# DR2S : Deep Regression with Region Selection for Camera Quality Evaluation

arXiv: https://arxiv.org/abs/2009.09981 · HTML: https://mlsanity.com/paper/2009.09981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Test your samples jointly: Pseudo-reference for image quality evaluation](https://mlsanity.com/paper/2304.03766/) (2023)

## Abstract (arXiv)

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.