# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 1 Introduction
However, in the HEVC encoder, the BLAQ method needs to find an adaptive QP for each block through rate-distortion optimization (RDO), so the computational complexity of the HEVC encoder increases greatly when BLAQ is enabled. In [26], [PERSON] et al. proposed a quantization matrix (QM) method to improve subjective quality control and avoid hypothetical reference decoder (HRD) overflow in HEVC. QM methods that take advantage of the spatial and frequency domain awareness of the human visual system (HVS) have been used in image/video coding for a long time. However, a challenge faced by QM methods is that the distortion cost is usually measured in terms of peak signal-to-noise ratio (PSNR), which correlates poorly with HVS models [27]. In addition, the quantization step size is also a key parameter that affects the quantization process. Some studies [28; 29] use rate-distortion-optimized quantization to select the optimal quantization step size, but the computational complexity will increase accordingly. However, the video sources of the above research based on HEVC are ordinary two-dimensional planar video, and the optimization of the emerging 360-degree panoramic video with three degrees of freedom is relatively rare. Moreover, the latter has more special video characteristics, which is beneficial to improving coding efficiency. VVC is the latest video coding standard jointly developed by ITU-T VCEG and ISO/IEC MPEG. Compared to HEVC, VVC can provide higher compression performance. However, the improvement of compression performance must be accompanied by the increase in coding complexity. For example, VVC provides a more complex CU division method, intra-frame prediction, which provides many new coding tools to adapt to different video scenes. Therefore, a considerable amount of current research is on how to reduce the coding time on the premise of ensuring the quality of video. Ref. [30] introduces the type of VVC block partition and the processing method of image boundary in detail. Aiming at the problem of high time complexity of VVC coding, the main idea of [31] is to filter out IBC and ISP, which are likely unused. By rearranging the common prediction model, the candidate is more likely to become the optimal model, and some neural network methods are also adopted. This candidate's advantage is that it takes into account the newly added VVC in-frame mode tools and can also achieve some performance improvement. Ref. [32] uses a histogram of oriented gradient (HOG) algorithm pruning forecast and can also achieve good performance, improved by 36.61%. The advantage of this method is that it is very suitable for the hardware algorithm, but the disadvantage is that the performance loss is large. In the inter-frame prediction module, there is also some research related to fast algorithms. Ref. [33] skips part of the affine search process of cu through the strategy of early termination, thus reducing the coding time. On the basis of a 63% reduction in codingtime, the video loss quality will not be very high. In the hardware direction, [34] reduces the area of the interpolation circuit and the cost of hardware by reducing the number of filter taps in the search process of fractional motion estimation (FME) with acceptable performance loss. The differential evolution (DE) algorithm is a parameter optimization method based on the evolutionary theory proposed by [PERSON] and [PERSON] in 1995 [35]. It is widely used in global optimization problems in the fields of science and engineering [36]. Researchers in various fields have proposed many improved DE algorithms by performing some parameter adjustments and problem evolution on the traditional DE algorithm. [PERSON] et al. [37] added some auxiliary populations based on the original population and proposed rules for automatically calculating scaling parameters to improve its efficiency and robustness.