# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 1 Introduction  
To transport huge volumes of video data effectively, some new versions of flexible video standards like H.264 [1], H.265 [2], and H.266 [3] are continually being developed. Each of these standards provides many efficient coding methods, greatly increasing the code complexity. For example, to obtain the best CU, versatile video coding (VVC) uses highly adaptable block coding (i.e., quad tree-based merge and transform (QTMT) structure), increasing the intra-frame coding complexity by an average of 18 times compared to HEVC [4]. It will become considerably more difficult if the rate distortion optimization (RDO) is also used. The VVC standard's practical application is constrained by its high level of complexity, particularly in real-time settings. Given that both VVC and HECV use block-based encoding constructions with some variations in specific technologies as well as the fact that deep learning-based accelerating methods typically require the power of a graphics processing unit (GPU), HEVC is more developed and widely used than VVC in this regard. Thus, to estimate the performances of the various methods and validate the efficacy of the suggested algorithm, this work utilized HEVC without losing its universality. Plenty of approaches to simplify HEVC coding have been developed during the past 10 years. It has been demonstrated that the standard reference software HM's recursive CU partition procedure consumes up to 80% of the intra-frame encoding time. Therefore, reducing the complexity of quad-tree CU partitioning without compromising coding efficiency is a much-anticipated problem in the field of video compression. Currently, complexity reduction methods are mainly categorized into heuristics and learning-basedmethods, and there are also individual papers that have proposed other ways to reduce the coding time, like in [5; 6]. The RDO search for CU partitions can be streamlined using several middle characteristics in heuristic strategies [7; 8; 9; 10; 11; 12; 13; 14; 15]. The approach by [PERSON] et al. [7] circumvented the large CU size of the close-by coding tree unit (CTU) and offered an early CU size determination method based on the texture homogeneity measured by the mean absolute distortion. Quantization parameters (QPs), RD cost distributions, and video content were used to update an adaptive early termination threshold in [PERSON] et al. 's [8] statistical modeling-based CU partition technique. [PERSON] et al. [9] developed a quick joint approach to address the issue of high complexity when the PU crosses the RDO. In particular, it performs jump and split termination judgments on prediction units (PUs) using Bayes' rule and freshly set decision parameters, avoiding RD processing. [PERSON] et al. [10] originally used a class conditional probability density function using one-two essential features and prior experience to identify whether or not the CU is divided. They then developed a Bayesian threshold method to ensure that the loss of the large coding unit (LCU) partition was kept to a minimum. The technique is limited in its ability to encode various scenarios because it lacks statistical features that are simple to compute. [PERSON] et al. [11] overcame the constraints of [10] by presenting a statistical-based model solution to the high-complexity of the CU partition and PU mode decision-making process. Using statistical qualities, the method extracted as many key features of the image as possible, minimizing the coding time to a greater extent. Learning-based approaches have gradually replaced traditional heuristics to extract more features and lower the complexity of the CU partition in today's environment, where artificial intelligence technologies are constantly encouraged [16; 17; 18; 19; 20].