# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 4 Simulation and Analysis
### Comparative Results and Statistical Analysis  
The performance of the proposed method was also evaluated for various JPEG and PNG image formats to obtain compression ratios with percentages of compression. The image sizes considered for the simulation were 128 \(\times\) 128, 256 \(\times\) 256, 512 \(\times\) 512, and 1024 \(\times\) 1024. The performance measures of the proposed model are shown in Table 2. The expected performance in terms of compression ratios to other models is detailed in Table 3. The expected performance in terms of compression percentages to other models is shown in Table 4. The expected performance in terms of computing times for the proposed model to other models is shown in Table 5. The expected compression and decompression computing times for the benchmark datasets for the proposed model in relation to those of the other models are shown in Tables 6 and 7, respectively. \begin{table}
\begin{tabular}{l c c c} \hline
**Size** & **Features** & **JPEG** & **PNG** \\ \hline
128 \(\times\) 128 Image 1 & Compression ratio & 8.9\% & 8.7\% \\
128 \(\times\) 128 Image 1 & Compression \% & 52.5\% & 46.6\% \\
128 \(\times\) 128 Image 2 & Compression ratio & 8.5\% & 8.2\% \\
128 \(\times\) 128 Image 2 & Compression \% & 53.5\% & 41.5\% \\
256 \(\times\) 256 Image 1 & Compression ratio & 8.1\% & 8.0\% \\
256 \(\times\) 256 Image 1 & Compression \% & 42.5\% & 41.2\% \\
256 \(\times\) 256 Image 2 & Compression ratio & 7.5\% & 7.2\% \\
256 \(\times\) 256 Image 2 & Compression \% & 36.2\% & 33.5\% \\ \hline \end{tabular}
\end{table} 
Table 2: JPEG and PNG format comparisons. \begin{table}
\begin{tabular}{l c c} \hline
**Size** & **Proposed Model** & **Other Models** \\ \hline
128 \(\times\) 128 & 8.6\% & 8.1–8.5\% \\
256 \(\times\) 256 & 8.4\% & 8.1–8.3\% \\
512 \(\times\) 512 & 7.9\% & 7.5–7.8\% \\
1024 \(\times\) 1024 & 7.8\% & 7.7–7.8\% \\ Public datasets & 8.5\% & 7.8–8.4\% \\ \hline \end{tabular}
\end{table} 
Table 3: Expected performance—compression ratios compared with those of other models. The following significant inferences were drawn based on the simulation. * JPEG formats' compression ratio percentages were slightly higher than PNG formats. When the image size increases, that is, for high-resolution images, the compression ratio lies between 7% and 7.5%, and the compression percentage lies between 30% and 37%. * The proposed model increases the expected compression ratio and percentage compared to other models. The average compression ratio lies between 7.8% and 8.6%, and the expected compression ratio lies between 35% and 60%. * Computing time is reduced with the proposed method relative to other methods. For high-resolution images, the expected computing time lies between 4 ms and 5 ms compared to other approaches. The limitations of the proposed model are as follows:
* The memory requirement through DWT is higher since it processes the complete image. * Training and inference can be achieved by designing an optimal neural network to reduce complexity. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Size** & **Proposed Model** & **Other Models** \\ \hline \(128\times 128\) & 2.7 & 3.8 \\ \(256\times 256\) & 3.2 & 5.9 \\ \(512\times 512\) & 4.1 & 5.4 \\ \(1024\times 1024\) & 4.2 & 5.5 \\ Public datasets & 12.56 & 21.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expected performance—compression computing times (in ms) compared with those of other models. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Size** & **Proposed Model** & **Other Models** \\ \hline \(128\times 128\) & 58.6\% & 58.1–58.3\% \\ \(256\times 256\) & 48.4\% & 48.1–48.3\% \\ \(512\times 512\) & 57.2\% & 47.5–47.9\% \\ \(1024\times 1024\) & 37.8\% & 35.7–36.4\% \\ Public datasets & 58.4\% & 45.5–55.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Expected performance—compression percentages compared with those of other models.