# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 4 Experimental Results
The error distribution chart Figure 7 shows the difference between the coding bitrate estimated by the linear regression model and the original coding bitrate for each scenario. The reduced value is positive, which means that our model predicts coding bitrate better than the original method in terms of obtaining the same video quality. In addition, the linear regression method was used. The results show that 28 scenarios running at lower coding bitrate can achieve the required image quality; 11 scenarios running at the same bitrate have no change in image quality, and 18 scenarios need to run on a larger coding bitrate to complete the task. The results of our experiment show that for the 18 scenarios, we need to increase the bitrate on average by 96 Kbps when using our method, whereas for the 28 scenarios we can reduce the bitrate by 194.29 Kbps on average when using our linear regression model. The moving object in scenario 9 has a small area and slow speed, and its estimated coding bitrate is 512 Kbps, the same as the exact value. In Figure 8, the x-axis is the frame number at which the moving object appears, and the y-axis shows the PSNR value of each frame. The red line (+) is the PSNR value for coding at 64 Kbps without a network transfer. The green line (*) is the PSNR value for coding at 64 Kbps transferred through the network environment. As can be seen, when the moving object appears, the PSNR values decrease significantly. The blue line (-) represents the estimated PSNR value for a 512 Kbps coding bitrate transferred by the network environment. As shown, the blue line is closer to the red line than the green line. Figure 6: The experimental video scenes. (**a**) Experimental videos and (**b**) moving objects in experimental videos. We captured frames 119, 154, and 236 of the moving object from the video and show them in Figure 8a. In Figure 8a, the bottom row shows the distortion result of those frames at a coding bitrate of 64 Kbps under network transfer, and the upper row shows the results of the same frames at an estimated coding bitrate (i.e., 512 Kbps) and network transfer. The video quality obviously improved by increasing the coding bitrate. Figure 8b shows the results of scenario 15. Here, the area of the moving object is similar to that of scenario 9 but the object has slower speed. Please note that the estimated and real values show no differences. Figure 8: **(a)** The red line (+) represents the PSNR value for coding at 64 Kbps without network transfer. The green line (*) represents the PSNR value for coding at 64 Kbps transferred through the network environment. The blue line (–) represents the estimated PSNR value for a 512 Kbps coding bitrate transferred by the network environment. As shown in the figure, the blue line is closer than the green line to the red line. It is clear that frames 119, 154, and 236 show a better image quality. **(b)** Another set of the moving objects. Similarly, the result shows that the moving objects at the frame 125, 188, and 280 positions have better image quality except that their moving speed is slower. Figure 7: The error distribution chart shows the difference between the coding bitrate estimated by the linear regression model and the original coding bitrate for each scenario. The positive reduced value means that the bitrates predicted by our model is better than those predicted by the original method. Each unit in the reduced value is 64 Kbps. Taking scenario 3 as an example, our method saves 256 Kbps. Figure 9a shows the experiment results of scenario 38. Here, there is a 192 Kbps difference (i.e., three units). The moving object has a large area and higher speed. When the moving object appears in the video, the PSNR value is reduced significantly. When we input this scenario into our model, we obtain an estimated coding bitrate of 768 Kbps (blue line). Please note that there is a significant gap between the blue and red lines, where the red line is the experimental goal. In theory, it should fall in 960 Kbps, even if we set the limit to 1 Mbps.