Computer Science ›› 2019, Vol. 46 ›› Issue (6): 256-262.doi: 10.11896/j.issn.1002-137X.2019.06.038

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No-reference Quality Assessment of Depth Images Based on Natural Scenes Statistics

CHEN Xi, LI Lei-da, LI Qiao-yue, HAN Xi-xi, ZHU Han-cheng   

  1. (School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China)
  • Received:2018-05-30 Published:2019-06-24

Abstract: Depth-image-based-rendering (DIBR) has been widely used in virtual view synthesis.The quality of depth maps is a crucial factor influencing the synthesis results,because the errors of depth information can easily lead to severe geometry distortions in virtual synthesis views,and it is difficult to obtain perfect depth maps.In this paper,an NSS-based no-reference quality assessment algorithm for depth maps was proposed.Firstly,the edges of the depth map are detected by the Canny operator,and the distorted edge region of the depth map is defined based on the detected edges.Secondly,Gradient Magnitude (GM) and Laplacian of Gaussian (LOG) of depth map in the distorted edge region are calculated.The GM distribution is fitted by Weibull function for distorted images as well as undistorted ones.The Asymmetric Generalized Gaussian Distribution (AGGD)is used to fit the LOG distributions for distorted images as well as undistorted ones.Images are naturally multiscale,and distortions affect image structure across scales.Hence,all features are extracted at five scales of the original image.Finally,Random Forests (RF) regression model is used to produce a quality index to assess the quality of the depth maps.Extensive experiments on benchmark databases demonstrate the effectiveness of the proposed method,and it outperforms the state-of-the-art methods.

Key words: Depth image, Natural scenes statistics, No-reference, Quality assessment, Random forests, View synthesis

CLC Number: 

  • TP391
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