# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 4 Conclusions and Feature Works  
In this paper, we proposed an optimization algorithm for CNN based on the hybrid attention mechanism (i.e., CNN-CBAM), which aimed to accelerate CU partitioning in the HEVC. Firstly, we extracted the global information and local texture features of the video sequence using CNN. Then, we introduced CBAM to CNN to construct CNN-CBAM, which was then embedded into the coding framework of HECV to predict the CU partition in advance. CBAM mainly extracted the nonuniform fusion of the fetched multi-channel features, which in turn improved the information representation of the fused features. Finally, the experimental data showed that the CNN-CBAM could reduce the intra-frame coding time to 64.05%. Despite a slight performance loss in BD-PSNR and BD-BR (with a BD-PSNR and BD-BR of \(-\)0.09 dB and 1.94%, respectively), relatively good video quality was maintained. Although some findings have been made in this study, there is room for further refinement and expansion in the future. We can further optimize and refine the CNN-CBAM model by combining the rules of inter-frame coding. In addition, we will further explore how to apply the optimized CNN-CBAM to VVC. With such improvements, we expect to achieve better results and advance the technology in the field of video compression. In conclusion, the algorithm presented in this paper offers a promising solution for accelerating CU partition in the HEVC. However, future research endeavors should focus on refining and extending the algorithm to achieve even better results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Algorithm [11]** & **Algorithm [21]** & **Algorithm [28]** & **Proposed** \\ \hline FoM & 3.9089 & 4.5401 & 3.3637 & 3.0312 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the FoM of the four methods. Figure 11: Comparison of the coding performance (\(\Delta T_{all}\), BD-BR, and BD-PSNR) with other algorithms on standard test sequences [11; 21; 28]. **Author Contributions:** Conceptualization, H.L., G.W. and T.W. ; Methodology, H.L., G.W., T.W., Q.Z. and T.B. ; Software, H.L., G.W. and T.W. ; Validation, H.L., G.W., T.W. and R.W. ; Formal analysis, H.L., G.W. and T.W. ; Investigation, H.L., G.W. and T.W. ; Resources, H.L. ; Data curation, H.L. ; Writing--original draft preparation, H.L. and G.W. ; Writing--review and editing, H.L., G.W., T.W., Q.Z., T.B. and R.W. ; Visualization, H.L. and G.W. ; Supervision, G.W. ; Project administration, G.W. ; Funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Natural Science Foundation of Guangxi Province (Grants No. 2020 GXNSFAA297184, No. 2020 GXNSFBA297097), the National Natural Science Foundation of China (Grant. No. 62161031), and the Science and Technology Planning Project of Guangxi Province (Grant. No. AD21238038). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the finding of the study are available from Dr. [PERSON] at [EMAIL_ADDRESS] upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript.