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Perceived quality measurement of stereoscopic 3D images based on sparse representation and binocular combination

机译：基于稀疏表示和双目组合的立体3D图像的质量测量

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Measurement of the perceived quality of stereoscopic three-dimensional (S3D) images has attracted an increasing amount of research interest in recent years. This paper proposes a S3D image quality measurement (IQM) metric based on sparse representation and binocular combination. The proposed method involves learning binocular and monocular dictionaries from a training database such that the sparse features of binocular combination can be expressed by a linear combination of a few selected basis feature vectors. Following this, scores for the similarity of these sparse features between reference and distorted S3D images are measured. Based on the observation that sparse features are invariant against weak degradations, similarity scores of the features of the gradient magnitude of binocular combination are then computed and used as a complementary feature. Finally, by using kernel-based support vector regression (SVR), these similarity scores are integrated into an overall quality value. Experimental results on three public S3D-IQM datasets show that in comparison with the relevant existing metrics, the devised metric attains significantly high consistency alignment with subjective quality assessment. (C) 2019 Elsevier Inc. All rights reserved.
机译：近年来，立体三维（S3D）图像的感知质量的测量引起了越来越多的研究兴趣。本文提出了基于稀疏表示和双目组合的S3D图像质量测量（IQM）度量。所提出的方法涉及从训练数据库学习双目和单眼词典，使得双目组合的稀疏特征可以通过少数选定基础特征向量的线性组合来表示。在此之后，测量参考和失真S3D图像之间的这些稀疏特征的相似性的分数。基于观察到稀疏特征不变于弱劣化，然后计算双目组合梯度幅度特征的相似性分数，并用作互补特征。最后，通过使用基于内核的支持向量回归（SVR），这些相似度得分被集成到整体质量值中。三个公共S3D-IQM数据集的实验结果表明，与相关现有度量相比，设计的指标与主观质量评估相比显着高的一致性对齐。 （c）2019 Elsevier Inc.保留所有权利。

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