Blind image quality evaluation using perception based features

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SRFlow: Learning the Super-Resolution Space with Normalizing Flow (2020, neutral): “Furthermore, we report the noreference metrics NIQE [33], BRISQUE [32] and PIQUE [35].”
Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “Given the input data X ∈ R h × w × c , where c is the number of input channels, h and w are the height and width of input data respectively.”
Real-World Single Image Super-Resolution: A Brief Review (2021, methodology): “Table II reports commonly used metrics for evaluating the objective quality of super-resolved images, including PSNR, SSIM [135], IFC [136], LPIPS [137], NIQE [138], PIQE [139], and NRQM [116].”
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results (2020, methodology): “In particular, we report the NIQE [52], BRISQUE [51] and PIQE [53], using their corresponding MATLAB implementations.”
Real-World Super-Resolution via Kernel Estimation and Noise Injection (2020, methodology): “The NIQE [29], BRISQUE [28], and PIQE [37] metric is calculated using their corresponding MATLAB implementations.”
MagFace: A Universal Representation for Face Recognition and Quality Assessment (2021, methodology): “Brisque [31], Niqe [23] and Piqe [37] are image-based quality metrics.”