In this article, we propose a novel method to enhance image refinement in video retargeting by incorporating a source image attention mechanism. This mechanism allows the network to focus on the most relevant parts of the source image when generating the output frame, resulting in improved quality compared to traditional methods. We evaluate our approach using various metrics and demonstrate its effectiveness through visual results.
Introduction
Image refinement is an essential step in video retargeting, as it improves the overall quality of the output frames. Our proposed method extends previous work by adding functionalities to the pipeline and refinement network. This extended network, called generator G, includes a source image attention mechanism that helps the network focus on the most relevant parts of the input image.
Source Image Attention Mechanism
The attention mechanism is based on the idea of computing a weighted sum of the source image features, where the weights are learned during training. This allows the network to selectively focus on different regions of the source image depending on their relevance to the output frame. We use a multi-layer perceptron (MLP) to compute the attention weights and combine them with the feature maps from the generator network.
Results
We evaluate our method using various metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual quality score (VQS). Our results show that our proposed method outperforms traditional methods in terms of image refinement, with improved PSNR and SSIM values. We also provide visual results to demonstrate the effectiveness of our approach.
Conclusion
In conclusion, we proposed a novel method for improving image refinement in video retargeting by incorporating a source image attention mechanism. Our proposed method enhances the quality of the output frames by selectively focusing on the most relevant parts of the input image. The experimental results demonstrate the effectiveness of our approach and its potential to improve the overall quality of video retargeting systems.
