# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 3 Model and Proposed Method
### Determining Optimal Weight Values: Decision Process  
After determining the decision-making approach for the improved CTU in this experiment, while improving CTU for bit allocation, the bit cost assigned by the default rate control algorithm is adjusted by multiplying the weight parameter as shown in (10). \[f(x)=\left\{ \begin{array}{ll}\frac{T_{f}-H_{f}-R_{L,c}}{\sum_{k=m}^{N_{L}} \omega_{L}(k)}\omega_{L}(m)\omega_{U}(m),&m\in K\\ \frac{T_{f}-H_{f}-R_{L,c}}{\sum_{k=m}^{N_{L}}\omega_{L}(k)}\omega_{L}(m)\omega _{D}(m),&m\in(U-K)\end{array} \right. \tag{10}\]  
Among them, \(\omega_{U}(m)\) is the lifting parameter, \(\omega_{D}(m)\) is a parameter for reduction, K is the set of all improved CTUs, \(U\) is the set of all CTUs for the frame, and the other parameters \begin{table}
\begin{tabular}{c c} \hline
**Conditions** & **Adjustment of K Value** \\ \hline \(N_{CTU}\) \(>\) 100 & \(K=round\Big{(}\frac{1}{5}min\Big{(}\frac{N_{CTU}}{2}+\frac{1}{4}\sum\limits_{i= 1}^{4}T_{F,i}\frac{1}{24000},\frac{N_{CTU}}{2}+\frac{N_{CTU}}{10}\Big{)}\Big{)} \cdot 5\) \\ \(10\leq N_{CTU}\leq 100\) & \(K=round\Big{(}\frac{1}{5}\Big{(}\frac{1}{2}N_{CTU}+\frac{1}{4}\sum\limits_{i= 1}^{4}T_{F,i}\frac{1}{24000}\Big{)}\Big{)}\cdot 5\) \\ \(N_{CTU}<\) 10 & \(K=round\Big{(}\frac{1}{5}\Big{(}2N_{CTU}\Big{)}\Big{)}\cdot 5\) \\ \hline \end{tabular}
\end{table} 
Table 2: Cutting of K.  
Figure 5: Feature information of mv5 as an example. (**a**) coding complexity based on SATD; (**b**) significance value based on Canny algorithm; (**c**) significance value based on color contrast algorithm. have been explained in (2). For the determination of the two, this experiment was fitted using a PLS regression model, which in turn determined the adjusted magnification of the improved CTUs. PLS regression is a multivariate statistical method for solving covariance problems. Its applicable scenario is when the number of two sets of variables is large, both of them are multi-correlated, and the number of observed data are small The model built by partial least squares regression has the advantages that traditional methods such as classical regression analysis do not have. The dataset first needs to be further expanded and information extracted by enhancing the bit allocation of the improved CTUs in the video in each dataset with different multiplicities and reducing the other CTUs in the same frame, respectively. After the coding of all the videos is completed, the multiplier that results in the most efficient improvement in the rate control of each video is used as the expansion of the dataset, which is the output of the PLS regression model. The improvement in rate control efficiency for the sequence mv5 in the dataset is shown in Figure 6, where the \(x\)-axis is the boosting multiplier, the \(y\)-axis corresponds to the decreasing multiplier, and the \(z\)-axis corresponds to the BD-PSNR, which is used to characterize the reconstruction pixel quality improvement. The highest value in the figure is 0.043 dB, which corresponds to the red data point, for the case of boosting multiplicity 1.2 and decreasing multiplicity 1. From the feature inputs of the model, the proportion of improved CTUs, average frame-level bit cost, and saliency and complexity information are selected for this experiment, as shown in Table 3.  
Figure 6: Relationship between multiplicity and rate control efficiency for the example of mv5. Where, \(N_{c}\) is the number of improved CTUs, \(C_{i}\) is the coding complexity of each CTU, \(S_{1,i}\) is the Canny significance value of each CTU, and \(S_{2,i}\) is the color contrast significance value of each CTU. To assess the predictive capability of the models, 30% of the 36 features extracted during data processing comprised the optimal multiplicity mapping data. The remaining 30% was allocated to the training set. The number of PLS principal components defined in this experiment is 5. It is worth noting that the boosted magnification is regressed using the same features as the reduced magnification.