# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 3 Materials and Methods
### Initialization  
In the process of population initialization, the dimension of the population must be determined. The population dimension is usually proportional to the dimension of the problem and affects the convergence speed and accuracy of the algorithm. The larger the population, the stronger the diversity and the more the search space is expanded. The range and the possibility of obtaining the optimal solution decrease, but a large-scale population will inevitably increase the number of evaluations of the fitness function. If the fitness function operation is complex and the execution time is long, the time complexity of the entire algorithm will increase exponentially. However, if the population size is small, the diversity is relatively low, and the algorithm search space is narrow, it will accelerate the convergence speed of the algorithm and easily lead to local optimality or algorithm stagnation [47]. Therefore, the choice of population size generally requires a balance between the convergence speed and search capabilities of the algorithm, while also considering the size and complexity of the problem. The resolution of 360-degree panoramic video is large, resulting in a large number of CTUs in each video frame. The resolution of the 360-degree panoramic video test sequence used in this article is 3840 \(\times\) 1920, and the size of each CTU is 64 \(\times\) 64, so the number of CTUs in each frame is 1800. In the differential evolution algorithm, the dimension of the population is usually related to the number of populations and the number of iterations. If the quantization parameter decision is made directly for 1800 CTUs, that is, the dimension corresponding to each population size is 1800, it will result in the number of populations and the number of iterations required by the algorithm, thus increasing the time complexity of the algorithm. Since in the ERP projection format, CTUs at the same latitude have the same sampling degree--that is, the CTUs in each line of the projected two-dimensional video have the same sampling degree--by analyzing the 360-degree panoramic video in the ERP projection format, the number of CTU rows of the two-dimensional video after projection is regarded as the population dimension of the differential evolution algorithm.  
After determining the dimensions of the population, the population size and number of iterations based on the complexity of the problem are decided. Since mutation operation is required for each individual in the population in the differential evolution algorithm, the process randomly selects three individuals that are different from the current individual, so the population size cannot be less than 4. By changing the value of the population size and conducting multiple experimental verifications and taking into account the convergence speed and algorithm performance, the proposed algorithm sets the population size to 50 and the maximum number of iterations to 75.  
In H.265/HEVC, the optional values of the quantization parameter QP range from 0 to 51, with a total of 52 optional values. The initial population is established by randomly determining the optional value of the quantization parameter for each individual in the population, expressed as (X\({}_{1}\), X\({}_{2}\),..., X\({}_{50}\)), where X\({}_{1}\) = (QP\({}_{1,1}\), QP\({}_{1,2}\),..., QP\({}_{1,N}\)). Figure 2 shows the correspondence between X\({}_{1}\) individuals in the population and CTUs in each row.  
Figure 1: Quantization parameter decision scheme based on differential evolution.