IEEE Trans Image Process. 2019 Aug;28(8):3946-3958. doi: 10.1109/TIP.2019.2902831. Epub 2019 Mar 5.
The goal of objective stereoscopic image quality assessment (SIQA) is to predict the human perceptual quality of stereoscopic/3D images automatically and accurately. Compared with traditional 2D image quality assessment, the quality assessment of stereoscopic images is more challenging because of complex binocular vision mechanisms and multiple quality dimensions. In this paper, inspired by the hierarchical dual-stream interactive nature of the human visual system, we propose a stereoscopic image quality assessment network (StereoQA-Net) for no-reference stereoscopic image quality assessment. The proposed StereoQA-Net is an end-to-end dual-stream interactive network containing left and right view sub-networks, where the interaction of the two sub-networks exists in multiple layers. We evaluate our method on the LIVE stereoscopic image quality databases. The experimental results show that our proposed StereoQA-Net outperforms state-of-the-art algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs of various distortion types. In a more general case, the proposed StereoQA-Net can effectively predict the perceptual quality of local regions. In addition, cross-dataset experiments also demonstrate the generalization ability of our algorithm.
客观立体图像质量评估（SIQA）的目标是自动、准确地预测立体/3D 图像的人眼感知质量。与传统的 2D 图像质量评估相比，由于复杂的双目视觉机制和多个质量维度，立体图像的质量评估更具挑战性。在本文中，受人类视觉系统的分层双流交互性质的启发，我们提出了一种用于无参考立体图像质量评估的立体图像质量评估网络（StereoQA-Net）。所提出的 StereoQA-Net 是一个端到端的双流交互网络，包含左右视图子网，其中两个子网的交互存在于多个层中。我们在 LIVE 立体图像质量数据库上评估了我们的方法。实验结果表明，我们提出的 StereoQA-Net 在各种失真类型的对称和非对称失真立体图像对上均优于最先进的算法。在更一般的情况下，所提出的 StereoQA-Net 可以有效地预测局部区域的感知质量。此外，跨数据集实验也证明了我们算法的泛化能力。
