In this paper, we introduce a completely blind and unsupervised no-reference model that performs the quality assessment of stereoscopic 3D videos by using joint motion and depth visibility. In order to achieve such objective metric, first we compute the correlation map between the motion vector map and the depth map, and then we study the natural scene statistics of the estimated correlation map. We empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD) and compute the UGGD parameters at multi-scale and multi-orient steerable subband decomposition. It is shown that the estimated UGGD features are capable of discriminating distortions. We then compute the mean vector and the covariance matrix from the estimated UGGD features of the correlation maps of motion vector and the depth maps of undistorted and distorted S3D videos. This is followed by the computation of the Wave Hedges distance between the mean vectors of the undistorted and distorted contents, and the Bhattacharyya distance is measured between the corresponding covariance matrices. We pool these distance measures to achieve the overall joint motion and depth quality of S3D videos. The efficiency of the proposed algorithm is evaluated on the well-known IRCCYN, LFOVIAS3DPh1 and LFOVIAS3DPh2 S3D video datasets. Our model demonstrates robust and consistent performance across all distortion types and shows competitive performance against other 2D and 3D image and video quality assessment algorithms. Furthermore, since the proposed algorithm is completely blind, it does not require any training and testing analysis on the content features and the subjective scores.

在本文中，我们介绍了一种完全盲目且无监督的无参考模型，该模型通过使用关节运动和深度可见性来执行立体 3D 视频的质量评估。为了达到这样的客观度量，首先我们计算运动向量图和深度图之间的相关图，然后我们研究估计相关图的自然场景统计。我们使用单变量广义高斯分布 (UGGD) 对这些统计数据进行经验建模，并在多尺度和多方向可控子带分解下计算 UGGD 参数。结果表明，估计的 UGGD 特征能够区分失真。然后，我们根据估计的运动向量相关图的 UGGD 特征和未失真和失真 S3D 视频的深度图计算平均向量和协方差矩阵。接下来是计算未失真和失真内容的平均向量之间的 Wave Hedges 距离，并测量相应协方差矩阵之间的 Bhattacharyya 距离。我们汇集这些距离度量以实现 S3D 视频的整体关节运动和深度质量。在著名的 IRCCYN、LFOVIAS3DPh1 和 LFOVIAS3DPh2 S3D 视频数据集上评估了所提出算法的效率。我们的模型展示了所有失真类型的稳健和一致的性能，并显示出与其他 2D 和 3D 图像和视频质量评估算法相比具有竞争力的性能。此外，