/Contrast-quality-assessment-using-deep-learning

In this work we describe a Convolutional Neural Network (CNN) to predict image contrast quality based on human visual systems (HVS) values and without a reference image. The network consists of four convolutional layer with max pooling, one fully connected layers and the output which is considered as the quality assessment metric after being normalized to a score between 0 and 1 via Sigmoid function. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality based on MOS. Three different correlation also performs between the predicted values from CNN to the MOS values to find out the relation. This approach achieves state of the art performance on the CEED2016 dataset .

Primary LanguageMATLAB

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