Table 2 Classification of IQA models... | Emerald Publishing

Table 2

Classification of IQA models based on reference availability and assessment methodology.

IQA model	Reference availability	Assessment methodology	Remarks (strength and weakness)
PSNR	FR	Image fidelity	• Simple • Low correlation
NQM	FR	HVS model	• Better measureofadditive noisethanPSNR • 80% correlation to visual results
PSNR-HVS-M	FR	HVS model	• Incorporate CSF model • 98% correlation with subjective scores
VSNR	FR	HVS model	• Low computational complexity and memory requirements • Accommodate different viewing conditions • 88.9% correlation with subjective scores in LIVE database
SSIM	FR	Signal structure	• Easy to implement • Good correlation with subjective scores
MS-SSIM	FR	Signal structure	• Incorporate image details at different resolutions • Better correlation with subjective scores than SSIM
IFC	FR	Signal structure	• Use mutual information to quantify signal fidelity • Better correlation with subjective scores than SSIM
VIF	FR	Signal structure	• Use mutual information to quantify signal fidelity • Better correlation with subjective scores than IFC
RRIQA	RR	Signal structure	• Better performance than PSNR
MGA-based IQA	RR	Signal structure	• Good consistency with subjective scores • Low data rate to represent features
JNBM	NR	HVS model	• Can predict the relative amount of sharpness/blurriness in images
MREBN	NR	Signal structure	• Goodcorrelationwithsubjectivescores • Low computation load
FSIM	FR	Signal structure	• Use low-level features • Very good correlation with subjective scores
MAD	FR	HVS model	• Combine two different strategies to predict visual quality • Good correlation with subjective scores
MMF	FR	Learning-oriented	• Use machine learning to automatically fuse the scores from multiple quality metrics • Very high correlation with subjective scores • Can incorporate new IQA metrics

IQA model	Reference availability	Assessment methodology	Remarks (strength and weakness)
PSNR	FR	Image fidelity	• Simple • Low correlation
NQM	FR	HVS model	• Better measureofadditive noisethanPSNR • 80% correlation to visual results
PSNR-HVS-M	FR	HVS model	• Incorporate CSF model • 98% correlation with subjective scores
VSNR	FR	HVS model	• Low computational complexity and memory requirements • Accommodate different viewing conditions • 88.9% correlation with subjective scores in LIVE database
SSIM	FR	Signal structure	• Easy to implement • Good correlation with subjective scores
MS-SSIM	FR	Signal structure	• Incorporate image details at different resolutions • Better correlation with subjective scores than SSIM
IFC	FR	Signal structure	• Use mutual information to quantify signal fidelity • Better correlation with subjective scores than SSIM
VIF	FR	Signal structure	• Use mutual information to quantify signal fidelity • Better correlation with subjective scores than IFC
RRIQA	RR	Signal structure	• Better performance than PSNR
MGA-based IQA	RR	Signal structure	• Good consistency with subjective scores • Low data rate to represent features
JNBM	NR	HVS model	• Can predict the relative amount of sharpness/blurriness in images
MREBN	NR	Signal structure	• Goodcorrelationwithsubjectivescores • Low computation load
FSIM	FR	Signal structure	• Use low-level features • Very good correlation with subjective scores
MAD	FR	HVS model	• Combine two different strategies to predict visual quality • Good correlation with subjective scores
MMF	FR	Learning-oriented	• Use machine learning to automatically fuse the scores from multiple quality metrics • Very high correlation with subjective scores • Can incorporate new IQA metrics