Quality Assessment Without Reference Images Based on Convolution Neural Network and Deep Forest

Table 1. Standard databases of image quality assessment
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Table 1. Standard databases of image quality assessment
Imagedatabase Number ofreferenceimages Number ofdistortedimages Number ofdistortiontypes Numberof peopletested
LIVE 29 779 5 161
TID2008 25 1700 17 838

Table 2. Comparison of SROCC experimental results on LIVE database

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Table 2. Comparison of SROCC experimental results on LIVE database

Model	JP2K	JPEG	WN	BLUR	FF	ALL
DIIVINE^[6]	0.913	0.910	0.984	0.921	0.863	0.916
BLIINDS-II^[20]	0.929	0.942	0.969	0.923	0.889	0.931
BRISQUE^[7]	0.914	0.965	0.979	0.951	0.877	0.940
CORNIA^[21]	0.943	0.955	0.976	0.969	0.906	0.942
CNN^[11]	0.952	0.977	0.978	0.962	0.908	0.956
Proposed	0.967	0.974	0.983	0.971	0.938	0.972

Table 3. Comparison of LCC experimental results on LIVE database

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Table 3. Comparison of LCC experimental results on LIVE database

Model	JP2K	JPEG	WN	BLUR	FF	ALL
DIIVINE^[6]	0.922	0.921	0.988	0.923	0.888	0.917
BLIINDS-II^[20]	0.935	0.968	0.980	0.938	0.896	0.930
BRISQUE^[7]	0.922	0.973	0.985	0.951	0.903	0.942
CORNIA^[21]	0.951	0.965	0.987	0.968	0.917	0.935
CNN^[11]	0.953	0.981	0.984	0.953	0.933	0.953
Proposed	0.971	0.979	0.983	0.985	0.944	0.975

Table 4. Comparison of SROCC and PLCC experimental results on TID2008 database
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Table 4. Comparison of SROCC and PLCC experimental results on TID2008 database
Model SROCC LCC
CORNIA^[21] 0.890 0.880
BRISQUE^[7] 0.882 0.892
CNN^[11] 0.920 0.903
Proposed 0.935 0.929

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Yindong Chen, Chaofeng Li, Qingbing Sang. Quality Assessment Without Reference Images Based on Convolution Neural Network and Deep Forest[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111003

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