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Blind Image Quality Assessment Using a General Regression Neural Network

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3 Author(s)
Chaofeng Li ; Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), the School of IoT Engineering, Jiangnan University, Wuxi, Jiangsu, China ; Alan Conrad Bovik ; Xiaojun Wu

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:22 ,  Issue: 5 )