Fusion of mSSIM and SVM for reduced-reference facial image quality assessment
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
KIMEL: A kernel incremental metalearning algorithm
Signal Processing
International Journal of Communication Networks and Distributed Systems
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We develop a no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN). The new algorithm is trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types. The features deployed for QA include the mean value of phase congruency image, the entropy of phase congruency image, the entropy of the distorted image, and the gradient of the distorted image. Image quality estimation is accomplished by approximating the functional relationship between these features and subjective mean opinion scores using a GRNN. Our experimental results show that the new method accords closely with human subjective judgment.