# A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
## 2 Related Work  
Currently, studies on fast algorithms for VVC can be divided into two parts: artificial intelligence-based methods and coding content-based methods. For artificial intelligence-based methods, in [5], a fast algorithm based on machine learning was proposed, and it used texture complexity to determine the division direction and a lightweight neural network to determine the division mode. [PERSON] et al. [6] proposed a fast decision binary partition algorithm based on Bayesian rules by utilizing the correlation between the current CU and the sub-CU after horizontal binary partition, and it used the correlation between the current CU and the sub-CU after horizontal binary segmentation to terminate the vertical binary segmentation. In [7], a deep learning method was proposed to predict the CU partition, and a multi-stage exit CNN model was also proposed for fast CU partition in multiple stages while using an early exit mechanism to skip redundant CUs. [PERSON] et al. [8] divided a CTU into four parts to train the neural network and to calculate the probability of various partition modes in the CTU. Once the probability of the partition mode exceeded the threshold, it was skipped to achieve complexity reduction. In [9], the proposed algorithm was formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers. In [10], a random forest classifier was used to make an early decision about the partition mode by utilizing the texture features of the image and the intra-frame prediction process was optimized based on the image content. [PERSON] et al. [11] transformed the QTMT partition process into multiple binary classification problems for each decision layer, which were processed by a decision tree classifier. Although this method saved coding time, the BDBR (Bjontegaard Delta Bit Rate) losses were high. In [12], a hierarchical  
Figure 1: Example of the CU partition structure in VVC:(**a**) CU division results, ad (**b**) corresponding tree structure. grid fully convolutional network (HG-FCN) framework was proposed to predict a specific level of partition structure, achieving a certain degree of complexity reduction. [PERSON] et al. [13] used a classifier to make early decisions about the DC mode and the Planar mode during the prediction process, and they used the pixel variances of the sub-blocks to decide whether to use intra-frame sub-block partition technology. In [14], a convolutional neural network was trained to predict partition modes by training a probability vector. In [15], a neural network model was trained using cross-entropy, and it was used to terminate the partitioning process early. In [16], a fast CU-partitioning decision algorithm based on texture complexity and convolutional neural networks (CNN) was used, and this algorithm used symmetric convolution kernels to extract features and redesign the loss function. In [17], a novel feature based on Statistical Oriented Gradient (SOG) was proposed to extract the feature information of a coding block, and it used SOG to speed up the intra prediction mode decision process. In [18], a fast QTMT partition algorithm based on a CNN-binary tree horizontal (CNN-BTH) network was developed to predict the BTH mode decision at 32 \(\times\) 32 coding units (CUs). The BTV decision tree algorithm was also predicted at this level by a CNN-binary tree vertical (CNN-BTV). For coding content-based methods, in [19], a latitude-based preprocessing was introduced for the early termination of the coding unit (CU) partition in the polar region. In [20], the factor of an average background luminance for just-noticeable-distortion was applied to identify the visually distinguishable (VD) pixels within a coding unit (CU). [PERSON] et al. [21] proposed a fast partition scheme based on adjacent sub-regions, and it skipped unnecessary partition modes in advance based on the similarity of the adjacent sub-regions in the horizontal and vertical directions.