This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integrate these characteristics to estimate the quality of stereoscopic images without reference through a convolutional neural network. Our method is based on two main tasks: the first task predicts naturalness analysis based features adapted to stereo images, while the second task predicts the quality of such images. The former, so-called auxiliary task, aims to find more robust and relevant features to improve the quality prediction. To do this, we compute naturalness-based features using a Natural Scene Statistics (NSS) model in the complex wavelet domain. It allows to capture the statistical dependency between pairs of the stereoscopic images. Experiments are conducted on the well known LIVE PHASE I and LIVE PHASE II databases. The results obtained show the relevance of our method when comparing with those of the state-of-the-art. Our code is available online on \url{https://github.com/Bourbia-Salima/multitask-cnn-nrsiqa_2021}.
翻译:本文用新的多任务深层学习方法来解决盲人立体图像质量评估问题。 在立体视觉领域,信息在左对右视角以及双筒现象之间公平分配。 在这项工作中,我们提议将这些特征整合在一起,通过共生神经网络来评估立体图像的质量,而无需参考。我们的方法基于两个主要任务:第一项任务预测根据立体图像调整的自然特性分析,而第二项任务则预测这些图像的质量。以前的所谓辅助任务旨在寻找更稳健和相关的特性来改进质量预测。要做到这一点,我们利用复杂的波列域的自然显形统计模型来计算自然特征。它能够捕捉立体神经图像对夫妇之间的统计依赖性。实验是在众所周知的LVE PHASE I和LIVE PHASE II数据库上进行的。在将我们的方法与现有的州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/我们代码进行比较时,获得的结果显示了我们方法的相关性。