Blind image quality assessment via semi-supervised learning and fuzzy inference

Applied Informatics

Table 2 Median LCC across 1000 train-test on the LIVE database II

Metric	Type	JP2K	JPEG	WN	Gblur	FF	Entire database
PSNR	FR	0.8962	0.8596	0.9858	0.7834	0.8895	0.8240
SSIM	FR	0.9367	0.9283	0.9695	0.8740	0.9428	0.8634
IFC	FR	0.9027	0.9047	0.9581	0.9608	0.9614	0.9106
VIF	FR	0.9615	0.9430	0.9839	0.9744	0.9618	0.9501
NSS	Blind	0.9210	0.3661	0.8217	0.7007	0.7224	0.4946
BIQI	Blind	0.8086	0.9011	0.9538	0.8293	0.7328	0.8205
DIIVINE	Blind	0.9220	0.9210	0.9880	0.9230	0.8880	0.9170
BLIINDS-II	Blind	0.9630	0.9793	0.9854	0.9481	0.9436	0.9232
SRNSS	Blind	0.9359	0.9391	0.9404	0.9356	0.9473	0.9318
BRISQUE	Blind	0.9229	0.9734	0.9851	0.9506	0.9093	0.9424
NIQE	Blind	0.9370	0.9564	0.9773	0.9525	0.9128	0.9147
CBIQ	Blind	0.912	0.963	0.959	0.918	0.885	0.896
S²F²-I	Blind	0.9578	0.9489	0.9668	0.9556	0.9370	0.9464