# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 5 Results of Experiments and Discussion
### Quantitative Evaluation  
To evaluate the image quality of the generated drawing sequences, we conducted quantitative comparisons using three widely adopted metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image-Patch Similarity (LPIPS). These metrics were computed frame by frame over the entire predicted sequences, and the average scores across all test samples are plotted in Figures 9-11. Figure 8: Failed generation result from training with noise-augmented images. Figure 7: Sequential generation result from training with noise-augmented images. Figure 9: Frame-wise PSNR comparison between our proposed methods and PredRNN. As shown in the figures, our proposed model with noise-augmented training consistently outperformed standard training across all metrics, confirming that adding noise during training improves the stability and visual quality of sequential generation. In comparison to PredRNN, a strong recurrent baseline, our model performed competitively in early frames but exhibited greater degradation in later frames. PredRNN achieved the highest overall performance, particularly in preserving spatial and perceptual consistency, as indicated by its superior SSIM and LPIPS scores across the sequence. For example, PredRNN achieved an average PSNR of 24.05, SSIM of 0.88, and LPIPS of 0.08, while our noise-augmented model achieved 20.48, 0.81, and 0.13, respectively. Additionally, we conducted a case-level analysis comparing the final frames of the generated sequences for 10 samples. The results, shown in Table 3, indicate that the noise-augmented model outperformed PredRNN in 3 out of 10 cases across all metrics (PSNR, SSIM, and LPIPS). This highlights that although PredRNN performed better on average, the proposed method can achieve superior results in specific sequences, suggesting its robustness and potential advantage in certain drawing styles or structures. Figure 11: Frame-wise LPIPS comparison between our proposed methods and PredRNN. Figure 10: Frame-wise SSIM comparison between our proposed methods and PredRNN. Nevertheless, our model maintains advantages in terms of computational simplicity and training efficiency. These trade-offs are valuable for lightweight or real-time applications where latency and model size are critical factors. This comparison suggests that while temporal architectures such as PredRNN are more effective in long-term consistency, the proposed encoder-decoder framework still provides reasonable accuracy under constrained conditions. To further compare the computational efficiency, we measured the number of trainable parameters and inference time for generating one sequence (five images per character). PredRNN had approximately 6.41 million trainable parameters and an average inference time of 0.188 s per sequence, whereas our proposed model had 5.20 million parameters and achieved a significantly faster inference time of 0.005 s. This supports the suitability of our model for lightweight or real-time applications where responsiveness is critical. To complement frame-level metrics such as PSNR, SSIM, and LPIPS, we also calculated the Frechet Inception Distance (FID) using the set of final generated images. FID evaluates the overall distributional similarity between the generated and ground-truth images.