## 3 Proposed Methods  
### Object Detection-Based Video Compression  
The current block-based video coding applies the same quantization for the whole picture because it does not consider detected-object regions. Here, we propose object detection-based video compression by the encoder and decoder, which allocates lower quantization parameters to the detected-object regions and higher quantization parameters to the background. Therefore, better image quality is obtained for the detected objects on the decoder side compared with the existing video coding standard. The combined method of the VVC codec and the YOLO object detection algorithm is designed to control QPs in VVC. The decoder block diagram of the proposed object detection-based video compression is shown in Figure 3. The object information, such as the center (x, y) coordinate of each object, the width and height of each object, and the name of each object (object index), are obtained from the object detection. In the Object Detection Decoder, the Feature Decoder holds the object information and Machine Analysis module infers the results. The object information obtained by the Feature Decoder is used in the VVC Decoder. Using the Object Detection Decoder and VVC Decoder, an object detection-based compression method is proposed to modify the QP of the corresponding CU by transmitting the object information (features) in the picture. In this paper, the object information obtained by the Feature Decoder is applied to both the VVC Decoder and Machine Analysis modules.  
Figure 2: Examples of VCM pipelines: (**a**) video coding, (**b**) feature coding, and (**c**) hybrid coding, in which Video Encoder and Video Decoder represent the VVC encoder and decoder, respectively [22].  
Tables 1 and 2 show the object indices that are used to classify the name of objects [20], where voc.data in Table 1 and coco.data in Table 2 have a maximum of 20 and 80 objects, respectively. The small PSNR gain in the detected object areas is meaningful in the object detection-based video coding using the VVC standard because any machine analysis techniques on the decoder side can be applied to the PSNR-improved detected object areas for further processing, such as face recognition, segmentation, and shape extraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Name of Object** & **Index** & **Name of Object** \\ \hline
0 & airplane & 10 & table \\
1 & bicycle & 11 & dog \\
2 & bird & 12 & horse \\... &... &... &... \\
4 & bottle & 14 & person \\
8 & chair & 18 & train \\
9 & cow & 19 & monitor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Name of objects and object index in voc.data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Name of Object** & **Index** & **Name of Object** \\ \hline
0 & person & 60 & table \\
1 & bicycle & 61 & toilet \\
2 & car & 62 & monitor \\
3 & motorbike & 63 & laptop \\... &... &... &... \\
18 & sheep & 78 & hair drier \\
19 & cow & 79 & toothbrush \\ \hline \hline \end{tabular}
\end{table}
Table 2: Name of objects and object index in coco.data.  
Figure 3: Block diagram of the proposed object detection-based video compression in the decoder. (object information from the Feature Decoder: number of detected objects and, for each object index, starting (x, y) coordinate, width and height of objects, etc.).  
Figure 4 shows the performance of YOLO, which detects objects in the common test condition (CTC) original sequence. The names of objects defined differently from those in Tables 1 and 2 can be used in the proposed method. In the following subsections, the proposed method that uses the object information to control the QP values in the coding blocks is explained in detail.