# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 3 Proposed Adaptive Coding Method Using Object Dynamics
### Adaptive Bitrate by Prediction  
The adaptive bitrate function is given as follows:  
\[B^{t}=B^{t-1}+\varphi(A_{i},S_{i}), \tag{9}\]  
where the area and speed of object \(i\) are denoted \(A_{i}\) and \(S_{i}\), respectively. \(\varphi(A_{i},\,S_{i})\) is an adaptive parameter function that is described in the following. \(B^{t}\) represents the current coding bitrate at time \(t\). The bitrate of \(B^{t}\) is determined by the previous bitrate \(B^{t-1}\) with the \(\varphi(A_{i},\,S_{i})\) calculation.  
When the moving object appears, its area and moving speed will degrade the video quality. To maintain the same quality as the previous time point, the coding bitrate must be increased; however, the bitrate cannot be greater than the maximum available bandwidth. In contrast, when the moving object slows down, stops, or disappears, a higher coding bitrate is not required to maintain video quality; thus, the bitrate should be reduced to the default value.  
The proposed coding control method increases the coding bitrate to the necessary level rather than simply increasing it to the maximum. Please note that wired network transmission and the default coding bitrate are considered the standard. When a moving object appears in the video in the wireless network environment, we attempt to maintain quality that is equal to the established standard. Thus, we can achieve constant quality in every situation. However, tuning the adaptive parameter function \(\varphi(A_{i},\,S_{i})\) remains a very important issue.  
To maintain constant video quality when a moving object presents, we input the speed and area values into the model to determine which plane is closest to the \(z=0\) plane. By doing so, we are able  
Figure 2: The moving object’s area, speed, and PSNR results. (**a**) Plane generated from 43 moving objects using robust linear regression at 64 Kbps coding bitrate and (**b**) Planes created from each individual coding bitrate and the \(z=0\) plane (black) show the same PNSR as the 64 Kbps video transferred by the wired network. In order not to make the graph look too cluttered, the layer interval is set to 128 Kbps.  
to find the bitrate of the closest plane that can be used to maintain the constant video quality. Thus, we define \(\varphi(A_{i},S_{i})\) as follows:  
\[\varphi(A_{i},S_{i})=\hat{B_{t}}-B^{t-1}\text{a}=1 \tag{10}\]  
Here, \(\hat{B_{t}}\) is the predictive bitrate of object \(i\) defined as follows:  
\[\hat{B_{t}}=argmin(dP_{1}(A_{i},S_{i}),\ldots\ldots,dP_{n}(A_{i},S_{i})), \tag{11}\]  
where \(dP_{j}(A_{i},S_{i})\) is the distance of object \(i\) from \(z=0\) to the \(j\)th bitrate regression plane.  
Figure 3 shows that, when moving objects appear, sufficient bandwidth for moving objects can be obtained using Equations (9)-(11) could let 64 K wireless has almost the same image quality as 64 K wired. The proposed method determines the required bitrate according to the moving object's area and speed for saving network bandwidth. In Figure 4, the blue diamond line shows the network bandwidth used by the proposed method, and the pink star line shows the efficient packet delivery methods used. Generally, the other methods will adjust to the maximum bandwidth to improve image quality when the moving object appears.  
Figure 3: Using the adaptive algorithm, the blue diamond line shows that the bitrate changes when a moving object appears.