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Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network
2016
Pattern Recognition
In this paper, we propose to learn the structures of stereoscopic image based on convolutional neural network (CNN) for no-reference quality assessment. Taking image patches from the stereoscopic images as inputs, the proposed CNN can learn the local structures which are sensitive to human perception and representative for perceptual quality evaluation. By stacking multiple convolution and max-pooling layers together, the learned structures in lower convolution layers can be composed and
doi:10.1016/j.patcog.2016.01.034
fatcat:cend7opm4bempp2vbqpocy33qu