## 4 Experimental Results and Analysis
### Quantization Calibration Set Optimization Experiment  
One hundred independent infrared images from various scenarios were selected for the quantization calibration experiment (some of the images are shown in Figure 14) and the quantization increment experiments of the quantization calibration set were conducted in two ways. Table 5 shows the increment based on the image dynamic range, and Table 6 shows the increment based on the image variance. Cosine similarity and relative Euclidean distance were used as evaluation metrics to reflect the quantization effect. As shown in Tables 4 and 5, the experimental results indicate that the dynamic range and variance of the infrared images are positively correlated with the quantization effect. From a portion of the experimental data, it can also be concluded that when the maximum dynamic range remains unchanged and the maximum variance increases, the quantization effect remains the same; however, when the maximum variance remains unchanged and the maximum dynamic range increases, the quantization effect improves. Therefore, the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Bits \& Channel**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\begin{tabular}{c} **Memory Copy** \\ **Time/μs** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Hardware Computation** \\ **Time/μs** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Data Processing** \\ **Time/μs** \\ \end{tabular} } & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**FPS**} \\ \hline \multirow{4}{*}{Int16c4} & 1 & 6065 & 2678 & 24,127 & 32,870 & 30.42 \\  & 2 & 6023 & 2634 & 22,909 & 31,566 & 31.67 \\  & 3 & 6022 & 2800 & 26,492 & 35,314 & 28.31 \\  & 4 & 6071 & 2790 & 29,959 & 38,820 & 25.75 \\  & average & 6045 & 2725 & 25,871 & 34,641 & 28.86 \\ \hline \multirow{4}{*}{Int16c1} & 1 & 1587 & 1780 & 16,235 & 19,602 & 51.01 \\  & 2 & 1623 & 1802 & 15,965 & 19,390 & 51.57 \\  & 3 & 1590 & 1906 & 16,325 & 19,821 & 50.45 \\  & 4 & 1577 & 1846 & 16,060 & 19,483 & 51.32 \\  & average & 1594 & 1833 & 16,146 & 19,573 & 51.09 \\ \hline \multirow{4}{*}{Int8c4} & 1 & 3108 & 2101 & 24,151 & 29,360 & 34.05 \\  & 2 & 3036 & 2077 & 23,422 & 28,535 & 35.04 \\ \cline{1-1}  & 3 & 3005 & 2017 & 26,045 & 31,067 & 32.18 \\ \cline{1-1}  & 4 & 3005 & 2122 & 30,127 & 35,254 & 28.36 \\ \cline{1-1}  & average & 3038 & 2079 & 25,936 & 31,053 & 32.20 \\ \hline \multirow{4}{*}{Int8c1} & 1 & 998 & 1111 & 15,811 & 17,920 & 55.80 \\ \cline{1-1}  & 2 & 934 & 1212 & 15,787 & 17,933 & 55.76 \\ \cline{1-1} \cline{2-6}  & 3 & 969 & 1149 & 15,892 & 18,010 & 55.52 \\ \cline{1-1}  & 4 & 984 & 1139 & 15,790 & 17,913 & 55.82 \\ \cline{1-1}  & average & 971 & 1152 & 15,820 & 17,943 & 55.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the different quantization bits and different input channels. maximum dynamic range of an image significantly influences the quantization effect. Furthermore, it is essential to select a sufficient number of quantization calibration sets to ensure they encompass a diverse range of images with both wide dynamic ranges and high variance. In addition, experiments on the quantization effects of different batch sizes were conducted, as shown in Table 7. The results indicate that as batch size increases, the quantization effect improves, and the optimal quantization effect is attained with a batch