论文标题
DR2：深度回归，可进行相机质量评估的区域选择
DR2S : Deep Regression with Region Selection for Camera Quality Evaluation
论文作者
论文摘要
在这项工作中，我们解决了在给定照明条件下估算摄像机能力以保持精细纹理细节的问题。重要的是，我们的质地保存测量应与人类的感知相吻合。因此，我们将问题提出为回归，并引入了深度卷积网络以估计纹理质量得分。在培训时，我们使用专家人类注释者提供的基础真相质量分数来获得主观质量措施。此外，我们提出了一种区域选择方法，以识别更适合衡量感知质量的图像区域。最后，我们的实验评估表明，我们基于学习的方法优于现有方法，并且我们的区域选择算法始终提高质量估计。
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.
