# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 4 Experimental Results
### Mode Prediction Accuracy Evaluation  
To evaluate the accuracy of our mode selection method, we compared the decisions made with Tsutake [10] with the decision made by the proposed method. We computed how often each affine transform model was used and how accurately the proposed method estimated the correct model. We consider the correct model the one that was used in the final coding, so if a given model was found better than the translation of the full block during the motion estimation phase but was inferior to a split block with different translation, skipping affine is classified as correct choice. We also evaluated the accuracy of the early skipping based on the variance that skips evaluating affine prediction entirely. The results are shown in Table 2.  
Sensitivity represents how often the proposed method predicted this model correctly compared to how often this model was the best when evaluating both. It is calculated with the following formula:  
\[\text{sensitivity}_{model}=\frac{\text{TP}_{model}}{\text{TP}_{model}+\text{FN}_ {model}} \tag{15}\]  
where \(\text{TP}_{model}\) is the true positives for a given model (prediction said to use the model and the model was used), and \(\text{FN}_{model}\) the false negatives (prediction said to use the other model or to skip while this model was correct). The best block rate is the percentage of encoded blocks that use that model. The correct model represents how often the proposed method chose the right affine prediction model. It is the weighed average between the sensitivity values for both models, weighed by the prevalence of each model. The skip statistics represent how often the proposed method decided to skip evaluating affine parameters wrongly, and the rate of missed opportunities for skipping affine prediction. While the accuracy appears to be low for many cases, the accuracy is not weighed with the loss of coding efficiency. While one could measure the efficiency gains estimated for a single block, it is not perfect, as the state of the entropy coder influences the coding of the following blocks. However, \begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence Name** & **Motion** \\ \hline Twilight Scene (s215) & Zoom \\ Rotating Disk (s251) & Rotation \\ Fountain (s265) & Rotation \\ Station & Zoom \\ Blue Sky & Rotation \\ Fungus Zoom & Zoom \\ Tractor & Rotation + Zoom \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Video Sequences. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence Name**} & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Best Block Rate**} & \multicolumn{2}{c}{**Correct**} & \multicolumn{2}{c}{**Affine Skip**} \\ \cline{2-7}  & **Rotation[\%]** & **Zoom[\%]** & **Rotation[\%]** & **Zoom[\%]** & **Model[\%]** & **Bad[\%]** & **Missed[\%]** \\ \hline Station & 14.6 & 90.8 & 0.5 & 33.4 & 89.7 & 3.3 & 70.2 \\ Fountain & 71.2 & 8.0 & 2.0 & 0.4 & 59.8 & 35.3 & 59.8 \\ Fungus Zoom & 0.0 & 99.2 & 0.3 & 35.6 & 98.4 & 0.0 & 100.0 \\ Rotating Disk & 86.9 & 30.9 & 21.6 & 1.3 & 83.9 & 4.5 & 89.7 \\ Blue Sky & 92.8 & 20.4 & 10.2 & 0.7 & 88.2 & 4.5 & 93.6 \\ Tractor & 27.4 & 58.6 & 0.9 & 16.7 & 57.0 & 29.3 & 48.6 \\ Twilight Scene & 30.8 & 38.6 & 0.8 & 1.2 & 35.5 & 34.4 & 83.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of mode prediction accuracy of the proposed method compared to Reference [10]. according to our results detailed in the following subsection, the blocks that were predicted incorrectly offered little benefit. On some sequences where a type of motion is very dominant, like Fungus Zoom where zooming is used much more than rotation, the encoder will often predict the most common model even when it is not the best. This leads to a very low sensitivity for this model, but has a limited effect on the encoding efficiency since that model is not used much. This can be seen with the correct model value that is very high in this case.