## 4 Experimental Design and Results
### Evaluation Metrics  
Various performance metrics have been established to evaluate the efficiency of compression algorithms. In this study, the effectiveness of the developed algorithm was assessed using commonly utilized metrics for evaluating lossless compression algorithms, as highlighted in the literature: Compression Ratio (CR), Bits Per Pixel (BPP), and Space Saving (SS) [54]. The definitions and corresponding equations for these metrics are detailed below.  
The Compression Ratio (CR), as defined in Equation (2), is calculated as the ratio of the original image size to the compressed image size. A higher CR value signifies greater compression efficiency, indicating that the image has been more effectively reduced in size.  
\[CR=\frac{Original\ Image_{size}}{Compressed\ Image_{size}} \tag{2}\]  
Another key metric for evaluation is the Bits Per Pixel (BPP), as defined in Equation (3). This metric is calculated by dividing the size of the compressed image by the total number of pixels in the image. Its primary purpose is to determine the average number of bits  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Image Code** & **Image Name Tag** & **Resolution** & **Size (Bytes)** \\ \hline \multirow{6}{*}{USC-SIPI [50]} & 4.1.05 & 1.1 & 256 \(\times\) 256 & 196,608 \\  & 4.2.03 & 1.2 & 512 \(\times\) 512 & 786,432 \\  & 4.2.07 & 1.3 & 512 \(\times\) 512 & 786,432 \\  & 5.1.12 & 1.4 & 256 \(\times\) 256 & 196,608 \\  & 5.3.01 & 1.5 & 1024 \(\times\) 1024 & 3,145,728 \\  & 7.2.01 & 1.6 & 1024 \(\times\) 1024 & 3,145,728 \\ \hline \multirow{6}{*}{Draper Satellite Image [51]} & set120\_1 & 2.1 & 3099 \(\times\) 2329 & 21,652,713 \\  & set162\_4 & 2.2 & 3099 \(\times\) 2329 & 21,652,713 \\ \cline{1-1}  & set192\_5 & 2.3 & 3099 \(\times\) 2329 & 21,652,713 \\ \cline{1-1}  & set213\_3 & 2.4 & 3099 \(\times\) 2329 & 21,652,713 \\ \cline{1-1}  & set237\_4 & 2.5 & 3099 \(\times\) 2329 & 21,652,713 \\ \cline{1-1}  & set260\_4 & 2.6 & 3099 \(\times\) 2329 & 21,652,713 \\ \hline \multirow{6}{*}{Chest X-Ray Images [52]} & IM-0005-0001 & 3.1 & 2031 \(\times\) 1837 & 11,192,841 \\  & IM-0007-0001 & 3.2 & 2053 \(\times\) 1818 & 11,197,062 \\ \cline{1-1}  & IM-0013-0001 & 3.3 & 2444 \(\times\) 2155 & 15,800,460 \\ \cline{1-1}  & IM-0029-0001 & 3.4 & 2343 \(\times\) 2139 & 15,035,031 \\ \cline{1-1}  & IM-0035-0001 & 3.5 & 2480 \(\times\) 2329 & 17,327,760 \\ \cline{1-1}  & IM-0110-0001 & 3.6 & 2251 \(\times\) 1828 & 12,344,484 \\ \hline \multirow{6}{*}{Agriculture Crop Images [53]} & maize028a & 4.1 & 224 \(\times\) 224 & 150,528 \\ \cline{1-1}  & maize032a & 4.2 & 224 \(\times\) 224 & 150,528 \\ \cline{1-1} \cline{1-1}  & rice007a & 4.3 & 224 \(\times\) 224 & 150,528 \\ \cline{1-1} \cline{1-1}  & rice011a & 4.4 & 224 \(\times\) 224 & 150,528 \\ \cline{1-1} \cline{1-1}  & wheat0002a & 4.5 & 224 \(\times\) 224 & 150,528 \\ \cline{1-1} \cline{1-1}  & wheat023a & 4.6 & 224 \(\times\) 224 & 150,528 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample datasets and test image details.  
needed to represent each pixel. A lower BPP value indicates a more efficient compression, as fewer bits are required per pixel to represent the image.  
\[BPP=\frac{\textit{Compressed Image}_{size}}{\textit{Total number of pixels in the image}} \tag{3}\]  
The Space Saving (SS) value, closely related to the Compression Ratio (CR), is another widely utilized metric in recent studies [54]. As expressed in Equation (4), the SS value measures the reduction in size relative to the original size, offering a clear indication of the algorithm's compression efficiency.  
\[SS=1\quad-\frac{\textit{Compressed Image}_{size}}{\textit{Original Image}_{size}} \tag{4}\]  
The metrics detailed above served as comprehensive tools for evaluating the performance of the algorithms, offering a robust framework for assessing its overall effectiveness.