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The Kernel method is a powerful tool for extending an algorithm from linear to nonlinear case. Metalearning algorithm learns the base learning algorithm, thus to improve performance of the learning system. Usually, metalearning algorithms exhibit faster convergence rate and lower Mean-Square Error (MSE) than the corresponding base learning algorithms. In this paper, we present a kernelized metalearning...
Full reference image quality assessment (FR-IQA) algorithms aim to establish generic measures of perceptual image quality independent of distortion types. Recent developments in FR-IQA have marked the use of phase congruency features. Phase congruency is a dimensionless, normalized feature calculated from the log-Gabor energy of the image, equipped to be relatively insensitive to noise variations...
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