# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 1 Introduction
In order to further reduce the degree of distortion, researchers continue to increase the number of projection planes. For example, the OHP projection format projects a sphere onto an octahedron and then performs extended splicing; the ISP format projection projects a sphere onto an icosahedron and then performs splicing. However, an increase in the number of projection planes will have the effect of increasing discontinuity boundaries and will also have an adverse impact on the prediction process, resulting in a decrease in coding performance. In order to solve the encoding-related problems caused by severe distortion in the bipolar areas in the ERP projection format, [PERSON] et al. [10] divided the projection plane of the ERP format into different tiles according to latitude and realized the dynamics of sampling density by adjusting the height and width of the tiles, thus solving the problem of bit allocation at different latitudes. Similarly, [PERSON] et al. [11] also conducted in-depth research on the latitude direction of the ERP projection format and proposed performing downsampling pixel by pixel according to the latitude value. That is, the higher the latitude, the greater the downsampling ratio; the lower the latitude, the smaller the downsampling ratio. Although this method can achieve great bitrate savings and is suitable for situations where the transmission bandwidth is limited, the computational complexity of the algorithm is very high. In addition, during the quantization process, the degree of quantization of the transformed prediction residual has a great impact on the bitrate. If some areas can be coarsely quantized with less impact on visual quality, the bitrate will be further saved. Therefore, [PERSON] et al. [12] proposed performing coarse quantization on high-latitude areas and fine quantization on low-latitude areas in the projection plane of ERP format, thereby further reducing the bit rate while maintaining stable visual quality. H.265/HEVC coding standard adds new technologies to many coding modules. For example, the number of angle prediction modes for intra-prediction has increased to 35; advanced motion vector prediction (AMVP) technology and merge technology are provided during inter-prediction [13; 14; 15; 16; 17; 18]; the quantization process proposes the implementation method of the quantization group [19; 20; 21]. These new tools have greatly improved coding efficiency. However, the increase in new coding technologies will inevitably lead to an increase in coding parameters, and the selection of some coding parameters in the H.265/HEVC coding standard is unreasonable. For example, the quantization process requires the use of corresponding quantization parameters for each coding tree unit (CTU). In H.265/HEVC coding standards, the quantization parameter value of each CTU is fixed. Such a quantization parameter allocation method lacks adaptability and will inevitably lead to a waste of bitrate and a reduction in rate-distortion performance. Since quantization will bring distortion to the compressed video data; it is particularly important to develop efficient and low-complexity quantizers and study efficient quantization algorithms [22; 23]. During the HEVC standardization process, many improvements to the quantization method were proposed. [PERSON]. [24] combined the encoded frame data with the Laplacian distribution and proposed an adaptive quantization method based on inverse quantization parameters. However, the accuracy of the results will be affected to some extent. [PERSON]. [25] proposed a quantization algorithm for improving subjective quality, named block-level adaptive quantization (BLAQ). In BLAQ, each block has its own quantization parameters which can better adapt to the local content of the video sequence. In addition, BLAQ can effectively reduce the non-uniformity of the video source of the quadtree-based hybrid video codec, which divides each CTU into sub-blocks and then performs a uniform reconstruction quantizer (URQ).