# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 3 Data Compression and Decompression
### Compression and Decompression Model
### Discussion

In summary, the compression algorithm proposed in this paper outperforms traditional compression algorithms (Huffman, LZW, PPM, Bzip2, and Mftpc), traditional deep learning compression algorithms (BiGRU, GRU_Multi, BiLSTM, LSTM_Multi, Transformer, and MHSA-MLP), as well as popular compressors (ZIP, ZZ, TGZ, and GZ) in terms of both compression ratio and compression time. Table 4 presents the average compression ratio and average compression time. To evaluate compression performance, this study uses compression ratio and compression time as the primary statistical indicators. Compression time represents the computational time required for the data compression process, while compression ratio measures the proportion of the compressed data size to the original data size. A smaller compression ratio indicates better compression performance. The calculation of the compression ratio is expressed by Equation (9). In this context, compressed data size represents the size of the compressed data, and original data size represents the size of the original data. Figure 10:2

Among them, the average compression time for traditional compression algorithms and popular compressors is less than 1 s, with average compression ratios of 46.25% and 34.71%, respectively. The compression ratios are 30.14% and 22.6% higher than the compression algorithm proposed in this paper, respectively. Although the average compression time is relatively short, the average compression ratio is relatively high, resulting in unsatisfactory compression performance overall. The average compression time of traditional deep learning compression algorithms is approximately 9 s longer than that of traditional compression algorithms and popular compressors. However, the average compression ratio is reduced by 23.69% and 12.15%, respectively. The comparison between the proposed compression algorithm and traditional deep learning compression algorithms on the PC is shown in Figure 10:3 Compared to traditional deep learning compression algorithms, the proposed compression algorithm not only achieves a lower compression ratio but also shorter compression time. Additionally, in comparison with the three baseline methods, the \(p\)-value between compression ratios was introduced as a statistical significance test metric. In this study, the \(p\)-value is used to determine whether the proposed compression method exhibits a significant difference in compression performance compared to the baseline methods. The results show that the \(p\)-values for the proposed compression algorithm compared to the three baseline methods are all less than 0.05, indicating that the statistical tests reveal a significant difference in compression performance. Further analysis demonstrates that the proposed compression algorithm significantly outperforms these three baseline methods in terms of compression effectiveness. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compression Algorithm** & **Average Compression Ratio (\%)** & **Average Compression Time (s)** \\ \hline Traditional compression algorithms & 46.25 & \textless{}1 \\ Traditional deep learning compression algorithms & 22.56 & 9.9 \\ Popular compressors & 34.71 & \textless{}1 \\ This article (PC) & 12.11 & 4.4 \\ This article (Jetson nano) & 12.11 & 36.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the effects of different compression algorithms. Specifically, the compression algorithm achieves an average compression ratio of 12.11% on both the PC and Jetson Nano, with an average compression time of 4.4 s on the PC and 36.6 s on the Jetson Nano. Compared to the first three methods, the proposed algorithm reduces the average compression ratio by 34.14%, 10.45%, and 22.6%, respectively, achieving a significant decrease in compression ratio. The average compression time on the PC is reduced by 5.5 s compared to traditional deep learning compression algorithms, while the average compression time on the Jetson Nano is increased by 32.2 s compared to the PC.