基于双目融合的无参考立体图像质量评价

针对对称失真和非对称失真图像的评价问题,提出了一种基于双目融合的无参考立体图像质量评价方法。首先,分别将立体图像的左、右视点图像分解成拉普拉斯金字塔序列,利用图像平均梯度和区域能量确定各层融合系数,在双目加权模型的基础上逐层融合两序列并重构合成图像。然后,提取左、右视点图像、合成图像的多尺度多方向频域变换特征和对比度、熵、能量、逆差分矩特征。最后,将特征参数作为支持向量回归模型的输入进行训练并预测图像质量。在LIVE 3D phaseⅠ和LIVE 3D phaseⅡ图像库上作相关性分析,其Pearson线性相关系数和Spearman等级相关系数均分别达到0.96和0.95以上。结果表明,本文方法对立体图像质量的预测结果与主观评价值具有较高的一致性。

Non-reference stereo image quality evaluation based on binocular fusion

Aiming at the evaluation problem of symmetric distortion and asymmetric distortion image, a non-reference stereo image quality evaluation method based on binocular fusion is proposed. Firstly, the left and right viewpoint images of the stereo image are decomposed into Laplacian pyramid sequences respectively, and the fusion coefficients of each layer are determined by using the image ave- rage gradient and the region energy. On the basis of the binocular weighted model, the two sequences are merged layer by layer and the cyclopean image is reconstructed. Then, the multi-scale, multi-directional frequency domain transform features and the contrast, entropy, energy and inverse difference moment features of the left and right viewpoint images and the cyclopean images are extracted. Finally, feature parameters are trained as input to the support vector regression model and the image quality is predicted. The correlation analysis is performed under LIVE 3D phase I and LIVE 3D phase II image databases. The Pearson linear correlation coefficient and Spearman rank correlation coefficient reach 0.96 and 0.95 respectively. The results show that the prediction results of stereo image quality have higher consistency with subjective evaluation values.