# Region-of-Interest Based Coding Scheme for Live Videos
## 6 Conclusions  
In this paper, we propose an ROI-centric rate control algorithm for High Efficiency Video Coding (HEVC). The innovative algorithm operates akin to a reservoir management system and synergistically combines it with a Convolutional Neural Network (CNN)-based parameter prediction network. This integration aims to enhance the quality metric of Regions of Interest (ROI) while maintaining the overall stability of the video bitrate.This proposed scheme proves highly effective in scenarios where high-resolution, high-frame-rate, and high-bitrate live broadcasts are essential. It is versatile across various content types and delivers superior results. Not only does it ensure improved quality and uniformity within the ROI regions but also achieves a significantly lower bitrate compared to direct Quantization Parameter (QP) adjustments in CU as implemented by x265. Thus, it offers better Quality of Service (QoS) in demanding live broadcast environments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video Sets** & **R-\(\lambda\)** & **ROI Constant QPoffset** & **ROI Constant QPoffset vs. R-\(\lambda\)(\%)** & \(K_{roi}\) = 4 & \(K_{roi}\) = 4 vs. R-\(\lambda\)(\%)** \\ \hline Class B & 3.60 & 3.79 & 5.4\% & 4.01 & 11.4\% \\ Class C & 2.85 & 2.90 & 1.8\% & 3.09 & 8.4\% \\ Class D & 3.61 & 3.71 & 2.7\% & 3.93 & 9.0\% \\ Class E & 4.22 & 4.23 & 0.4\% & 4.45 & 5.5\% \\ Class F & 3.18 & 3.26 & 2.9\% & 3.54 & 11.0\% \\ Average & 3.49 & 3.58 & 2.5\% & 3.80 & 9.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: ClassB–ClassF MOS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video Sets** & **ABiRate (dbpps)** & **AROI-PSNR (dbl)** & **AROI-SSIM** & **AROU-PSNR (dbl)** & **AROU-SSIM** \\ \hline Class B & 4.2\% & 1.43 & 0.017 & \(-\)0.32 & \(-\)0.004 \\ Class C & 2.8\% & 1.52 & 0.028 & \(-\)0.45 & \(-\)0.007 \\ Class D & 1.5\% & 3.25 & 0.038 & \(-\)0.71 & \(-\)0.011 \\ Class E & 2.2\% & 2.92 & 0.043 & \(-\)0.53 & \(-\)0.009 \\ Class F & 3.5\% & 1.87 & 0.025 & \(-\)0.78 & \(-\)0.006 \\ Average & 2.8\% & 2.20 & 0.030 & \(-\)0.56 & \(-\)0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ClassB–ClassF ABiRate and \(\Delta\)metrics, \(K_{roi}\) = 4 vs. R-\(\lambda\).  
In future work, we will enhance the prediction accuracy by employing CNN methods and conducting temporal analysis through the use of multiple reference frames, aiming to improve the precision of encoding complexity prediction. Moreover, the selection of coding modes in the current frame significantly impacts the accuracy of bitrate control; hence, in subsequent stages, we plan to model and quantify this influence, incorporating it into our CNN-based bitrate prediction model.  
Conceptualization, X.D.; Methodology, X.D.; Software, X.D.; Validation, X.D.; Writing--review & editing, X.D.; Supervision, X.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the company Tencent. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.