No-Reference Stereoscopic Image Quality Assessment Guided by a Disparity Map

Given the importance of the disparity map in stereoscopic imaging,a two-channel convolution neural network(CNN)was designed to evaluate the quality of stereoscopic imaging without reference.Firstly,a CNN structure with a dense connection network as the main body was established for feature extraction.Secondly,a column of CNN input came from the cyclopean image,which was based on the characteristics of binocular combination and rivalry of the human visual system.Left and right views were fused into three channels to get the color cyclopean image.This image was used as the input of one channel of the CNN.The input of other channel was the disparity map,which provided some compensatory information for the cyclopean image.More importantly,we employed the features of the disparity map to guide and weigh the feature maps obtained from the cyclopean image,which were implemented by modifying the structure of the squeeze and excitation(SE)block.This weighting strategy strengthened the transmission of important information,and reduced the transmission of non-significant information from the cyclopean image.Finally,we combined the outputs from the two columns and processed them to get the final quality score of the stereoscopic image at the end of the CNN.The experiment was carried out on two open LIVE stereoscopic databases.Experimental results demonstrated that the proposed method could achieve highly consistent alignment with the subjective assessment. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.