# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results  
### Experimental Conditions  
**Experimental data:** The database for the CU partition of HEVC(CPH) [34] (2000 ultra-high definition (UHD) images were selected from the original image datasets) was used to test the network presented. First, these UHD images were randomly divided into validation, testing, and training, which was 5%, 10%, and 85% of the entire dataset, respectively. Generally speaking, the more the training data, the more accurate the predicted CTU partition. Second, each group contains four subsets equally (i.e., 768 \(\times\) 512, 1536 \(\times\) 1024, 4928 \(\times\) 3264, and 2880 \(\times\) 1920). Finally, the CPH video sequences are encoded with HM16.5 to different QPs to obtain the containing CU partition after training, testing, and validation of the data labels.  
In addition, all trainable parameters are randomly initialized when training the model. The specific hyper parameter settings are shown in Table 2.  
**Standard experimental configuration**: In this study, to encode the standard video test sequence, four QP values [22; 27; 32; 37] and encoder_intra_main.cfg [35] were chosen using the Benchmark software HM16.5 as the experimental platform. Table 3 shows the specific experimental conditions.  
**Objective evaluation indicators**: Validation studies were carried out on 18 reference sequences with varied resolutions provided by JCT-VC in this publication for fair comparison. Simultaneously, to evaluate the overall effectiveness of the suggested strategy, we settled on four criteria (i.e., \(\Delta T\), \(\Delta T_{avg}\), BD-PSNR [36], and BD-BR). The specific calculation formulas are as follows:  
\[\Delta T=\frac{T_{proposed}-T_{HM16.5}}{T_{HM16.5}}\times 100\%, \tag{8}\]  
\[\Delta T_{avg}=\frac{1}{4}\sum_{i=1}^{4}\frac{T_{proposed}(QP_{i})-T_{HM16.5}( QP_{i})}{T_{HM16.5}(QP_{i})}\times 100\%, \tag{9}\]  
\[BD-PSNR=\frac{1}{r_{h}-r_{l}}\int_{r_{l}}^{r_{h}}\Big{(}D_{proposed}(r)-D_{HM16.5 }(r)\Big{)}dr, \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper Parameters** & **Value** \\ \hline Model initialization & Random initialization \\ Distribution type & Truncated normal distribution \\ Mean & 0 \\ Standard deviation & 0.1 \\ Batch size (R) & 64 \\ Momentum & 0.9 \\ Initial learning rate & 0.01 \\ Learning rate decay & Decrease by 1\% every 2000 iterations \\ Total iterations & 1,000,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific hyper parameter settings of CNN-CBAM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Model** \\ \hline Operating system & Window 11 \\ python & 3.6 \\ TensorFlow & 1.8 \\ Processor & AMD Ryzen 7 5800 H with Radeon Graphics @3.20 GHz \\ Running memory & 8.00 GB RAM \\ Graphics Card Adapter & NVIDIA GeForce GTX1650 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.  
\[BD-BR=\frac{1}{D_{h}-D_{l}}\int_{D_{l}}^{D_{h}}\Big{(}r_{proposed}-r_{HM16.5}\Big{)}dD, \tag{11}\]  
where \(T_{HM16.5}\) and \(T_{proposed}\) denote the coding time by HM16.5 and the proposed method, respectively; \(D_{proposed}\) and \(D_{HM16.5}\) are the two corresponding RD curves; \(r_{l}\) and \(r_{h}\) are the low and high ends of the output bit rate range, respectively; \(r_{HM16.5}\) and \(r_{proposed}\) are the corresponding bit rates of the original one in HM16.5 and the proposed method, respectively; \(D_{l}\) and \(D_{h}\) are the low and high ends of the output RD curve range, respectively.