Research On No-reference 3D Image Quality Assessment Via Combined Model

Currently,stereoscopic 3D image has been widely applied in many fields.However,it may suffer from various quality degradations during the acquisition and transmission.Therefore,an effective 3D image quality assessment(IQA)method has great significance for 3D multimedia applications and 3D technique.In this paper,we have designed a no-reference quality assessment algorithm for 3D images by utilizing combined model.First,in order to extract the distorted information in different frequency,the Gabor filter bank is employed to decompose the 3D image pair.Second,the “Cyclopean” and difference maps,representing for binocular characteristic and asymmetric information,are generated from the Gabor filter results.Then,the statistical characteristics of “Cyclopean” and difference maps are estimated by utilizing the generalized Gaussian distribution(GGD)fitting.Finally,a SVR regression is learned to map the feature vector to the recorded subjective difference mean opinion scores(DMOS).Besides,we also make an attempt to utilize structural similarity index(SSIM)to measure the asymmetric information of 3D image pair.The performance of our algorithm is evaluated on the popular 3D IQA databases.Extensive results show that the proposed algorithm outperforms state-of-the-art noreference 3D IQA algorithms and is comparable to some full-reference 3D IQA algorithms.Meanwhile,we also explore the way of deep learning on the 3D image quality assessment.In our proposed 3D image quality assessment net,first,siamese net is employed to extract feature from the left view and right view of 3D image;second,the multiple perception machine is employed to select the important feature based the previous results;finally,the ridge regression with regularization is employed to training the whole net.Through the experiment,we prove that the deep learning has potential for 3D image quality assessment.