# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 5 Conclusions  
In this paper, a bit allocation scheme based on saliency and coding complexity is proposed in accordance with the VVC standard, taking into account the unevenness of bit allocation at the CTU level in the intra-rate control scheme. The construction is completed through a fusion operation that incorporates a color contrast-based significance detection algorithm, a Canny-based significance detection algorithm, a self-constructed video dataset, and a PLS principal component regression model. The findings of this study indicate that intra-rate control is enhanced by the CTC test conditions for rate control on official test sequences; the average rate control error is decreased to 4.037%, the average BD-rate is decreased by 0.33%, and the average Y-PSNR is enhanced by 0.03 dB. This outcome subtly illustrates the potential benefits and feasibility of utilizing this model to resolve analogous challenges in subsequent endeavors. The experimental procedure and results indicate that the model still has space for improvement, including the method for dividing the number and type of videos in the self-built dataset, as well as further refinement of the video enhancement of the dataset and a reduction in the multiplier. In addition to this, the lack of publicly available datasets of image-mapped video sequences poses some challenges for the experiments. In order to continue to validate the model's adaptability and application value in different video categories, it will continue to expand the video dataset as well as the corresponding multiplicity, and design more excellent bit allocation schemes. It is hoped that this research can provide good suggestions and new ideas for researchers. In future work, this paper will further focus on the rate control scheme of the VVC standard  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequences** & **BD-PSNR (dB)** & **BD-Rate (\%)** \\ \hline PeopleOnStreet & 0.00658 & \(-\)0.11867 \\ BasketballDrive & 0.01045 & \(-\)0.25067 \\ BQMall & 0.00655 & \(-\)0.08705 \\ RaceFloreses & 0.10506 & \(-\)0.87276 \\ FourPeople & 0.00560 & \(-\)0.09022 \\ BasketballDrillText & 0.02654 & \(-\)0.54218 \\ Average & 0.03 & \(-\)0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 7: BD-rate and BD-PSNR results.  
and seek to find innovative work on the basic algorithms in different coding modes as well as in more domains.  
Conceptualization, H.S.; methodology, H.S.; software, H.S.; validation, H.S.; formal analysis, H.S.; investigation, H.S.; resources, H.S.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, H.S., X.J. and Y.Z.; visualization, Y.Z.; supervision, H.S.; project administration, X.J.; funding acquisition, X.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Collaborative Innovation Achievement Program of Double First-class Disciplines in Heilongjiang Province grant number IJGXCG2023-074.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that the dataset production is part of the innovation point of this paper, the dataset will not be open-sourced at the first time, we will continue to explore new possibilities in the subsequent experimental research, and then the dataset will be made public along with the related code.  
The authors declare no conflict of interest.