Quality evaluation of screen content image based on neural network
With the rapid development of computer mobile network technology,it is particularly important to realize the distribution and processing of screen content images to achieve information sharing.The method of screen content image quality evaluation based on deep learning Convolution neural network(CNN)is proposed.The human visual system has different concerns for the text area and the image area,so it cannot judge the quality of the image from a single feature.The purpose of multi-feature extraction can be achieved by designing a deeper convolution neural network.The model in this paper first uses the Full convolution network(FCN)to divide the screen content image into text area and image area,then uses the deep level CNN to evaluate the quality of the text area and image area respectively,and finally integrates the scores through the fusion strategy to get the overall quality score of the image.The model in this paper allows the joint learning of local quality and local weight,driven by data,and does not rely on the knowledge of manual features and image statistics.Experiments on two public data sets show that the proposed algorithm achieves higher consistency in subjective perception than the current methods.
NETL
NSTL
万方数据
维普
