DR2S : Deep Regression with Region Selection for Camera Quality Evaluation
This work addresses camera quality evaluation for photography and imaging applications, representing an incremental improvement over existing methods.
The paper tackles the problem of estimating camera texture preservation quality aligned with human perception by introducing a deep regression network with region selection. The experimental results show that this learning-based approach outperforms existing methods, with the region selection algorithm consistently improving quality estimation.
In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation.