立体图像失真会影响图像边缘、结构和深度等低层次结构特征,为此,基于人眼对图像低层次结构的理解提出一种无参考立体图像质量评价方法。首先,对输入左右视图、合成图和视差图进行双树复小波变换;其次,提取左右视图、合成图和视差图小波子带的相位幅度特征,以及左右视图和合成图小波子带的梯度特征;最后,将所得特征输入支持向量回归(SVR)中训练,获得特征到质量分数的映射关系模型,预测立体图像质量。分别在LIVE3 DIQD Phase 1数据库和LIVE3 DIQD Phase 2数据库中测试本文算法性能,实验结果表明,本文算法与人眼视觉特性保持很高的一致性,且优于目前大多数主流算法。

The stereoscopic image distortion can affect the edge, structure, depth and other information of image. In this paper, we propose a no-reference stereoscopic image quality assessment metric based on the human eyes'comprehension of image's low-level structure. First, the left and right views, cyclopean map and disparity map are decomposed by the dual-tree complex wavelet transform. Second, the phase amplitude characteristics of the wavelet sub-band of the left and right views, cyclopean image and disparity map are extracted. Similarly the gradient features of the wavelet sub-band of the left and right views and cyclopean image are extracted. Finally, these features are feeded into the support vector regression to train the mapping model for predicting the quality score of tested stereoscopic image. The experimental results on LIVE3 DIQD Phase 1 and LIVE3 DIQD Phase 2 show that the proposed method is highly correlated with the human visual system, achieving excellent prediction performance.