# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
The results derived from Figures 6 and 7 confirm that the quality of reference pictures including objects that have slow and steady motion significantly affects the results of PSNR in the proposed method. Figure 8 shows the example of the object-detected 13 th picture in the 3840 \(\times\) 2160 DaylightRoad2 sequence of class A2, where DaylightRoad2 consists of vehicles and traffic-related objects. Since it is a video sequence, specialized for object detection, the detection accuracy was quite high. The width and height of object detection between pictures were constant, which favorably affects the quality of reference pictures. Therefore, performance improvement of the proposed method is observed in DaylightRoad2. The average \(\Delta\)Y-PSNR, 0.73% at the QP value of 22, in the AI configuration shows better performance than the 0.23% at the QP value of 22 in the RA configuration. It can be inferred that the B- or \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Random Access (RA)**} & \multicolumn{4}{c}{**All Intra (AI)**} \\ \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**QP**} & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** \\  & & & \(\Delta\)**-PSNR** & \(\Delta\)**Cb-PSNR** & \(\Delta\)**Cr-PSNR** & \(\Delta\)**Y-PSNR** & \(\Delta\)**Cb-PSNR** & \(\Delta\)**Cr-PSNR** \\ \hline \multirow{5}{*}{**Class A1**} & **Class A1** & 0.11\% & 1.58\% & 0.29\% & 0.18\% & 0.10\% & 0.11\% \\  & **Class A2** & 0.25\% & 0.18\% & 0.18\% & 0.40\% & 0.15\% & 0.16\% \\ \cline{1-1}  & **Class B** & \(-\)0.02\% & \(-\)0.61\% & \(-\)0.37\% & 0.65\% & 0.20\% & 0.22\% \\ \cline{1-1}  & **Class C** & 0.39\% & 0.29\% & 0.26\% & 1.03\% & 0.54\% & 0.50\% \\ \cline{1-1}  & **All** & **0.18\%** & \(-\)0.11\% & \(-\)0.02\% & **0.71\%** & **0.30\%** & **0.30\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 6: Example of the object-detected pictures in the 1920 \(\times\) 1080 MarketPlace sequence: (**a**) 226 th picture, (**b**) 234 th picture, (**c**) 240 th picture, and (**d**) 246 th picture. (purple colored block: person; green colored block: truck; blue colored block: chair). P-picture in the RA configuration had a poor PSNR result due to low PSNR results on the reference pictures. In other words, as the video compression proceeded in the RA configuration, \(\Delta\)PSNR of the image decreased due to the poor quality of reference pictures. Moreover, the object detection (which sometimes fails to detect the objects that should be detected) did not accurately predict all objects. Therefore, when the false-alarm object regions are compressed with a high QP, and those regions are used as a reference picture, the \(\Delta\)PSNR result is low because of the low quality of the reference picture. In common with the AI and RA configurations, it seems that the lower the QP, the better the performance. Figure 8: Example of the object-detected 13 th picture in the 3840 \(\times\) 2160 DaylightRoad2 sequence of class A2. Figure 7: Example of the object-detected 501 st picture in the 1920 \(\times\) 1080 BQTerrace sequence. Figure 9a-c shows the cropped 501 st pictures of the BQTerrace sequence where the starting point (x, y) of the cropped image is (1480, 470) and (width, height) is (140, 125). Figure 9a is the original cropped image. In Figure 9b, compressed with QP = 32 in the RA configuration, image noise is observed around the head of the person in the yellow box. However, in Figure 9c, when the proposed compression is performed, image quality is improved, which reduces the noise around the head in the yellow boundary box. This is because the proposed method provides better image quality by modifying the QP values. Figure 10a-c shows the cropped 100 th pictures of the BasketballDrive sequence, where the start (x, y) coordinate of the cropped image is (1525, 800) and (width, height) is (180, 215).