# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
The shoelace in the yellow box is clearly visible in the original cropped image in Figure 10a, but the shoelace is less visible in Figure 10b, which is compressed by VTM-8.2 with the QP value of 27 in the RA configuration. It seems that a large amount of quantization noise is visible in the yellow box. In Figure 10c, since the shoelace is included in the object-detection area, the cropped image was compressed with a QP value less than 27, so the shoelace is clearly visible in Figure 10c due to less quantization noise. Figure 11a-c shows the cropped 5 th pictures of the BasketballDrive sequence, where the start (x, y) coordinate of the cropped image is (1080, 400) and (width, height) is (170, 185). In Figure 11b, compressed by VTM-8.2 with the QP value of 37 in the RA configuration, blocking artifacts and ringing artifacts are visible on the basketball, compared with the original cropped image in Figure 11a. However, in Figure 11c, blocking artifacts disappear from the ball. Since the proposed method detects the basketball and basketball player as objects, it was compressed with a QP value less than 37. Since the VTM-8.2 anchor compresses the whole picture with a high QP value 37, the overall video quality is not sufficient compared with the original picture. However, since the objects that are detected  
Figure 11: Experimental results in the cropped 5 th picture of the BasketballDrive sequence, where the start (x, y) coordinate of the cropped image is (1080, 400) and (width, height) is (170, 185): (**a**) original sequence, (**b**) anchor (VTM-8.2) compressed with QP = 37 in the RA configuration, and (**c**) proposed method in the RA configuration. by the proposed method are compressed at a lower QP value, the objects in the proposed method are shown with better quality than those in Figure 11b. In the experiments, the QP value difference \(QP_{\textit{Offset}}\) in Equation (1) was set to 4. The BD-rate indicates the bitrate reduction ratio over the anchor (VTM-8.2) in the same PSNR. For example, a positive BD-rate value means that the coding efficiency is decreased. The BD-rates for the Y, Cb, and Cr components were calculated, with \"Average BD-rate\" meaning the average BD-rate for each component for all class sequences. Table 5 shows the experimental results for BD-rate in all sequences. In the RA configuration, the BD-rates of Y, Cb, and Cr decreased to 2.33%, 2.67%, and 2.78%, respectively. In the AI configuration, the BD-rates of Y, Cb, and Cr decreased to 0.59%, 1.66%, and 1.42%, respectively. When object detection-based video encoding was performed, the BD-rate of video sequences dropped compared with the VVC standard because:  
* Even if the PSNR is high in the object-detected areas, background areas that do not include object areas usually take up more area than the detected object areas in a picture, so the PSNR decreased due to a high QP value in the background areas, and the bitrates increased to compensate for the distortion. * In the case of intra prediction, the already-decoded reference samples with a high QP value that were used to encode the current block had relatively higher quantization errors than those samples decoded with a low QP value, so that the coding efficiency dropped due to the high intra prediction error. * In the case of inter prediction using the P (Predictive) and B (Bi-directional predictive) pictures, the background areas in the previous reference pictures were decoded with a high QP value, which was used to predict that each block in the current picture would have relatively higher quantization errors than those areas decoded with a low QP value; thus, the coding efficiency dropped due to the high inter prediction error, i.e., when the proposed object detection-based video coding was applied, the PSNRs of the previously decoded pictures were lower than those of the previously decoded pictures in VVC.