# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Analyzing Experimental Outcomes
#### 3.3.3 Comparison of the Proposed Algorithm with Three Existing Algorithms  
We compared the proposed method to the competing algorithms including the heuristic-based scheme by [PERSON] et al. [11], the CNN-based algorithm by [PERSON] et al. [21], and the CNN-based scheme by [PERSON] et al. [28] to further illustrate its superior performance, as shown in Table 5. The meanings of BD-BR (%), BD-PSNR (dB), and \(\Delta T_{avg}\) (%) were the same as noted above. On the one hand, for the coding complexity, we noted that the proposed scheme was reduced on average by 64.05%, better than both the 59.69% in [21] and 60.35% in [28], and even about 10% higher compared to [11]. The method has such a low complexity because it can extract all features of the current CTU pixels from both the global information and refined texture features, while the encoding process does not require any intermediate features, and the CNN-CBAM can predict all of the CU partition results at one time, skipping more encoding unit depth computation processes, and thus effectively removing redundant CU partition computations. In addition, as above-mentioned, for some regions, especially for the regions with gentle movement and simple textures, the CU partition of the CNN-CBAM better reflected the subjective feelings than the method in HM 16.5. For these regions, the CNN-CBAM terminated the CU partition early, which further reduced the intra encoding time. On the other hand, for the RD performance, all four methods in Table 5 showed a slight loss compared with the method in HM16.5; the proposed method was superior to the methods in [11; 21; 28] both in terms of the code rate and video quality with 2.18% and \(-\)0.11 dB, 2.71% and \(-\)0.13 dB, and 2.03% and \(-\)0.09 dB, respectively. Our method had a better generalization ability compared with competing algorithms. Meanwhile, the CBAM algorithm could better identify the features of the current block and induce the CNN to focus on the target object correctly, thus improving the network prediction performance. In summary, compared to heuristic-based methods [11], the CNN-CBAM based scheme excels in feature extraction and adaptively adjusts to scene characteristics, optimizing the network structure. In contrast to CNN-based schemes [21; 28], the distinctive convolutional block design in CNN-CBAM strengthens the interaction of information between blocks, leading to the acquisition of more abundant texture features. Moreover, the incorporation of CBAM enhances image quality without incurring additional computational complexity. It is undeniable that our method has an advantage over competing methods. In Figure 11, the results of the comparison between the other three algorithms and the proposed algorithm are presented in the form of three-dimensional visualization graphs, which vividly show the differences between the four algorithms on the three metrics. Note that the values of BD-PSNR (dB), \(\Delta T_{all}\) (%), and BD-BR (%) were the average of all 18 test sequences. Undeniably, the proposed algorithm is more advantageous in terms of the overall criteria for assessment compared with the three methods in [11; 21; 28]. On the one hand, the proposed method has significant advantages in reducing the encoding complexity since the proposed CNN-CBAM can classify blocks more comprehensively and faster, and consequently reduce many RDO candidates. On the other hand, the feature refinement process of the CBAM enables the CNN-CBAM to effectively utilize the given features, which enhances the neural network's capacity for generalization and maintains good coding performance. We further used a metric (i.e., figures of merit (_FoM_) mentioned in [37]) to more objectively show the complexity-RD performance of the proposed method.