本文针对高斯模糊失真的立体图像, 提出了一种基于二维经验模式分解的无参考质量评价方法。该方法先通过二维经验模式分解将图像分解成内蕴模式函数分量和残差图像, 再对每个内蕴模式函数分量提取它的统计信息量(均值, 方差和信息熵), 并结合广义高斯分布方法提取每个内蕴模式函数分量的形状参数、尺度参数作为图像的特征信息, 然后利用支持向量回归模型对特征进行融合并预测得到立体图像质量的客观评价值。实验结果表明, 该模型与主观评价结果有较好的相关性, 符合人眼视觉系统。

For Gaussian blur distorted stereoscopic images, a no-reference image quality assessment method is proposed based on the Bidimensional Empirical Mode Decomposition (BEMD). In the method, intrinsic mode function components and residual image are firstly produced by applying BEMD. Then, the statistical information (mean, variance and entropy), and shape and scale parameters are obtained by applying generalized Gaussian distribution method on the intrinsic mode function components, to form stereoscopic image feature information. Finally, support vector regression is performed to predict the objective scores by establishing the relationship between the stereoscopic image features and the subjective scores. Experimental results show that, the proposed method can achieve higher consistency with subjective assessment of stereoscopic images for Gaussian blur distorted stereoscopic images.