# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{6}{c}{**Random Access (RA)**} & \multicolumn{6}{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 A2**} & \multirow{5}{*}{**CatRobot1**} & **22** & 0.19\% & 0.11\% & 0.18\% & 0.45\% & 0.07\% & 0.19\% \\  & & **27** & 0.14\% & 0.09\% & 0.16\% & 0.17\% & 0.05\% & 0.17\% \\  & & **32** & 0.17\% & 0.07\% & 0.21\% & 0.23\% & 0.03\% & 0.10\% \\  & & **37** & 0.21\% & 0.01\% & 0.19\% & 0.29\% & 0.09\% & 0.18\% \\ \cline{2-8}  & \multirow{5}{*}{**DaylightRoad2**} & **22** & 0.23\% & 0.08\% & 0.08\% & 0.73\% & 0.07\% & 0.10\% \\  & & **27** & 0.08\% & 0.10\% & 0.07\% & 0.10\% & 0.06\% & 0.09\% \\  & & **32** & 0.09\% & 0.11\% & 0.06\% & 0.13\% & 0.04\% & 0.07\% \\  & & **37** & 0.09\% & 0.23\% & 0.13\% & 0.19\% & 0.06\% & 0.07\% \\ \cline{2-8}  & \multirow{5}{*}{**ParkRunning3**} & **22** & 0.68\% & 0.60\% & 0.53\% & 0.73\% & 0.51\% & 0.39\% \\  & & **27** & 0.51\% & 0.35\% & 0.27\% & 0.68\% & 0.43\% & 0.28\% \\  & & **32** & 0.37\% & 0.25\% & 0.17\% & 0.60\% & 0.26\% & 0.16\% \\  & & **37** & 0.26\% & 0.18\% & 0.07\% & 0.49\% & 0.17\% & 0.11\% \\ \hline \multirow{5}{*}{**MarketPlace**} & **22** & \(-0.45\%\) & \(-1.90\%\) & \(-1.15\%\) & 0.44\% & 0.14\% & 0.14\% \\  & & **27** & \(-0.47\%\) & \(-1.98\%\) & \(-1.16\%\) & 0.36\% & 0.13\% & 0.14\% \\  & & **32** & \(-0.49\%\) & \(-2.11\%\) & \(-1.16\%\) & 0.40\% & 0.10\% & 0.10\% \\  & & **37** & \(-\)**0.53\%** & \(-\)**2.39\%** & \(-\)**1.17\%** & 0.40\% & 0.14\% & 0.15\% \\ \hline \multirow{5}{*}{**KittualDance**} & **22** & \(-0.25\%\) & \(-1.23\%\) & \(-1.01\%\) & 0.54\% & 0.16\% & 0.18\% \\  & & **27** & \(-0.29\%\) & \(-1.29\%\) & \(-1.09\%\) & 0.53\% & 0.20\% & 0.21\% \\  & & **32** & \(-0.35\%\) & \(-1.33\%\) & \(-1.12\%\) & 0.45\% & 0.19\% & 0.12\% \\  & & **37** & \(-0.39\%\) & \(-1.24\%\) & \(-1.13\%\) & 0.39\% & 0.16\% & 0.22\% \\ \cline{2-8}  & \multirow{5}{*}{**Cactus**} & **22** & 0.24\% & 0.09\% & 0.10\% & 0.85\% & 0.17\% & 0.25\% \\  & & **27** & 0.22\% & 0.08\% & 0.09\% & 0.50\% & 0.14\% & 0.27\% \\  & & **32** & 0.21\% & 0.06\% & 0.10\% & 0.56\% & 0.12\% & 0.19\% \\  & & **37** & 0.21\% & 0.03\% & 0.00\% & 0.57\% & 0.17\% & 0.21\% \\ \cline{2-8}  & \multirow{5}{*}{**BasketballDrive**} & **22** & 0.33\% & 0.12\% & 0.22\% & 0.94\% & 0.23\% & 0.38\% \\  & & **27** & 0.20\% & 0.11\% & 0.21\% & 0.49\% & 0.26\% & 0.42\% \\  & & **32** & 0.18\% & 0.13\% & 0.18\% & 0.39\% & 0.21\% & 0.30\% \\  & & **37** & 0.15\% & 0.12\% & 0.14\% & 0.42\% & 0.31\% & 0.36\% \\ \hline \multirow{5}{*}{**BQTerrace**} & **22** & 0.36\% & 0.14\% & 0.15\% & **2.25\%** & 0.38\% & 0.31\% \\  & & **27** & 0.21\% & 0.15\% & 0.14\% & 0.97\% & 0.29\% & 0.22\% \\  & & **32** & 0.22\% & 0.13\% & 0.13\% & 0.79\% & 0.20\% & 0.15\% \\  & & **37** & 0.26\% & 0.12\% & 0.05\% & 0.71\% & 0.20\% & 0.16\% \\ \hline \multirow{5}{*}{**BasketballDrill**} & **22** & 0.49\% & 0.37\% & 0.47\% & 1.01\% & 0.66\% & 0.77\% \\  & & **27** & 0.33\% & 0.26\% & 0.28\% & 0.78\% & 0.66\% & 0.77\% \\  & & **32** & 0.24\% & 0.30\% & 0.27\% & 0.64\% & 0.54\% & 0.46\% \\  & & **37** & 0.13 Figure 7 shows the example of the object-detected 501 st picture in the 1920 \(\times\) 1080 BQTerrace sequence in class B. Many vehicles and people are detected in the BQTerrace sequence, where the vehicles and people have relatively steady and slow motion compared with the MarketPlace sequence in Figure 6. As the object detection is successful, the prediction performance using the reference P and B pictures is also accurate, so that the average \(\Delta\)Y-PSNR improved to 2.25% at the QP value of 22.