基于自然场景统计的深度图像质量无参考评价方法

doi:10.11896/j.issn.1002-137X.2019.06.038

摘要/Abstract

摘要： 深度图在视角合成中起着很重要的作用,深度信息的错误易导致合成视角几何位置上的误差。由于很难获得完美的深度图,文中提出了一种基于自然场景统计的无参考型深度图质量评价方法。首先利用Canny算子检测出图像边缘并确定边缘失真区域,然后分别计算边缘失真区域的梯度幅值和高斯-拉普拉斯图像。无失真深度图的边缘失真区域的梯度幅值和高斯-拉普拉斯算子分别符合韦伯分布和非对称高斯分布;由于存在失真的深度图的这两个分布会发生不同程度的偏移,因此在5个尺度下提取这两个分布的共计30个参数构成了所提方法的特征。最后通过随机森林建立评价模型来评价深度图的质量。在公开数据库上进行的测试结果显示,所提方法与主观评价结果有着很好的一致性,而且其性能优于现有的图像质量评价方法。

关键词: 深度图, 视角合成, 随机森林, 无参考, 质量评价, 自然场景统计

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

中图分类号:

TP391

陈曦, 李雷达, 李巧月, 韩习习, 祝汉城. 基于自然场景统计的深度图像质量无参考评价方法[J]. 计算机科学, 2019, 46(6): 256-262. https://doi.org/10.11896/j.issn.1002-137X.2019.06.038

CHEN Xi, LI Lei-da, LI Qiao-yue, HAN Xi-xi, ZHU Han-cheng. No-reference Quality Assessment of Depth Images Based on Natural Scenes Statistics[J]. Computer Science, 2019, 46(6): 256-262. https://doi.org/10.11896/j.issn.1002-137X.2019.06.038

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