to JPEG 2000, the proposed method incurs a 10% loss in compression efficiency but provides a 20-fold speed improvement, reaching an average of 210 MB/s. Its performance and speed both outperform JPEG XT. Compared to dictionary-based lossless compression methods, the proposed method can achieve high-speed compression while maintaining high compression efficiency.  
The method proposed in this paper can be extended to other general image domains that require high-speed compression and can tolerate some loss in compression ratio. Additionally, if the code table is concealed, this method could also facilitate encrypted image transmission, making it applicable to secure communication systems. However, there are certain limitations to the proposed method. For instance, while the method significantly speeds up compression, the 10% loss in compression efficiency compared to JPEG 2000 suggests that further optimizations could be made to balance both the speed  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**The Average Results** & **JPEG 2000** & **TIFF-Deflate** & **TIFF-Lzw** & **TIFF-Packbits** & **TIFF-LZMA** \\ \hline Cr & 3.6379 & 1.9779 & 1.3473 & 0.4961 & 3.0139 \\ The percentage change & \(\backslash\) & \(-45.6307\%\) & \(-62.9649\%\) & \(-86.3630\%\) & \(-17.1528\%\) \\ Compression speed (MB/S) & 10.9728 & 79.0262 & 337.5899 & 639.4183 & 11.6381 \\ \hline \end{tabular}
\end{table}
Table 9: Experimental results for TIFF.  
and compression ratio. Additionally, the proposed algorithm might face challenges when applied to more complex image types or images with larger bit depths, as the probability model used may need to be adapted to these cases.  
Future research can explore several directions. First, improving the Huffman coding scheme to achieve better compression efficiency without compromising speed is a potential area of investigation. Second, adapting the proposed method to work with images of higher bit depths or more complex data types may enhance its applicability in other fields, such as medical imaging or remote sensing. This could be achieved by incorporating more widely used image prediction methods to reduce redundancy, which would allow the approach to better handle more complex image types. Finally, investigating hybrid methods that combine the strengths of both dictionary-based and statistical compression techniques could lead to even more efficient algorithms for infrared image compression.  
Conceptualization, Y.Z. ([PERSON]); Supervision, Y.Z. ([PERSON]); Validation, M.H. and Y.Z. ([PERSON]); Writing--original draft, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be available reasonably on request.  
The authors sincerely thank the anonymous reviewers for their insightful comments and valuable suggestions.  
The authors declare that there are no conflicts of interest.  
The detailed experimental data for JPEG series.