IQA图像质量评估

* 传统方法图像质量评估
* 深度学习图像质量评估


The ‘good-ness’ of any algorithm is gauged by measuring the correlation of algorithmic scores with subjective (differential) mean opinion scores (DMOS/MOS) on a large dataset spanning different distortion.

正如以上所说对于一种图像质量评价算法性能好坏的评估就是看在具有不同失真的大数据集上观察者的主观评分和算法评分的相关度。如果它们的相关度较高，则说明该质量评价算法的性能较好，否则反之。

VIF：

H. R. Sheikh, A. C. Bovik, and G. de Veciana. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14(12):2117–2128, Dec. 2005.

FSIM：

L. Zhang, D. Zhang, X. Mou, and D. Zhang. FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8):2378–2386, 2011.

CORNIA

CNN-NR-IQA