# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 1 Introduction
To achieve more efficient results, we used machine learning (ML) methods such as support vector machines (SVMs) [16; 17; 18], random forests (RFs) [19], or Bayesian decision rule [20] classifiers (not the block similarity metrics), which are used in statistical-based methods to simplify the complexity of the CU decision module. Unlike the ML approaches, the CNN methods have been proven to be effective in automatic feature extraction [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. CNN-based approaches, in comparison to heuristic-based methods, exhibit superior generalization ability and automatic feature learning. They can be effectively employed in various video coding and decoding scenarios, thereby enhancing the universality of the proposed algorithm. Consequently, CNN-based methods are increasingly gaining attention and finding diverse applications in the field of video coding and decoding. Although the complexity can be greatly reduced using both heuristic and learning-based approaches, heuristic methods often struggle to extract sufficient features from video content, and the learning-based methods lead to a significant increase in complexity at deeper CTU depths. Therefore, when designing the network structure, not only must sufficient features be extracted, but the encoding time must be considerably reduced within the acceptable range of model calculation. In this paper, we propose a fast partition algorithm in CU based on machine learning and heuristic methods. Firstly, a CNN that can quickly predict all CU partition results was proposed to adapt to three CU sizes of 16 \(\times\) 16, 32 \(\times\) 32, and 64 \(\times\) 64. In addition, the convolution blocks were designed to enhance the connection between the blocks in the intra-frame space. Secondly, a CBAM was introduced into the proposed CNN to construct CNN-CBAM with better performance, consequently refining the feature map while improving the network performance. Finally, the experimental data revealed that the CNN-CBAM significantly reduced the intra-frame coding time while somewhat degrading the RD performance. To conclude, our primary contribution is in the three categories listed below:  
* We propose a CNN for variable CU sizes to properly fetch the global information of the current CTU. The developed CNN can accurately anticipate the size of the HEVC coding units, resulting in a reduced intra-frame coding time. * Embedding a CBAM into CNN leads to the development of the first CNN-CBAM, which is capable of predicting CU partitions more accurately in HEVC, so that it can obtain the key information of the different sequences. * The CNN-CBAM is embedded in the HEVC coding framework to predict the CU partition in advance. This method can avoid the repeated iterations of traditional RDO to extract the optimal CU depth, therefore, it can remarkably decrease the coding complexity while sustaining good coding performance. The rest of this paper is organized as follows. Section 2 describes the proposed CNN-CBAM algorithm in detail. Section 3 reports the experimental results. Section 4 summarizes the paper and discusses some future work.