No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.

图像的无参考质量评估受到了广泛关注。然而，由于其生物学基础薄弱，这种评估的准确性仍然值得怀疑。在本文中，我们提出了一种基于超像素指数和生物双目机制的新型质量评估模型。我们模型的技术贡献是引入了局部单眼超像素特征和三个全局双目视觉特征。我们利用单眼超像素分割来提取两种类型的熵作为局部视觉特征，以进行准确的质量感知特征提取。另外，从双目视觉信息中提取自然场景统计特征以补充局部单眼特征并量化立体图像的自然性。最后，学习回归模型以评估立体图像的质量。来自三个流行数据库的实验结果表明，在预测准确性和通用性方面，所提出的模型比早期模型具有更可靠的性能。