Computer Science ›› 2020, Vol. 47 ›› Issue (9): 150-156.doi: 10.11896/jsjkx.190700213

• Computer Graphics & Multimedia • Previous Articles     Next Articles

No-reference Stereo Image Quality Assessment Based on Disparity Information

ZHU Ling-ying, SANG Qing-bing, GU Ting-ting   

  1. School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2019-07-31 Published:2020-09-10
  • About author:ZHU Ling-ying,master student.Her main research interests include image quality assessment and so on.
    SANG Qing-bing,Ph.D,professor.His main research interests include image processing,quality assessment and machine learning.
  • Supported by:
    Natural Science Foundation of Jiangsu Province,China (BK20171142).

Abstract: In recent years,with the rapid development of deep learning in the field of image quality assessment (IQA),2D-IQA has been improved,but 3D-IQA still needs to be improved.Therefore,combining the three-branch convolutional neural network,the paper proposes a no-reference stereo image quality assessment based on disparity information and analyzes the influence of different disparity maps on the performance of the model.The algorithm takes the left/right view patches and the disparity map patches as input,automatically extracts features,and obtains the regression model through training to realize the prediction of the stereo images.In this paper,5 different stereo matching algorithms are used to generate disparity maps.The experimental results show that the SAD algorithm is the best.The experimental results on stereo image databases LIVE3D and MCL3D show that the method is not only suitable for evaluating symmetric distortion images,but also for evaluating asymmetric distortion stereo images.The overall distortion results of this method are superior to other comparison algorithms.Especially on the MCL3D image database,the evaluation method PLCC and SROCC of the proposed method are 1% and 4% higher than other methods.The Experimental data shows that the proposed model improves the performance of stereo image quality assessment,which is highly consistent with human subjective perception.

Key words: Asymmetric distortion image, Convolutional neural network, Disparity information, Stereo image quality assessment, Stereo matching algorithm

CLC Number: 

  • TP183
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