Table 1 Explicit NR-IQA algorithm performance comparison
From: Explicit-implicit dual stream network for image quality assessment
Algorithm | Parameters | No. of | Indies on LIVE | Â | Training | Year |
|---|---|---|---|---|---|---|
| Â | used | parameters | SROCC | PLCC | method | Â |
BIQI | GGD(α,θ2) | 18 | 0.8271 | 0.8417 | SVM | 2010 |
DIIVINE | GGD(α,θ2) | 88 | 0.9160 | 0.9170 | SVM | 2011 |
BLIINDS-II | DCT | 16 | 0.9124 | 0.9164 | Model | 2012 |
BRISQUE | \(GGD\left (\alpha,\theta ^{2}\right),AGGD\left (\eta,\nu,\theta _{l}^{2},\theta _{r}^{2}\right)\) | 36 | 0.9395 | 0.9424 | SVM | 2012 |
DESIQUE | \(GGD\left (\alpha _{i},\theta _{i}^{2}\right)\) | 60 | 0.9437 | 0.9465 | SVM | 2013 |
NIQE | \(GGD\left (\alpha,\theta ^{2}\right),AGGD\left (\eta,\nu,\theta _{l}^{2},\theta _{r}^{2}\right)\) | 36 | 0.9153 | 0.9147 | Model | 2013 |
SSEQ | entropy | 12 | 0.9348 | 0.9383 | SVM | 2014 |
IL-NIQE | \(GGD\left (\alpha,\theta ^{2}\right),AGGD\left (\eta,\nu,\theta _{l}^{2},\theta _{r}^{2}\right)\) | 468 | 0.9025 | 0.9085 | Model | 2015 |