论文标题
无参考立体图像质量评估的端到端深度分数模型
End-to-end deep multi-score model for No-reference stereoscopic image quality assessment
论文作者
论文摘要
基于深度学习的质量指标最近在图像质量评估（IQA）方面取得了重大改进。在立体视觉的领域，信息均匀分布，左眼和右眼有轻微的差异。但是，由于不对称失真，左图和右图像的客观质量评级将有所不同，因此需要为每种视图学习独特的质量指标。与现有的立体IQA度量不同，主要侧重于估计全球人类分数，我们建议将左，右和立体客观分数纳入以提取每种视图的相应特性，从而估算立体镜面图像质量而无需参考。因此，我们使用深度得分卷积神经网络（CNN）。我们的模型已经接受了执行四个任务的培训：首先，预测左视图的质量。其次，预测左视图的质量。第三和第四，分别预测立体声观点和全球质量的质量，全球得分是最终的质量。实验是在滑铁卢IVC 3D阶段1和2阶段数据库上进行的。获得的结果表明，在与最先进的方法进行比较时，我们的方法的优势。可以在以下网址找到实现代码
Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multi-score-SIQA
