可控金字塔分解的立体图像质量评价方法

刘新会; 桑庆兵; 孙长滨

doi:doi:10.3969/j.issn.1007-5461.2017.06.005

量子电子学报, 2017, 34 (6): 672, 网络出版: 2017-12-08

可控金字塔分解的立体图像质量评价方法

Stereo image quality evaluation method based on steerable pyramid decomposition

论文大纲

刘新会 ^1,*桑庆兵 ¹孙长滨 ²

作者单位

¹ 江南大学物联网工程学院,轻工过程先进控制教育部重点实验室, 江苏无锡 214122

² 安徽省明光市第三中学, 安徽明光 239400

摘要

基于最小能量误差得到左右视图的视差图,分别对左、右视图和视差图进行4尺度、12个方向的可控金字塔分解,每一幅图像可得到1条高频子带和48条方向子带。对左、右视图分解后相对应的48对方向子带进行二元广义高斯分布拟合,提取其形状参数和尺度参数,并提取所有方向子带的跨尺度相关性、空间相关性等特征信息,将这些特征输入支持向量回归(SVR)训练预测得到立体图像质量评分。结果表明该质量评价模型在LIVE 3D数据库上的性能指标斯皮尔曼等级相关系数(SROCC)、线性皮尔逊相关系数(CC)均在0.93以上,与人类的主观评价具有较好的一致性。

Abstract

Based on the minimum energy error, the disparity image of the left and right views is obtained, and the steerable pyramid decomposition which has four scales and twelve orientations are carried on the left, right views and their disparity image. Each image can get one high frequency sub-band and fourty-eight directional sub-bands. The bivariate generalized Gaussian distribution fitting is carried on fourty-eight pairs of directional sub-bands corresponding to the decomposition of left and right views. Their shape and scale parameters are extracted, and across scales, spatial correlation statistical features from all orientation sub-bands are extracted. These features are input to support vector regression (SVR) to train and predict the stereo image quality score. Results show that the performance evaluation index of the quality evaluation model in LIVE 3D database, Spearman rank order correlation coefficient (SROCC), linear correlation coefficient (CC) are all above 0.93, which has good consistency with the subjective evaluation of human.

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刘新会, 桑庆兵, 孙长滨. 可控金字塔分解的立体图像质量评价方法[J]. 量子电子学报, 2017, 34(6): 672. LIU Xinhui, SANG Qingbing, SUN Changbin. Stereo image quality evaluation method based on steerable pyramid decomposition[J]. Chinese Journal of Quantum Electronics, 2017, 34(6): 672.

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