# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 1 Introduction
[PERSON] and [PERSON] [38] proposed an adaptive algorithm for the two important links of mutation and crossover operations in the differential evolution algorithm so that the mutation factor and crossover rate during the algorithm execution can be adaptively changed; [PERSON] [39] and others limited the range of some parameters in the algorithm and used different control parameters to solve certain specific problems. Finally, through experiments, they selected the best control parameters for each problem; [PERSON] et al. [40] proposed applying a larger mutation factor to the initial iteration process of the algorithm, which makes the algorithm have strong search capabilities at the beginning of the iteration and then makes the mutation factor linearly decrease as the number of iterations increases. Although the DE algorithm has been studied for nearly thirty years and there are many improved DE algorithms, most of them can only be applied to low-dimensional problems. For high-dimensional problems, there are currently few optimization solutions. For example, some researchers used co-evolutionary optimization algorithms [41; 42]. The core idea is to use a divide-and-conquer strategy to decompose the original problem into multiple sub problems until each sub problem can be solved individually, and then the results can be fused to obtain the optimal solution to the original problem. In order to speed up the convergence speed on large-scale high-dimensional problems, [PERSON] et al. [43] combined fast evolutionary programming (FEP) with co-evolution. Recently, for different practical applications, researchers have made different improvements to the differential evolution algorithm. The original algorithm treats the individuals in a population equally and evolves all of them in each generation. In order to overcome this shortcoming, ref. [44] proposed a new population evolution strategy (PES) to decrease the population size based on the differences among individuals during an evolution process and applied it to waveform inversion. The improved algorithm reduced the runtime by 50% compared to the original algorithm. For the problem of object tracking, [PERSON] [45] chose fast-compressive tracking for rapid real-time tracking with the element of compressed sensing theory and used a DE algorithm to search for the optimal parameters by iteratively solving and improving a solution with a proper objective function. As a result, the proposed model enhances the ability to tackle occlusion and out-of-plane rotation problems. With the rapid development of machine learning and deep learning technologies, differential evolution algorithms have also been applied to artificial intelligence (AI) methods. [PERSON] [46] proposes a novel personalized recommendation algorithm for learning resources based on DE and graph neural networks (GNN). The differential evolution algorithm is utilized to optimize model hyperparameters, resulting in improved recommendation performance. In conclusion, the differential evolution algorithm has become a stable and efficient parameter optimization algorithm. Therefore, a reasonable idea is to use differential evolution algorithm to make decisions for encoding parameters of 360-degree panoramic video based on the data characteristics of the video, so as to improve coding efficiency.