SAMA is a simple yet powerful method that leverages the multi-granularity pyramid to extract content and reduce the dimension of the pyramid to a fixed size. By adopting this approach, SAMA can effectively balance local details and global perception in image quality assessment. To understand how SAMA works, let’s consider an analogy. Imagine you have a large library with various books of different sizes. Each book represents a different scale or resolution of the image. Now, imagine that you want to find the best book (i.e., the most accurate prediction) that represents the overall quality of the image. SAMA helps you find this book by first organizing the books into a pyramid based on their sizes. Then, it samples fragments from each book and feeds them into an autoencoder to learn the representation of the overall quality.
The key insight behind SAMA is that the quality of an image can be represented at multiple scales or resolutions. By leveraging this property, SAMA can capture both local details and global perception simultaneously, leading to a more accurate prediction of image quality.
Complexity and Transferability
One potential challenge with SAMA is the trade-off between computational complexity and transferability. In other words, increasing the model’s capacity to handle more complex tasks may lead to a quadratic growth in computational complexity, which could be challenging for deployment on resource-constrained devices. To address this issue, researchers proposed a multi-branch scheme that transforms the input data into a multi-scale image representation and feeds them all into the model. However, this approach introduces extra model complexity and may not be computationally efficient.
To overcome these challenges, SAMA adopts an elegant solution by leveraging the multi-granularity pyramid to extract content and reduce the dimension of the pyramid to a fixed size. By doing so, SAMA can balance local details and global perception without introducing excessive computational complexity.
Conclusion
In conclusion, SAMA is an elegant solution that balances local details and global perception in image quality assessment by leveraging the multi-granularity pyramid. By organizing fragments into a pyramid based on their sizes, SAMA can capture both local details and global perception simultaneously, leading to a more accurate prediction of image quality. Although there are potential challenges with SAMA, its simplicity and effectiveness make it an exciting area of research in the field of image quality assessment.
