# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results  
After applying the QP modification indicated by Equation (1) to each CU, the Peak Signal-to-Noise Ratio (PSNR) was measured with only the object-detected areas in the AI and RA configurations. In the proposed method, PSNR was not measured for the areas not including objects to evaluate the accurate image quality of the proposed method for the areas including objects. The \(\Delta\)PSNR increase in each picture was calculated using Equations (2)-(6) by the ratio of the proposed method to the anchor as follows:  
\[\Delta PSNR=\left(PSNR_{pm}-PSNR_{anc}\right)/PSNR_{anc}, \tag{2}\]  
\[PSNR_{anc}=10\cdot\log_{10}\left(\frac{MAX_{l}^{2}}{MSE_{anc}^{obj}}\right),\ MAX_{I}=\left\{ \begin{array}{cc}255,&for\ 8-bit\ pixel\ depth\\ 1023,&for\ 10-bit\ pixel\ depth\end{array} \right. \tag{3}\]  
\[PSNR_{pm}=10\cdot\log_{10}\left(\frac{MAX_{l}^{2}}{MSE_{pm}^{obj}}\right), \tag{4}\]  
\[MSE_{anc}^{obj}=\sum_{i}^{MAX_{,obj}}\frac{1}{\left(e_{x_{i}}-s_{x_{i}}\right) \left(e_{y_{i}}-s_{y_{i}}\right)}\sum_{k=s_{x_{i}}}^{e_{x_{i}}}\sum_{k=s_{y_{ i}}}^{e_{y_{i}}}\left(anc(k,l)-org(k,l)\right)^{2} \tag{5}\]  
\[MSE_{pm}^{obj}=\sum_{i}^{MAX_{,obj}}\frac{1}{\left(e_{x_{i}}-s_{x_{i}}\right) \left(e_{y_{i}}-s_{y_{i}}\right)}\sum_{k=s_{x_{i}}}^{e_{x_{i}}}\sum_{k=s_{y_{ i}}}^{e_{y_{i}}}\left(prop(k,l)-org(k,l)\right)^{2} \tag{6}\]  
where \(MSE_{pm}^{obj}\) is the Mean Squared Error (MSE) between the proposed method represented by the subscript \(pm\) and the original sequence for the areas including the detected objects represented by the superscript \(obj\). \(MSE_{anc}^{obj}\) is the MSE between the VTM-8.2 anchor represented by the subscript \(anc\) and the original sequence for the areas including the detected objects. In Equations (5) and (6), \(s_{x_{i}}\) and \(s_{y_{i}}\) are the start (\(x_{i}\),\(y_{i}\)) coordinates (left-top) of the detected object \(i\), \(e_{x_{i}}\) and \(e_{y_{i}}\) are the end (\(x_{i}\),\(y_{i}\)) coordinates (right-bottom) of the detected object \(i\), \(MAX\_obj\) is the number of objects, and \(anc(k,l)\), \(prop(k,l)\), and \(org(k,l)\) are the VTM-8.2 (anchor) decoded picture, the proposed object detection-based decoded picture, and the original picture, respectively, where (\(k,l\)) represents the (\(x\),\(y\)) coordinate of picture. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Sequence Name** & **Picture Size** & **Number of Picture** & **Frame Rate** & **Bit Depth** \\ \hline \multirow{4}{*}{A1} & Tango2 & \(3840\times 2160\) & 294 & 60 & 10 \\  & FoodMarket4 & \(3840\times 2160\) & 300 & 60 & 10 \\  & Campfire & \(3840\times 2160\) & 300 & 30 & 10 \\ \hline \multirow{4}{*}{A2} & CatRobot1 & \(3840\times 2160\) & 300 & 60 & 10 \\  & DaylightRoad2 & \(3840\times 2160\) & 300 & 60 & 10 \\  & ParkRunning3 & \(3840\times 2160\) & 300 & 50 & 10 \\ \hline \multirow{4}{*}{B} & MarketPlace & \(1920\times 1080\) & 600 & 60 & 10 \\  & RitualDance & \(1920\times 1080\) & 600 & 60 & 10 \\  & Cactus & \(1920\times 1080\) & 500 & 50 & 8 \\  & BasketballDrive & \(1920\times 1080\) & 500 & 50 & 8 \\  & BQTerrace & \(1920\times 1080\) & 600 & 60 & 8 \\ \hline \multirow{4}{*}{C} & RaceHorses & \(832\times 480\) & 300 & 30 & 8 \\  & BQMall & \(832\times 480\) & 600 & 60 & 8 \\  & PartyScene & \(832\times 480\) & 500 & 50 & 8 \\  & BasketballDrill & \(832\times 480\) & 500 & 50 & 8 \\ \hline \multirow{4}{*}{D} & RaceHorses & \(416\times 240\) & 300 & 30 & 8 \\  & BQSquare & \(416\times 240\) & 600 & 60 & 8 \\ \cline{1-1}  & BlowingBubbles & \(416\times 240\) & 500 & 50 & 8 \\ \cline{1-1}  & BasketballPass & \(416\times 240\) & 500 & 50 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Information on video sequence for each class. In Equations (3) and (4), PSNR is measured using the anchor and the proposed method, respectively. \(MAX_{I}\) represents the max value 255 or 1023, according to the sequences that are of 8-bit pixel depth or 10-bit pixel depth.